# Braving the Storm I: Quantifying Disk-wide Ionized Outflows in the Large
Magellanic Cloud with ULLYSES

Yong Zheng (郑永) Department of Physics, Applied Physics and Astronomy,
Rensselaer Polytechnic Institute, Troy, NY 12180<EMAIL_ADDRESS>Kirill
Tchernyshyov Department of Astronomy, University of Washington, Seattle, WA
98195, USA  Knut Olsen National Optical Astronomy Observatory, Tucson, AZ
85719, USA  Yumi Choi National Optical Astronomy Observatory, Tucson, AZ
85719, USA  Chad Bustard Kavli Institute for Theoretical Physics, University
of California—Santa Barbara, Kohn Hall, Santa Barbara, CA 93107, USA  Julia
Roman-Duval Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Robert Zhu  Department of Astronomy, University of
California, Berkeley, CA 94720, USA  Enrico M. Di Teodoro Dipartimento di
Fisica e Astronomia, Università degli Studi di Firenze, I-50019 Sesto
Fiorentino, Italy  Jessica Werk Department of Astronomy, University of
Washington, Seattle, WA 98195, USA  Mary Putman Department of Astronomy,
Columbia University, New York, NY 10027, USA  Anna F. McLeod Centre for
Extragalactic Astronomy, Department of Physics, Durham University, South Road,
Durham DH1 3LE, UK  Institute for Computational Cosmology, Department of
Physics, University of Durham, South Road, Durham DH1 3LE, UK  Yakov Faerman
Department of Astronomy, University of Washington, Seattle, WA 98195, USA
Raymond C. Simons Department of Physics, University of Connecticut, 196A
Auditorium Road Unit 3046, Storrs, CT 06269 USA  Joshua Peek Space Telescope
Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA

###### Abstract

The Large Magellanic Cloud (LMC) is home to many H ii regions, which may lead
to significant outflows. We examine the LMC’s multiphase gas ($T\sim 10^{4-5}$
K) in H i, S ii, Si iv, and C iv using 110 stellar sight lines observed with
the HST’s Ultraviolet Legacy Library of Young Stars as Essential Standards
(ULLYSES) program. We develop a continuum fitting algorithm based on the
concept of Gaussian Process regression and identify reliable LMC interstellar
absorption lines over $v_{\rm helio}=175-375$ km s-1. Our analyses show disk-
wide, warm, ionized outflows in Si iv and C iv across the LMC with bulk
velocities of $|v_{\rm out,bulk}|\sim 20-60$ km s-1, which indicates that most
of the outflowing mass is gravitationally bound. The outflows’ column
densities correlate with the LMC’s star formation rate surface densities
($\Sigma_{\rm SFR}$), and the outflows in star-forming regions with higher
$\Sigma_{\rm SFR}$ tend to be more ionized. Considering the ionized outflows
from both sides of the LMC as traced by C iv, we conservatively estimate a
total outflow rate of $\dot{M}_{\rm out}\gtrsim 0.03~{}M_{\odot}{\rm yr}^{-1}$
and a mass loading factor of $\eta\gtrsim 0.2$. We compare the LMC’s outflows
with those detected in starburst galaxies, and find a universal scaling
relation of $|v_{\rm out,bulk}|\propto\Sigma_{\rm SFR}^{0.22}$ over a range of
star-forming conditions ($\Sigma_{\rm SFR}\sim 10^{-2}-10^{2}~{}M_{\odot}{\rm
yr}^{-1}{\rm kpc}^{-2}$). Lastly, we find that the outflows are co-rotating
with the LMC’s young stellar disk and the velocity field does not seem to be
significantly impacted by external forces; we thus speculate on the existence
of a bow shock leading the LMC, which may have shielded the outflows from ram
pressure as the LMC orbits the Milky Way.

Large Magellanic Cloud(903); Stellar feedback(1602); Metal line
absorbers(1032); Galaxy Evolution (594); Interstellar Medium (847)

††facilities: HST (COS, STIS), Mikulski Archive for Space Telescopes
(MAST)††software: Astropy (The Astropy Collaboration et al., 2018), Numpy
(Harris et al., 2020), George (Ambikasaran et al., 2015), Matplotlib (Hunter,
2007)

##  1 Introduction

Stellar feedback is a multiscale process. It requires a detailed understanding
of small-scale star-forming regions, large-scale structures such as the
interstellar medium (ISM) and the circumgalactic medium (CGM), as well as the
delicate interplay among these gaseous structures (McKee & Ostriker, 1977).
Feedback-driven outflows enrich the CGM with metals, momentum, and energy;
and, theoretical studies have found that the presence of stellar feedback is
key to producing a realistic galaxy and a gaseous CGM with multiphase
properties consistent with observations (e.g. Vogelsberger et al., 2014;
Hopkins et al., 2014; Schaye et al., 2015; Peeples et al., 2019).

Figure 1: A schematic illustration of the present-day LMC with respect to the
Milky Way disk. The locations, orientations, and sizes of the LMC (star-
forming) disk and a potential bow shock are based on a hydrodynamic simulation
of the LMC orbiting in the MW halo (Setton et al., 2023, see their Figure 4);
the simulation assumes the LMC to be on its first infall (Besla et al., 2007).
The location of the Small Magellanic Cloud (SMC) is indicated as a cross. We
highlight in blue arrows the LMC’s outflows on its near side, which we study
in this work using 110 sight lines from the ULLYSES DR5. We also show the
location of a foreground high-velocity cloud at $d_{\odot}<13.3$ kpc (Richter
et al., 2015; Werner & Rauch, 2015), which contaminates potential LMC outflow
absorption over $v_{\rm helio}\sim 90-175~{}\rm km~{}s^{-1}$. Our work focuses
on the LMC gas at $v_{\rm helio}=175-375~{}\rm km~{}s^{-1}$ to minimize
foreground contamination.

Galactic outflows have been ubiquitously observed in star-forming galaxies
(Veilleux et al., 2020). For example, the Na i 5890/5896 Å doublet probes
dusty outflows with velocities up to $\sim 1000~{}\rm km~{}s^{-1}$ in infrared
(ultra)luminous starburst galaxies (e.g. Heckman et al., 2000; Martin, 2005;
Rupke et al., 2005; Chen et al., 2010). These outflows tend to be neutral;
otherwise Na i would not exist with its ionization potential at 5.1 eV. Rubin
et al. (2014) studied cool outflows ($T\sim 10^{4}$ K) in star-forming
galaxies at $0.3<z<1.4$ using Mg ii and Fe ii doublets and found an outflow
detection rate of $\sim 66\%$ (see also Weiner et al. 2009; Erb et al. 2012;
Davis et al. 2023). Warmer ionized outflows ($T\sim 10^{4-5.5}$ K) in
starburst galaxies can be traced with numerous absorbers in the ultraviolet
(UV) such as Si ii, Si iii, Si iv, C iv, and O vi with velocities up to a few
hundreds of $\rm km~{}s^{-1}$ (e.g. Heckman et al., 2015; Chisholm et al.,
2015; Xu et al., 2022). Though with different tracers, a common finding among
the above studies is that the velocities of outflows, regardless of their
phases, correlate significantly with the host galaxies’ star formation
activities, stellar masses, and circular velocities.

For starburst galaxies, the bulk velocities of outflows correlate with the
galaxies’ star formation rates (SFRs) as a power-law, $v_{\rm
out,bulk}\propto{\rm SFR}^{\alpha}$, with $\alpha\sim 0.2-0.35$ (e.g. Martin,
2005; Chisholm et al., 2015; Xu et al., 2022). The power-law index $\alpha$ is
shallower when considering the correlation between $v_{\rm out,bulk}$ and SFR
per surface area $\Sigma_{\rm SFR}$ ($\alpha\sim 0.1-0.2$; e.g. Chen et al.
2010; Xu et al. 2022). Reichardt Chu et al. (2022) argue that these power-law
indexes are indicative of energy-driven outflows, where the energy is mostly
conserved as outflows break out of the ISM and propagate into the CGM (see
also Chen et al., 2010; Li et al., 2017; Kim et al., 2020).

Observationally, outflows have been measured largely based on stacking spectra
from galaxies with similar physical properties to maximize spectral signal-to-
noise ratios (e.g. Chen et al., 2010), or collecting a sufficient sample with
one sight line per galaxy to cover a wide parameter space (e.g. Xu et al.,
2022). While these approaches provide invaluable information on outflows over
$\sim$kpc scales, it remains unclear how outflows interact with their ambient
environments on smaller scales. From theoretical perspectives, how outflows
are generated and propagated in realistic environments such as the Solar
neighborhood and varying star formation conditions have been an active area of
research (e.g. Li et al., 2017; Kim & Ostriker, 2018; Kim et al., 2020;
Andersson et al., 2023; Tan & Fielding, 2023); however, these simulations
remain largely unconstrained because of the scarcity of observational details
on sub-kpc scales.

In this work, we examine how varying star-forming conditions impact the
physical properties of ionized outflows in the Large Magellanic Cloud (LMC).
The LMC is the closest galaxy that hosts many bright H ii regions; it is an
ideal galaxy to study how star formation drives outflows. Active star-forming
regions are found across the LMC, such as 30 Doradus (30 Dor), N11, N44, N55,
and N206 (Ambrocio-Cruz et al., 2016; McLeod et al., 2019). Table 1 lists the
key physical parameters of the LMC, and Figure 1 illustrates the location and
movement of the present-day LMC with respect to the Milky Way (MW) disk. At a
distance of 50.1 kpc (Freedman et al., 2001) and moving in the MW halo at a
Galactocentric velocity of $321~{}\rm km~{}s^{-1}$ (Kallivayalil et al.,
2013), the LMC experiences strong headwinds due to ram pressure, which results
in a truncated H i disk (Salem et al., 2015) and a potential bow shock leading
the LMC (Setton et al., 2023).

Table 1: Key Physical Parameters of the LMC Parameter | Value | Reference(s)
---|---|---
$d$ | 50.1 | Freedman et al.
(distance) | (kpc) |  (2001)
$v_{\rm LMC,LSR}$ | $264.0\pm 0.4$ | Choi et al.
(systemic velocity) |  ($\rm km~{}s^{-1}$) |  (2022)
$v_{\rm LMC,G}$ | $321\pm 24$ | Kallivayalil et al.
(Galactocentric velocity) |  ($\rm km~{}s^{-1}$) |  (2013)
$i$ | $23.4\pm 0.5$ | Choi et al.
(inclination) | (deg) |  (2022)
$M_{*}$ | $3\times 10^{9}$ | van der Marel et al.
(stellar mass) |  ($M_{\odot}$) |  (2002)
$M_{\rm HI}$ | $4.4\times 10^{8}$ | Brüns et al.
(H i gas mass) |  ($M_{\odot}$) |  (2005)
$Z$ | 0.5 | Russell & Dopita
(metallicity) |  ($Z_{\odot}$) |  (1992)
SFR | $\sim 0.2$ | Harris & Zaritsky
(present-day) |  ($M_{\odot}~{}{\rm yr}^{-1}$) |  (2009)
$v_{\rm rot,*}$ | $77.5\pm 1.3$ | Choi et al.
(stellar rotation[1]) |  ($\rm km~{}s^{-1}$) |  (2022)
$v_{\rm rot,HI}$ | $\sim 70$ | Kim et al.
(H i rotation) |  ($\rm km~{}s^{-1}$) |  (1998)
R.A.[2] (J2000) | $80.443$ | Choi et al.
(LMC center) | (deg) |  (2022)
Decl.[2] (J2000) | $-69.272$ | Choi et al.
(LMC center) | (deg) |  (2022)

Gas inflows and outflows have been detected using down-the-barrel observations
toward individual massive stars bright in the UV in nearby galaxies (e.g. Howk
et al., 2002; Danforth et al., 2002; Lehner & Howk, 2007; Zheng et al., 2017).
For the LMC, Wakker et al. (1998) detected C iv absorption with velocities
offset from the galaxy’s H$\alpha$ emission using five stars observed with the
Goddard High-Resolution Spectrograph on the Hubble Space Telescope (HST),
which they interpreted as evidence for a hot halo around the LMC. Barger et
al. (2016) compared ion absorption toward a pair of an LMC star and a
background QSO that are $\sim 100$ pc in projected separation; while the star
only probes outflows in front of the LMC, the QSO sight line shows nearly
symmetrical ion absorption from the outflows from both sides of the galaxy.

Thanks to the HST’s Ultraviolet Legacy Library of Young Stars as Essential
Standards (ULLYSES111 https://ullyses.stsci.edu/) program (Roman-Duval et al.,
2020), we are now able to probe outflows on sub-kpc scales in the LMC using a
large sample of over a hundred stellar sight lines (see Figure 2). This
manuscript is the first in a series in which we investigate how the interplay
between ram pressure and stellar feedback affects the kinematics and
ionization structures of outflows and inflows in the LMC (#HST-AR-16640, PI
Zheng222https://www.stsci.edu/cgi-bin/get-proposal-
info?id=16640&observatory=HST).

Figure 2: Distribution of 110 stellar sight lines (black circles) from the
ULLYSES DR5 across the LMC. The left panel shows an H i column density map
integrated from 190 to 386 $\rm km~{}s^{-1}$ (Kim et al., 2003) and the right
panel shows the LMC’s H$\alpha$ intensity (Gaustad et al., 2001). Red and
white crosses in the left and right panels indicate several major H ii regions
in the LMC, respectively. Information regarding the ULLYSES dataset can be
found in Section 2, and the H i and H$\alpha$ datasets are introduced in
Section 3.  Figure 3: Distribution of the ULLYSES sight lines against the
H$\alpha$ intensity map in an orthographic projection in the LMC plane,
following the method outlined in Choi et al. 2022. At the distance of the LMC
(50.1 kpc; Freedman et al. 2001), 1 deg $\approx$ 0.9 kpc. Major H ii regions
are indicated by white crosses. Filled blue circles indicate those ULLYSES
sight lines with reliable S ii (91/110; left), Si iv (44/110; middle), and C
iv (71/109; right) measurements over the LMC’s absorption range of $v_{\rm
helio}=175-375~{}\rm km~{}s^{-1}$. An ion measurement is considered reliable
if: (1) its stellar continuum shows a strong P-Cygni profile such that the
LMC’s interstellar absorption can be reliably identified, and (2) the column
density difference between doublet lines is within what is allowed by the
apparent optical depth method (Savage & Sembach, 1991, 1996). See Section 2
for further details.

This paper is organized as follows. In Section 2, we describe the ULLYSES LMC
dataset and relevant spectral analysis procedures. In Section 3, we introduce
auxiliary datasets in H$\alpha$, H i, and a sample of the LMC’s young
supergiant stars to trace the LMC’s recent star formation, and neutral and
stellar disk kinematics. We show the main results in Section 4, and compare
the LMC’s outflows with those detected in starburst galaxies in Section 5. We
discuss the implications of our work in Section 6 and conclude in Section 7.

Throughout this paper, the velocity is given in a heliocentric frame, unless
otherwise specified. Toward the direction of the LMC, the heliocentric
velocity $v_{\rm helio}$ and a velocity defined in the Local Standard of Rest
(LSR) is generally offset by $v_{\rm helio}-v_{\rm LSR}\sim 10~{}\rm
km~{}s^{-1}$. We note that outflow velocities are typically measured in two
ways in the literature: centroid velocities tracing bulk outflow mass (e.g.,
Heckman et al. 2015), or maximum velocities tracing the terminal velocities of
low-density outflowing gas (e.g., Chisholm et al. 2015). In this work, we
adopt the first definition to describe the kinematic properties of bulk
outflows in the LMC, $v_{\rm out,bulk}$, unless otherwise specified.

##  2 Data Sample: UV Absorption

###  2.1 ULLYSES DR5 Sample Information

We use far UV spectra of 110 massive stars in the LMC that were made public in
the ULLYSES’s fifth data release (DR5, 2022 June 28). Figure 2 shows the
distribution of the ULLYSES DR5 stellar sight lines in the LMC against
background images of H i 21cm (Kim et al., 2003) and H$\alpha$ maps (Gaustad
et al., 2001).

We are interested in those ULLYSES targets that were observed with the G130M
and G160M gratings of the Cosmic Origins Spectrograph (COS), and/or the E140M
grating of the Space Telescope Imaging Spectrograph (STIS). The COS data have
spectral resolutions of R=12,000–16,000 ($\delta v\approx$19–25 $\rm
km~{}s^{-1}$) in G130M and R=13,000–20,000 in G160M ($\delta v\approx$15–23
$\rm km~{}s^{-1}$; COS Instrument Handbook, Soderblom 2023), and the STIS
E140M data have a spectral resolution of R=45,800 ($\delta v\approx 6.6~{}\rm
km~{}s^{-1}$; STIS Instrument Handbook, Medallon & Welty 2023). While the STIS
data have a higher spectral resolution, the COS data offer a better
sensitivity; this essentially means that STIS was used to observe bright stars
while COS was targeted at fainter stars.

We use the coadded spectra released by the ULLYSES program and refer the
reader to Roman-Duval et al. (2020) and the ULLYSES’s data release
page${}^{\ref{footnote1}}$ for more information. When available, we prioritize
data taken with the STIS/E140M grating for the higher spectral resolution. We
do not consider data taken with other gratings such as STIS/E140H or
COS/G140L, which are less common among the ULLYSES targets; the only exception
to this is the S ii and Si iv measurements toward star SK-67D83, which has
both observations from COS/G130M and STIS/E140H, we use the STIS/E140H
spectrum for its higher resolution (R=114,000 or $\delta v\approx 2.6~{}\rm
km~{}s^{-1}$). By design, both the STIS and COS data from the ULLYSES program
yield a continuum signal-to-noise ratio of 20–30 per resolution element.

###  2.2 Far UV Line Choices: S ii, Si iv, and C iv lines

There are a number of far UV ions that are typically used to study gas flows
in nearby galaxies, such as Si ii, Si iii, Si iv, C iv, and O vi (e.g., Wakker
et al., 1998; Howk et al., 2002; Lehner & Howk, 2007; Chisholm et al., 2015,
2016; Barger et al., 2016; Zheng et al., 2017). We focus on the Si iv
1393/1402 Å and C iv 1548/1550 Å doublets in the LMC. We choose Si iv and C iv
because they are relatively less saturated than Si ii and Si iii in the LMC,
and thus provide a more accurate characterization of the gas kinematics. Si iv
and C iv trace a cool-warm ionized phase with $T\sim 10^{4-5}$ K, which is
found to contain most of the mass in an outflow in hydrodynamic simulations of
feedback-driven outflows (e.g. Li et al., 2017; Kim et al., 2020; Rathjen et
al., 2021).

Although a large fraction of the ULLYSES sight lines also have O vi 1031/1037
Å spectra from the Far Ultraviolet Spectroscopic Explorer (FUSE), we do not
use O vi in this work because the O vi 1037 Å line is in a region with
multiple contaminants, and the O vi 1031 Å is complicated due to the broad
interstellar O vi absorption blended with stellar wind features with unknown
continuum shapes. The O vi 1031 Å line is also contaminated by H2 absorption
(see Howk et al., 2002).

The Si iv and C iv doublets are in spectral regions with no other
contaminating ISM lines. But, one of the main challenges in analyzing Si iv
and C iv is that the widths of the stellar pseudo-continuum features are, for
some stars in the sample, comparable to the widths of the interstellar
absorption lines along the LMC’s lines of sight. In the next section, we
develop an evaluation matrix to select stars with smooth continua that allow
accurate imputation over the almost $500~{}\rm km~{}s^{-1}$ range spanned by
interstellar absorption from the MW, intervening halo gas, and the LMC.

We also analyze S ii 1250/1253 Å lines that trace a less ionized phase of the
LMC’s ISM. We do not use the S ii 1259 Å line because it is blended with Si ii
1260 Å from the MW’s ISM. In Figure 3, we show in filled blue circles the
ULLYSES sight lines with reliable S ii (91/110), Si iv (44/110), and C iv
(71/109333There are only 109 stars with C iv coverage; star SK-66D17 was only
observed with COS/G130M in the ULLYSES DR5.) measurements for the LMC’s
interstellar absorption; we describe how we determine reliable ion
measurements in the following sections. The atomic data, including accurate
wavelengths and oscillator strengths, are adopted from Morton (2003).

Figure 4: Left: Example spectra with well-developed P-Cygni profiles in Si iv
(see Section 2.3), with the fluxes in black curves and errors in gray. The MW
and LMC Si iv absorption can be reliably separated from the stellar P-Cygni
profiles. We show in red curves the best-fit continuum models, which we
discuss in Section 2.4. Right: Example spectra that have either mild or no
stellar winds, in which case the Si iv absorption from stellar photospheres
blend heavily with the MW and LMC interstellar absorption; we consider these
Si iv unreliable. In each panel, the blue and red vertical shades indicate the
stellar continuum regions that we use to calculate the wind ratios $f_{w}$
(Equation 1) to quantify the strengths of the stellar winds/P-Cygni profiles.

###  2.3 Selection of Stars with Well-Developed P-Cygni Profiles in Si iv or C
iv

Among the 110 LMC stars in the ULLYSES DR5, there are 57 O-type stars, 28
B-type stars, 15 Wolf-Rayet stars, and 10 with other types such as binaries
and luminous blue variables444Three ULLYSES stars are labelled either as an O
type or a Wolf-Rayet star, including LMCe055-1 (WN4/O4) and SK-67D22
(O2If*/WN5), and VFTS 482 (O2.5 If*/WN6); for a classification purpose, we
group them under the Wolf-Rayet category. And, two binaries, HD38029 (WC4+OB)
and SK-69D246 (WN5/6h+WN6/7h), are also grouped under the Wolf-Rayet category,
in which one or both of the stars are Wolf-Rayet type. We note that the
category classification does not affect the results shown in this work.. The
key to extracting reliable Si iv and C iv interstellar absorption lines is to
identify OB stellar spectra with well-developed P-Cygni profiles (Savage & de
Boer, 1981; Howk et al., 2002). The P-Cygni profiles often take the form of
redshifted emission peaks with optically thick, blueshifted absorption troughs
due to absorption and subsequent re-emission of photons in stellar winds
(Kudritzki & Puls, 2000).

We only consider the ULLYSES stars that meet the following criteria: (1) the
stars have developed winds/P-Cygni profiles in Si iv or C iv, (2) the winds
have high optical depths such that the fluxes of the absorption troughs are
low or approaching zero, and (3) the winds have high terminal velocities such
that the blue edges of the troughs are far from line centers.

Criterion 1 excludes stars with stellar absorption that may have similar width
to the MW and LMC interstellar absorption. Criterion 2 excludes stars with
complicated spectra shapes, because it is difficult to correctly impute the
pseudo-continuum over the interstellar absorption regions for P-Cygni profiles
with low optical depths. Lastly, we implement criterion 3, excluding winds
with low terminal velocities, because a large fraction of a narrow P-Cygni
profile will contain interstellar absorption.

In the left panels of Figure 4, we show three examples of Si iv line profiles
that meet criteria 1–3. In the right panels, we show another three examples
where the stars have either no recognizable Si iv winds (top right), low
opacity winds (middle right), or low terminal-velocity winds that cause sharp
rising profiles (bottom right). We note that, the Si iv and C iv spectra of
the 15 Wolf-Rayet stars are generally dominated by broad stellar features that
can be easily distinguished from interstellar absorption, so we use these
Wolf-Rayet stars without considering criteria 1–3.

To apply criteria 1–3 quantitatively, we develop an automated algorithm based
on a general observation that stronger stellar winds with high opacities
result in deeper blueshifted absorption troughs where the fluxes approach zero
(e.g. Hawcroft et al., 2023). We define a wind ratio parameter, $f_{w}$, which
is the depth of a blue absorption trough with respect to the height of a red
emission peak. In practice, $f_{w}$ is computed as the ratio of the median
flux over $v_{\rm helio}=[-400,-100]~{}\rm km~{}s^{-1}$ in the rest frame of
the bluer line (Si iv 1393 or C iv 1548) to the median flux over $v_{\rm
helio}=[475,775]~{}\rm km~{}s^{-1}$ in the rest frame of the redder line (Si
iv 1402 or C iv 1550). In Figure 4, we highlight these two flux regions in
blue and red vertical shades for each target, respectively.

The reasoning for the $f_{w}$ parameterization is as follows. We do not use
the ratio of the minimum absorption to the maximum emission fluxes because
this is only applicable to spectra that have developed P-Cygni profiles. For
those without obvious P-Cygni profiles (e.g., as shown in the top right panel
of Figure 4), the locations of the minimum and maximum fluxes are subject to
local spectral variations, and in many cases the MW/LMC absorption lines are
the strongest absorption features. Secondly, stellar winds with high terminal
velocities will have a single blended Si iv or C iv profile instead of two
distinct peaks and troughs; this is best seen in star SK-69D50 (bottom left in
Figure 4) where the red peak of Si iv 1393 is absorbed by the blue trough of
Si iv 1402\. Lastly, we measure a median absorption flux over $[-400,-100]$
$\rm km~{}s^{-1}$ blueward of the bluer lines to avoid the MW’s ISM absorption
near $\sim 0~{}\rm km~{}s^{-1}$; similarly, we measure a median emission flux
over $[475,775]~{}\rm km~{}s^{-1}$ redward of the redder lines to avoid the
LMC’s interstellar absorption. The $300~{}\rm km~{}s^{-1}$ velocity interval
reduces the impact of noise, local spectral variations, and differences in
wind terminal velocities.

We examine a set of Si iv and C iv P-Cygni profiles of OB stars from the
Potsdam Wolf-Rayet (PoWR) models (Hainich et al., 2019) at the LMC’s
metallicity and determine that a threshold at

$f_{w}=\frac{\langle I_{\rm blue}\rangle}{\langle I_{\rm red}\rangle}\leq 0.6$
(1)

can best provide an adequate diagnostic to select stars with well-developed
winds. Varying the $f_{w}$ threshold by $\pm 0.1$ does not significantly
change the number of stars passing the threshold ($<6$ for C iv, and $<9$ for
Si iv). For C iv, Equation 1 is analogous to the “good” or “best” quality
scores set by Hawcroft et al. (2023) when estimating C iv terminal velocities
for 67 OB stars in the ULLYSES LMC dataset, where the minimum flux of the blue
absorption trough is roughly less than half of the continuum average. In
Section 2.5, we will use Equation 1 to select stars with reliable interstellar
absorption in Si iv and C iv.

###  2.4 Continuum Fitting, Ion Column Densities, and Centroid Velocities

We describe our continuum fitting algorithm using the Si iv doublet in Figure
4 as an example, and note that the same procedure is applied to S ii and C iv
as well. For Si iv and C iv, the continuum fitting is performed for every star
independent of their spectral type or wind ratio $f_{w}$ (Equation 1).

For each ion doublet, we select a spectral region that covers $\sim$5–10Å
blueward of the bluer line and $\sim$5–10Å redward of the redder line555The
exact width of the spectral region does not matter as long as it covers the
doublet interstellar absorption and provides enough stellar continuum as a
training set for Gaussian Process.. We mask an interstellar velocity
range666Because COS’s line-spread-function (LSF) is broader and less well-
defined than STIS’s, interstellar absorption lines in COS appear to be
$\sim$30–50 $\rm km~{}s^{-1}$ broader than those in STIS. For this reason, the
interstellar velocity mask for each line is determined by visual inspection,
and the velocity mask chosen for a COS absorption line is generally $\sim
50~{}\rm km~{}s^{-1}$ wider than that of STIS. of $v_{\rm
helio}\sim[-100,380]~{}\rm km~{}s^{-1}$ at the rest frame of each line to
cover both the MW and LMC interstellar absorption. To predict stellar
continuum over the masked velocity region, a typical approach is to fit low-
order Legendre polynomials to absorption-free regions near the lines of
interest (e.g. Howk et al., 2002; Lehner & Howk, 2007; Lehner et al., 2009;
Barger et al., 2016; Zheng et al., 2017). However, as shown in Figure 4,
stellar continuum is highly variable from star to star and from line to line.
To automate the fitting process and reduce human biases in the continuum
placement, we develop a continuum-fitting algorithm that is based on the
concept of Gaussian Process regression (Rasmussen & Williams, 2006) and the
open-source package George777https://george.readthedocs.io/en/latest/
(Ambikasaran et al., 2015).

Figure 5: Normalized S ii, Si iv, and C iv spectra for SK-67D104 (left) and
SK-71D41 (right). We highlight three velocity components: (1) the MW’s ISM and
intermediate velocity cloud at $|v|\lesssim 90~{}\rm km~{}s^{-1}$ (gray), (2)
a high-velocity cloud at $90\lesssim v\lesssim 175~{}\rm km~{}s^{-1}$ and
$d_{\odot}<13.3$ kpc in the foreground (HVC90-175; orange), and (3) the LMC’s
ISM and outflows at $175\lesssim v\lesssim 375~{}\rm km~{}s^{-1}$ (blue). The
relative spatial locations of the three components are sketched in Figure 1.
The solid vertical lines show the velocities of the LMC’s stellar disk at the
locations of the stars (see Section 3). The absorption from HVC90-175 and the
LMC (ISM+outflows) can be well separated near $v=175~{}\rm km~{}s^{-1}$ along
most sight lines (82/91 for S ii, 34/44 for Si iv, and 49/71 for C iv). See
Section 2.4 for details.

Instead of assuming a particular function form (e.g., polynomials), Gaussian
Process is a non-parametric process that models the probabilistic distribution
of all available model functions. We refer to the unmasked part of the Si iv
stellar continuum (free of interstellar absorption) as the training set $X$
and the masked velocity region where we want to predict stellar continuum
shape as the test set $Y$. The first step of Gaussian Process is to estimate
the probability distribution function of the training set,
$\mathcal{N}(\mu_{X},\Sigma_{X})$, where $\mu_{X}$ is the sample mean and
$\Sigma_{X}$ is a covariance matrix that describes the correlation of every
spectral point $x_{i}$ with itself and every other spectral point in $X$. We
estimate the covariance matrix $\Sigma_{X}$ by applying a kernel function to
model the training set $X$ that takes the form of either an exponential-
squared kernel or a Matern 3/2 kernel. We then use the scipy.minimize function
to fit for the maximum likelihood parameters for the kernel function and use
that to predict the stellar continuum shape over the test set $Y$ (i.e., the
masked velocity regions).

In the left panels of Figure 4 where three examples of reliable Si iv
interstellar absorption are shown, we plot the best-fit continua as red curves
with $1\sigma$ uncertainties. We divide the fluxes by the best-fit continua to
normalize each doublet lines. The uncertainties of the normalized fluxes have
combined the original flux errors and the continuum fitting uncertainties
through error propagation.

We calculate ion column densities based on the apparent optical depth method
(AOD; Savage & Sembach 1991, 1996). The AOD method can also be used to test
whether a stellar continuum is placed correctly, which we describe as follows.
For an ion line with normalized fluxes of $I_{n}(v)$888For some STIS spectra
from the ULLYSES DR5, when a line is saturated, the fluxes near the line
center appear to be lower than the errors at the same velocities. In this
case, we replace those fluxes with the corresponding error values in the AOD
calculation., its apparent column density as a function of velocity is,

$\begin{split}N_{a}(v)&=3.768\times
10^{14}\frac{\tau_{a}(v)}{f\lambda({\rm\AA})}~{}[{\rm cm^{-2}~{}(\rm
km~{}s^{-1}})^{-1}]\\\ N_{a}&=\int_{\rm vmin}^{\rm
vmax}\tau_{a}(v)dv~{},\end{split}$ (2)

where $f$ is the oscillator strength, $\lambda$ the rest wavelength in unit of
Å, and $\tau_{a}(v)$ the apparent optical depth smeared by an instrumental
broadening profile.

We adopt a fixed velocity range of $[v_{\rm min},v_{\rm max}]=[175,375]~{}\rm
km~{}s^{-1}$ to measure the integrated ion column densities of the LMC. We set
$v_{\rm min}=175~{}\rm km~{}s^{-1}$ to avoid contamination from a foreground
high-velocity cloud (HVC) at $v\sim 90-175~{}\rm km~{}s^{-1}$ within 13.3 kpc
from the Sun (see Figure 1; Lehner et al., 2009; Richter et al., 2015; Werner
& Rauch, 2015; Roman-Duval et al., 2019a). Figure 5 shows two examples where
in one case (left panel) the absorption from the HVC and the LMC can be
clearly distinguished near $v=175~{}\rm km~{}s^{-1}$, while in the other case
(right panel) the two structures blend mildly together near $v=175~{}\rm
km~{}s^{-1}$. We have checked all reliable S ii, Si iv, and C iv normalized
lines and confirmed that along most sight lines (82/91 for S ii, 34/44 for Si
iv, and 49/71 for C iv) the HVC and LMC absorption can be well separated999We
quantify whether the HVC and the LMC absorption are well separated by
measuring the mean absorption flux of an ion line (S ii 1253, Si iv 1393, C iv
1402) over a velocity range of $v=175\pm 5~{}\rm km~{}s^{-1}$. If the mean
flux is more than 80% of the continuum flux, such as the case in the left
panels of Figure 5, we consider the line to have well separated HVC and LMC
absorption.  at $\sim 175~{}\rm km~{}s^{-1}$ – the spectra shown in the left
panels are more common. Thus, setting $v_{\rm min}$ at $175~{}\rm km~{}s^{-1}$
minimizes contamination from the foreground HVC. The right bound, $v_{\rm
max}=375~{}\rm km~{}s^{-1}$, is chosen such that the integration range is wide
enough to cover the entire LMC absorption.

The S ii, Si iv, and C iv doublets all have a doublet ratio of
$f_{1}\lambda_{1}/f_{2}\lambda_{2}=2$, where the subscript 1 is for the
stronger line and 2 for the weaker line. This means the integrated column
density ratio of an ion doublet is:

$\frac{N_{\rm 2,a}}{N_{\rm 1,a}}=\frac{[\int\tau_{\rm
2,a}(v)dv]/f_{2}\lambda_{2}}{[\int\tau_{\rm
1,a}(v)dv]/f_{1}\lambda_{1}}=\frac{\int\tau_{\rm 2,a}(v)dv}{\int\tau_{\rm
1,a}(v)dv}\times 2~{}~{}.$ (3)

In cases where both lines of a doublet are fully resolved without saturation,
we expect $N_{\rm 1,a}=N_{\rm 2,a}$ and the column density difference between
the doublet lines to be
$\Delta\log_{10}N\equiv\log_{10}N_{2,a}-\log_{10}N_{1,a}=0$. On the other
hand, when both lines are fully saturated with normalized fluxes near zero, we
expect $\int\tau_{\rm 1,a}(v)dv\approx\int\tau_{\rm 2,a}(v)dv$, and the column
density difference between the doublet lines to be $N_{\rm 2,a}/N_{\rm
1,a}\approx 2$ or $\Delta\log_{10}N\approx 0.3$ dex (Savage & Sembach, 1991;
Jenkins, 1996). Therefore, for lines that are moderately saturated, the column
density difference between the doublet lines should be:

$-\sigma_{\rm N}\leq\Delta\log_{10}N\leq+\sigma_{\rm N}+0.3~{}{\rm
dex}~{}~{}~{},$ (4)

where $\sigma_{\rm N}$ is the uncertainty tolerance set by the quadratic sum
of the uncertainties in $N_{\rm 1,a}$ and $N_{\rm 2,a}$. In Section 2.5, we
will combine Equation 4 with the wind ratio threshold in Equation 1 to select
stars with reliable interstellar absorption in S ii, Si iv, and C iv.

Figure 6: Distribution of stars with reliable S ii (top), Si iv (middle), and
C iv (bottom panel) LMC interstellar absorption as a function of spectral
type. Those sight lines that we adopt in our LMC analysis (i.e., reliable) are
shown in blue, red, and orange, respectively, and those that we consider
unreliable and thus discarded are shown in gray. See discussion in Section
2.5.

We calculate each line’s centroid velocity weighted by the apparent optical
depth over the same velocity range of $v_{\rm helio}=[175,375]~{}\rm
km~{}s^{-1}$. This is similar to the weighted average velocity used in
Chisholm et al. (2015, 2016), which traces the bulk motion of outflowing gas.
While Chisholm et al. (2015, 2016) show that $v_{90}$, velocity at 90% of the
continuum flux level, can trace low density gas and thus probe the terminal
velocity of outflows (of the corresponding transition lines), we do not
compute $v_{90}$ because of potential contamination from the foreground HVC at
$\sim$90–175 $\rm km~{}s^{-1}$ toward some sight lines (see Figure 5). We
discuss the physical properties of this HVC in Section 6.4.

###  2.5 Summary of Selection Rules and Final Target List

We adopt two criteria to evaluate whether a best-fit continuum produces
reliable S ii, Si iv, or C iv interstellar absorption lines: (1) whether we
expect a star to have a smooth continuum as a result of being a Wolf-Rayet
star or having $f_{w}\leq 0.6$ (Equation 1, Section 2.3), and (2) whether the
column density differences of the normalized ion doublet lines are within
theoretical values specified in Equation 4 (Section 2.4). Note that criterion
1 is only applied to Si iv and C iv lines.

In total, we identify 71 ULLYSES stars (out of 109) with reliable C iv
continuum placement over the LMC’s absorption range, 44/110 with reliable Si
iv, and 91/110 with reliable S ii. The spatial distribution of these reliable
ion measurements across the LMC are shown in Figure 3. We tabulate each ion’s
integrated column density and centroid velocity in Table 2.

Figure 6 shows the distribution of selected stars as a function of spectral
type. Most stars with reliable interstellar C iv absorption are Wolf-Rayet, O
types, or B types earlier than B1, while most stars with reliable interstellar
Si iv are Wolf-Rayet, O types later than O6, or B types earlier than B2. There
are only 44 stars in Si iv passing our selection rules because most of the
early O type stars are with winds that are highly ionized and thus with no
significant Si iv P-Cygni profiles; the lack of winds results in Si iv stellar
features spanning over similar wavelength widths as the MW and LMC
interstellar absorption, such as the top right panel of Figure 4. In such
cases, our algorithm flags the stars as with unreliable Si iv. For S ii, the
only rule that is used to select reliable interstellar absorption is whether
the $\Delta\log_{10}N$ condition in Equation 4 is satisfied. So the
distribution in the S ii panel does not show a particular trend with spectral
types, and most (91/110) stars are with reliable S ii continuum placement.

##  3 Auxiliary Datasets:
H$\alpha$, H i, and Red Supergiant Stars

We supplement the ULLYSES DR5 sample with three additional datasets. We use an
H$\alpha$ emission map (Gaustad et al., 2001) to estimate the LMC’s star
formation rate (SFR) surface density ($\Sigma_{\rm SFR}$) of the LMC in the
past $\sim 10$ Myrs. We analyze an H i 21cm datacube (Kim et al., 2003) to
estimate the total column density and bulk velocity of neutral hydrogen (H i)
along each stellar sight line. Lastly, we adopt an LMC stellar disk kinematic
model derived from a population of red supergiant stars (Olsen et al., 2011).
The derived $\Sigma_{\rm SFR}$, H i column densities and centroid velocities,
and line-of-sight stellar disk velocities are tabulated in Table 2.

Figure 7: Histogram distributions of the ULLYSES DR5 stellar sight lines as a
function of $\Sigma_{\rm SFR}$. Sight lines with reliable S ii (top), Si iv
(middle), and C iv (bottom) are highlighted in blue, orange and red,
respectively.

###  3.1 H$\alpha$ Emission from SHASSA

We obtained the LMC’s H$\alpha$ emission map from the Southern H$\alpha$ Sky
Survey Atlas (SHASSA; Gaustad et al. 2001). The survey covers a range from
${\rm Decl.}\sim-90\degree$ to +15$\degree$ at a spatial resolution of
$\approx 0.8\arcmin$ and with a sensitivity of 2R or $1.2\times
10^{-17}~{}{\rm ergs~{}cm^{-2}~{}s^{-1}~{}arcsec^{-2}}$. As the SHASSA’s
H$\alpha$ filter has a bandwidth of 32Å, the H$\alpha$ emission includes
contamination from the [N ii] 6549/6585 doublet. We expect the contribution
from [N ii] to be negligible (10% or less; Skibba et al. 2012) for the LMC and
do not account for this in our SFR calculation. Because H$\alpha$ emission
originates from recombination of ionized gas into neutral hydrogen atoms in H
ii regions containing massive OB stars, the SFR estimated from H$\alpha$
indicates the lifespan of these stars within the past $\sim 10$ Myr.

We calculate the star formation rate surface density $\Sigma_{\rm SFR}$ as
follows. We first convert the H$\alpha$ intensity $I({\rm H\alpha})$ to
H$\alpha$ luminosity as $L({\rm H\alpha})=4\pi D^{2}\theta^{2}I({\rm
H\alpha})$, where $D=50.1$ kpc (Freedman et al., 2001) and $\theta$ is the
angular size of the region of interest in unit of arcsec. We calculate $L({\rm
H\alpha})$ for every $0.1\times 0.1$ kpc2 area in the LMC; the region size of
$0.1\times 0.1$ kpc2 is to ensure sufficient sampling of the initial mass
function such that the following luminosity to SFR relation holds (Kennicutt &
Evans, 2012). Then we convert each region’s $L({\rm H\alpha})$ to SFR as
$\log_{10}{\rm SFR}=\log_{10}L({\rm H\alpha})-41.27$, following the
formulation in Kennicutt & Evans (2012). Lastly, the SFR surface density
$\Sigma_{\rm SFR}$ is estimated by dividing the SFR value of each region by
the corresponding size of $0.1\times 0.1$ kpc2, yielding a unit of
$M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$.

Figure 7 shows the distribution of the ULLYSES DR5 sight lines as a function
of $\Sigma_{\rm SFR}$. A majority of the sight lines are clustered in regions
with $\Sigma_{\rm SFR}$ ranging from $\sim 10^{-2}$ to $\sim
10^{-0.6}~{}M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$, with a handful of
sight lines directly probing either 30 Dor or other H ii regions (see Figures
2 and 3). The uneven distribution of sight lines across the LMC ultimately
affects the range of outflow environments that we will probe, which we discuss
in Section 4. We note that our continuum fitting and line analyses algorithm
do not exacerbate the sampling bias – Figure 7 shows that the histogram
distributions of reliable S ii, Si iv and C iv measurements are consistent
with the distributions of the original ULLYSES dataset.

###  3.2 H i 21cm Dataset

The H i datacube is a combination of an interferometry observation with the
Australia Telescope Compact Array (ATCA) at 1$\arcmin$ resolution (Kim et al.,
1998), and a single-dish observation with the Parkes multibeam receiver at
16.9$\arcmin$ (Kim et al., 2003). The combined datacube has a spatial
resolution of 1$\arcmin$, a spectral resolution of $1.6~{}\rm km~{}s^{-1}$,
and a flux sensitivity of $\sigma_{\rm T}\sim$2.4 K (or $\sim$15 mJy beam-1).
The datacube spans a velocity range from 190 to 386 $\rm km~{}s^{-1}$ in the
heliocentric frame.

For each sight line in the ULLYSES DR5, we extract median H i fluxes of all
spatial pixels within a diameter of 1 beam (1 arcmin) of the sight line. We
then integrate the H i spectrum over its entire velocity range to obtain an
estimate of the H i column density $N_{\rm HI}$. The centroid velocity $v_{c}$
is estimated as the flux-weighted velocity over pixels in the spectrum with
fluxes higher than $2\sigma_{\rm T}$. The flux threshold here is to ensure
that the centroid velocity of each spectrum reflects the kinematics of the
majority of the bright (dense) H i gas along a line of sight. We find that
this method better traces the center of mass for the H i gas than the velocity
estimated at a peak flux, especially in cases where there are multiple H i
velocity components toward some LMC regions (Kim et al., 2003; Oh et al.,
2022).

We note that there are five sight lines with negative H i fluxes from the
combined datacube that indicate self-absorption: VFTS440, BAT99-105, VFTS-482,
SK-65D47, and SK-67D266. The first three sight lines are near 30 Dor, and the
latter two are near two H i supergiant shells LMC-4 and LMC-5 on the northern
side of the LMC (Book et al., 2008), respectively. For these sight lines, we
do not attempt to estimate the total $N_{\rm HI}$ or centroid velocities, and
note “self-abs” in the corresponding rows and columns in Table 2.
Additionally, there are three sight lines with low H i signals (S/N$<3$); for
these cases, we indicate $3\sigma$ upper limits in $N_{\rm HI}$, but do not
obtain $v_{c}$.

###  3.3 Kinematics of the LMC’s Young Stellar Disk from Red Supergiant Stars

We adopt a kinematic model of the LMC’s stellar disk that is based on the
line-of-sight heliocentric velocities of 738 red supergiant stars (RSGs)
analyzed by Olsen et al. (2011). This model includes the effects of the LMC’s
bulk center-of-mass motion and internal rotation on the observed line-of-sight
velocities, as discussed by van der Marel et al. (2002). The RSGs represent a
young ($\lesssim 20$ Myr) stellar disk, and their internal rotation curve is
found to be consistent with that of the LMC’s H i gas (Olsen et al., 2011).

At the locations of the ULLYSES DR5 sight lines, we calculate the line-of-
sight velocities predicted by the RSG-based model; these are the velocities
that stars would have at the corresponding locations if they resided in the
LMC disk plane, shared the LMC’s center-of-mass motion, and moved on circular
orbits at speeds specified by the fitted rotation curve. We tabulate these
model velocities as $v_{\rm RSG}$ in Table 2, and use $v_{\rm RSG}$ as the
velocity reference of the LMC stellar disk to examine the relative motions of
multiphase gas probed by H i, S ii, Si iv, and C iv in the following sections.

Figure 8: Top: gas column density as a function of star formation rate surface
density ($\Sigma_{\rm SFR}$) for H i, S ii, Si iv, and C iv, respectively.
Solid circles are detections, open squares with upward arrows are saturations
with lower limits (only in S ii, Si iv, and C iv), and open triangles with
downward arrows are non-detections (only in H i). Bottom: 2D spatial
distributions of H i, S ii, Si iv, and C iv column densities in the same
orthographic projection as shown in Figure 3. For S ii, Si iv, and C iv, the
shown $\log N$ values/colors in bins noted with upward arrows should be
considered as conservative lower limits. We highlight several major H ii
regions as crosses in each panel. We find high ion column densities correlate
with regions with high star-forming activities, such as 30 Dor at $x\sim
1.5\degree$ and $y\sim 0\degree$. See Section 4.1 for further details.

##  4 Results

We discuss our main results that compare the S ii, Si iv, and C iv ion
properties to other properties of the LMC such as the H i gas content,
$\Sigma_{\rm SFR}$, and stellar kinematics. The measurements of these
properties are tabulated in Table 2. Specifically, we look into how the
ionized gas kinematics is blue-shifted with respect to the H i and stellar
disk kinematics, indicating the presence of disk-wide outflows.

###  4.1 Ion Column Density vs. $\Sigma_{\rm SFR}$

In the top panels of Figure 8, we compare the column densities of H i, S ii,
Si iv, and C iv with respect to $\Sigma_{\rm SFR}$ toward each line of sight.
H i and S ii trace neutral and low ionization gas in the LMC’s ISM, while Si
iv and C iv trace warm ionized gas at $T\sim 10^{4-5}$ K. In general, we find
that the H i and ionized gas column densities all increase with $\Sigma_{\rm
SFR}$ across the LMC disk.

For S ii, Si iv, and C iv, the column density thresholds above which the ion
lines become saturated (i.e., lower $N$ limits) are around $\sim
10^{15.4}~{}{\rm cm^{-2}}$, $\sim 10^{13.6}~{}{\rm cm^{-2}}$, and $\sim
10^{14.0}~{}{\rm cm^{-2}}$, respectively. The saturation rates of the ion
lines increase toward high $\Sigma_{\rm SFR}$; in general, all ions are
saturated (100%) at $\Sigma_{\rm SFR}\gtrsim 2\times
10^{-1}~{}M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$. We note that, by
design, the ULLYSES sight lines are targeted at massive stars that reside in
or near active star-forming regions (see Figure 3). As is shown in Figure 7,
our sample spans two orders of magnitudes in $\Sigma_{\rm SFR}$ and probes a
variety of star-forming conditions.

Figure 9: Velocity offsets between the multiphase gas (H i, S ii, Si iv, and C
iv) and the LMC’s young stellar disk, $\delta v_{\rm bulk}$ (Equation 5), as a
function of $\Sigma_{\rm SFR}$. The bars on the right sides show the histogram
distributions of $\delta v_{\rm bulk}$. The top left panel shows that the H i
gas largely follows the rotation of the LMC’s young stellar disk. For S ii, Si
iv, and C iv, negative offset velocities indicate outflows; we refer to the
negative values at $\delta v_{\rm bulk}<-15~{}\rm km~{}s^{-1}$ as the outflow
velocities $|v_{\rm out,bulk}|$ and discuss them in absolute values. We
conservatively do not consider data points within $|\delta v_{\rm
bulk}|<15~{}\rm km~{}s^{-1}$ (gray bands) to avoid the LMC’s ISM. Generally,
we find outflows in S ii, Si iv, and C iv with bulk velocities of $|v_{\rm
out,bulk}|\sim 20-60~{}\rm km~{}s^{-1}$, suggesting that the bulk mass of the
outflowing gas should be gravitationally bound. At the higher $\Sigma_{\rm
SFR}$ end, we only detect outflows in Si iv and C iv but not in S ii,
suggesting that star-forming regions with higher $\Sigma_{\rm SFR}$ are likely
to launch outflows that are more ionized. See Section 4.2 for further details.

In the bottom panels of Figure 8, we show the two-dimensional (2D)
distributions of H i, S ii, Si iv, and C iv column densities across the LMC
disk in the same orthographic projection as in Figure 3. We grid the dataset
into $0.5\degree\times 0.5\degree$ bins and calculate mean ion column
densities of all sight lines within each bin. Most bins have one sight line
per bin, and $\sim 10$ bins near major H ii regions have $\sim 2-8$ sight
lines per bin. For sight lines with lower limit $\log N$ values, we treat the
lower limits as detections when calculating the mean and place an upward arrow
in the corresponding spatial bin to indicate a lower limit. We have checked
that the choice of mean or median values does not change the overall $\log N$
patterns shown in the 2D maps.

We find that regions with high ion column densities coincide with active star-
forming sites, such as 30 Dor near $x\sim 1.5\degree$ and $y\sim 0\degree$.
The correlation between ion column densities and $\Sigma_{\rm SFR}$ suggests
that star formation activities in the past $\sim 10$ Myr, as traced by
H$\alpha$ emission, significantly impact the distribution of gas in all phases
across the LMC.

###  4.2 Detection of Disk-Wide Outflows in the LMC

Figure 10: 2D distribution of $\delta v_{\rm bulk}$ (Equation 5) for S ii
(left), Si iv (middle), and C iv (right) in the same orthographic projection
as in Figure 3 and in unit of $\rm km~{}s^{-1}$. Gray indicates regions with
gas kinematics consistent with the LMC’s stellar disk (and ISM) within $\pm
15~{}\rm km~{}s^{-1}$, blue for bulk outflows and red for bulk inflows. Major
H ii regions are indicated by crosses. We find disk-wide ionized outflows in S
ii, Si iv and C iv with bulk velocities of $|v_{\rm out,bulk}|\sim 20-60~{}\rm
km~{}s^{-1}$. See Section 4.2 for further details.

In Figure 9, we show how the bulk velocities of the multiphase gas probed by H
i, S ii, Si iv, and C iv offset from the LMC’s young stellar disk kinematics
as a function of $\Sigma_{\rm SFR}$. We define an offset velocity as

$\delta v_{\rm bulk,x}\equiv v_{\rm helio,x}-v_{\rm RSG}~{}~{}~{},$ (5)

where x indicates H i, S ii, Si iv, or C iv, $v_{\rm helio,x}$ is the flux-
weighted centroid velocity of an ion as calculated in Section 2.4, and $v_{\rm
RSG}$ is the line-of-sight velocity of the LMC’s stellar disk as represented
by red supergiant stars that we discuss in Section 3.3. The values of $v_{\rm
helio,x}$ and $v_{\rm RSG}$ for the ULLYSES DR5 sight lines can be found in
Table 2.

For H i, because the 21cm emission comes from both the front and back sides of
the LMC disk, the signs in $\delta v_{\rm bulk,HI}$ cannot be used to diagnose
inflows or outflows. The top left panel shows that most of the neutral gas
probed by H i has bulk velocities consistent with those of the red supergiant
stars within $\sim$20 $\rm km~{}s^{-1}$. The similarity between H i kinematics
and that of the red supergiant stars suggests that the bulk mass of the H i
gas in the LMC is co-rotating with the underlying young stellar disk (see also
Olsen & Massey 2007; Olsen et al. 2011).

When considering the ionized gas, Figure 9 shows that the bulk velocities of S
ii, Si iv, and C iv are preferentially offset toward negative values with
$\delta v_{\rm bulk}\lesssim 0~{}\rm km~{}s^{-1}$. Given that the stellar
sight lines only probe absorption by gas in the foreground of the stars,
negative $\delta v_{\rm bulk}$ values in the ion panels indicate outflowing
gas from the LMC toward our lines of sight.

Hereafter, we refer to those data points with $\delta v_{\rm bulk}<-15~{}\rm
km~{}s^{-1}$ as outflows, and discuss the outflow velocities in absolute
values as $|v_{\rm out,bulk}|$. Figure 9 shows that the bulk velocities of the
ionized outflows are over a range of $|v_{\rm out,bulk}|\sim 20-60~{}\rm
km~{}s^{-1}$. The bulk mass of the outflowing gas should be gravitationally
bound to the LMC, given that the outflow velocities are within the escape
velocity of the LMC near the disk ($v_{\rm esc}\sim 90~{}\rm km~{}s^{-1}$;
Barger et al. 2016) and a majority of the outflow mass is found to be in the
cool-warm phase in simulations (e.g. Li et al., 2017; Kim et al., 2020;
Rathjen et al., 2021). Note that the $|v_{\rm out,bulk}|$ values are the
projected outflow velocities along our lines of sight, and they should be
considered as lower limits to the actual outflow velocities in the LMC; in
Section 5, we discuss the impact of the LMC’s inclination and other factors on
the observed outflow velocities.

We find S ii, Si iv, and C iv outflows commonly detected over $\Sigma_{\rm
SFR}\sim 10^{-2}-10^{-1}~{}M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$. Toward
the higher $\Sigma_{\rm SFR}$ end, outflows are only detected in Si iv and C
iv but not in S ii, which indicates that star-forming regions with high
$\Sigma_{\rm SFR}$ are launching outflows that are likely to be more ionized.
We investigate whether the outflows with high ionization states and in high
$\Sigma_{\rm SFR}$ regions are preferentially associated with Wolfe-Rayet or O
types earlier than O5. While there is tentative evidence that in high
$\Sigma_{\rm SFR}$ regions the C iv outflows are detected toward more Wolfe-
Rayet/early O type stars, the statistics are too small for a conclusive
result.

We find large scatters in the outflow velocities with respect to $\Sigma_{\rm
SFR}$. Although a Spearman rank-order correlation test does indicate a
significant correlation between the Si iv outflow velocities and $\Sigma_{\rm
SFR}$ ($p_{\rm SiIV}=0.02$, bottom left panel), we refrain from suggesting a
trend given the limited data points toward the higher $\Sigma_{\rm SFR}$ end.
The scatters in the outflow velocities are likely to be caused by multiple
factors, such as outflow opening angles, ages and locations (i.e., outflows
launched at different times may reach different heights). We further discuss
the scatters in Section 5.

Lastly, in Figure 10, we show the 2D spatial distributions of $\delta v_{\rm
bulk}$ for S ii, Si iv, and C iv. The offset velocities $\delta v_{\rm bulk}$
are averaged over $0.5\degree\times 0.5\degree$ bins to bring out the large-
scale kinematic pattern in the ionized gas across the LMC. Gray pixels
indicate that the ion velocities are consistent with the LMC’s stellar disk
(and ISM) within $\pm 15~{}\rm km~{}s^{-1}$, and blue pixels highlight regions
with outflows. We find that the Si iv and C iv outflows are commonly found
near major H ii regions. Some H ii regions with high $\Sigma_{\rm SFR}$, such
as 30 Dor, do not have outflows in S ii, which is likely due to outflows being
more ionized in these regions, as is also shown in Figure 9.

Figure 11: 2D distributions of H i, S ii, Si iv, and C iv bulk velocities in
the heliocentric frame and in unit of $\rm km~{}s^{-1}$. The systemic velocity
of the LMC at the center of its mass (x,y)=(0$\degree$, 0$\degree$) is $v_{\rm
sys}=264~{}\rm km~{}s^{-1}$ (Choi et al., 2022), which is shown as gray color.
Major H ii regions are indicated by black crosses. All ions show signs of co-
rotation with the LMC’s disk. See Section 4.3 for further details.

With most of the mass gravitationally bound to the LMC disk, the outflowing
gas is likely to be part of the LMC’s galactic fountain flows and would
eventually reverse its course and become inflows toward the LMC at cooler
phases, as typically seen in outflow simulations (e.g. Kim & Ostriker, 2018).
However, in Figure 10, the ionized outflows are commonly detected across the
LMC disk in all ions, while inflows are not as common. The rare exceptions are
two bins in S ii near $(x,y)\sim(2\degree,-1.5\degree)$, and one bin in Si iv
near $(x,y)\sim(-2\degree$, $2\degree$). We discuss potential causes for these
rare inflow detections in Section 6.5.

###  4.3 Outflows Co-rotating with the LMC Disk

In Figure 11, we show the 2D distributions of H i, S ii, Si iv, and C iv bulk
velocities in the heliocentric frame. The H i moment one map (left panel) is
based on the 21cm emission data cube from Kim et al. (2003) as discussed in
Section 3.2, while the S ii, Si iv, and C iv are measured along the ULLYSES
sight lines and averaged over $0.5\degree\times 0.5\degree$ bins in the same
way as for the $\log N$ values discussed in Section 4.1.

The H i panel shows that the northeast half of the LMC disk is moving away
from us at a faster speed of $v_{\rm helio}\sim 280-320~{}\rm km~{}s^{-1}$,
while the southwest half is moving slower at $v_{\rm helio}\sim 200-240~{}\rm
km~{}s^{-1}$. Note that the northeast half of the LMC disk is closer to us
(van der Marel et al., 2002), and the LMC is rotating clockwise. In the S ii,
Si iv, and C iv panels, we find that the ions’ bulk velocities exhibit a
rotation pattern similar to the one in H i, indicating that the ionized
outflows are co-rotating with the LMC disk.

In all, Figures 8–11 show a coherent picture that the LMC is currently
launching disk-wide, warm-ionized outflows with bulk velocities of $|v_{\rm
out,bulk}|\sim 20-60~{}\rm km~{}s^{-1}$. The bulk mass of the outflowing gas
should be gravitationally bound to the LMC, and is co-rotating with the LMC.
Star-forming regions with higher $\Sigma_{\rm SFR}$ are launching outflows
that are likely to be more ionized.

##  5 A Scaling Relation between Outflows and Star Formation

We compare our LMC outflow measurements with three literature studies,
including Heckman et al. (2015), Chisholm et al. (2015), and Xu et al. (2022),
which also examine ionized outflows with $T\sim 10^{4-5}$ K in commonly
accessible ions such as Si ii, Si iii, Si iv, and C iv. Because these works
measured outflows using either the same or similar instruments (either HST or
FUSE spectroscopy101010The HST and FUSE spectra generally have similar
velocity resolutions. The HST/COS spectra typically have $\delta v=15-25~{}\rm
km~{}s^{-1}$, HST/STIS spectra have $\delta v=6.6~{}\rm km~{}s^{-1}$ in E140M,
and FUSE have $\delta v\sim 15~{}\rm km~{}s^{-1}$.), the comparison below
avoids significant systemic uncertainties such as differences in spectral
resolution and outflow phases. Note that the galaxy samples from these three
studies are not mutually exclusive. We elaborate on the key aspects of each
work, and adopt the most recent measurements for galaxies that have been used
in more than one study.

Heckman et al. (2015) studied warm outflows in 39 starburst galaxies at
$z<0.2$ using Si iii (COS) and N ii and Ciii (FUSE), and their galaxy sample
covers a parameter space of $M_{*}=10^{7.1-10.9}~{}M_{\odot}$ and SFR=0.016–66
$M_{\odot}~{}{\rm yr}^{-1}$. Chisholm et al. (2015) examined outflows traced
by Si ii in 48 star-forming galaxies with $M_{*}=10^{7.3-11.4}~{}M_{\odot}$
and SFR=0.02–136.8 $M_{\odot}~{}{\rm yr}^{-1}$ at $z=0.0007-0.26$. Both
studies measured bulk velocities of outflows (center of mass) using weighted
average velocities, similar to what we did for the LMC outflows. Chisholm et
al. (2015) noted that Si ii may not be a perfect tracer of warm ionized
outflows because it only requires 8.2 eV to produce and 16.3 eV to ionize,
which means the ion traces both neutral and ionized outflows. However, a
follow-up study by Chisholm et al. (2016) using the same sample111111Chisholm
et al. (2016) studied 37 star-forming galaxies, which is a subset of Chisholm
et al. (2015)’s because of an implementation of $3\sigma$ cut in equivalent
widths to only select galaxies with significant outflow detections. showed
that outflows probed by different ions such as O i, Si ii, Si iii, and Si iv
are most likely to be comoving and cospatial given the similarity in the
derived outflow velocities and widths. Therefore, in Figure 12 we compare the
Si iv outflows in the LMC with Chisholm et al. (2015)’s Si ii measurements
without additional correction.

Figure 12: Scaling relations between bulk outflow velocity and $\Sigma_{\rm
SFR}$ (top) and SFR (bottom). We compare the LMC outflows with three studies
on starburst galaxies that are based on the same or similar instruments
(either HST or FUSE) to minimize systemic uncertainties. The black lines show
the best-fit power-law relations for all the data points. The LMC bulk
outflows lie at the slower end of the scaling relation.

Xu et al. (2022, CLASSY III) studied galactic outflows in 45 starburst
galaxies with $M_{*}\sim 10^{6-10}~{}M_{\odot}$ and SFR$\sim
0.01-100~{}M_{\odot}~{}{\rm yr}^{-1}$ at $0.002<z<0.182$. For each galaxy,
they fit Gaussian profiles to ISM absorption, and when available, outflow
absorption lines in O i, C ii, Si ii, Si iii, and Si iv. The final outflow
velocities are taken as the median values of outflows detected in all
available ion lines along the corresponding sight lines. Because the
contribution of the ISM absorption has been taken out, by design, their
outflow velocities would be slightly faster than those measured for the same
galaxies using simply flux-weighted centroid velocities. Therefore, the
outflow velocities adopted from Xu et al. (2022) may be systematically higher
than the rest because of their different method.

Figure 12 shows the outflow velocity as a function of $\Sigma_{\rm SFR}$ (top)
and global SFR (bottom). Our LMC measurements (red circles) in the top panel
are those of Si iv with $v_{\rm out,bulk}\leq-15~{}\rm km~{}s^{-1}$, while in
the bottom panel it represents the LMC’s global SFR of $\sim
0.2~{}M_{\odot}~{}{\rm yr}^{-1}$ (Harris & Zaritsky, 2009; Mazzi et al., 2021)
and a median Si iv bulk outflow velocity of $31.8\pm 10.5~{}\rm km~{}s^{-1}$,
with the error bar indicating a $1\sigma$ standard deviation.

In black and gray lines, we show power-law fits to all data points using emcee
(Foreman-Mackey et al., 2013). The relations, $v_{\rm out,bulk}=v_{0}{\rm
X}^{\alpha}$, are fitted in linear space to take into account the errors in
outflow velocities, with $\rm X$ being either $\Sigma_{\rm SFR}$ (top panel)
or SFR (bottom panel). Because Heckman et al. (2015) did not include errors in
$|v_{\rm out,bulk}|$, we assume a uniform error of $15~{}\rm km~{}s^{-1}$ for
those data points, which is the median error value found in our LMC
measurements. We adopt priors of $v_{0}\in[-1000,1000]~{}\rm km~{}s^{-1}$ and
$\alpha\in[-100,100]$ to fully explore the parameter space. The best fit
results are:

$\begin{split}v_{\rm out,bulk}&=(79\pm 2~{}\rm
km~{}s^{-1})\times(\frac{\rm\Sigma_{\rm SFR}}{1.0~{}M_{\odot}~{}{\rm
yr}^{-1}~{}{\rm kpc}^{-2}})^{0.22\pm 0.01}\\\ v_{\rm out,bulk}&=(80\pm 2~{}\rm
km~{}s^{-1})\times(\frac{\rm SFR}{1.0~{}M_{\odot}~{}{\rm yr}^{-1}})^{0.29\pm
0.01}~{}~{}~{},\end{split}$ (6)

which are 50th percentiles from our MCMC runs, and the errors indicate the
$84^{\rm th}-50^{\rm th}$ and $16^{\rm th}-50^{\rm th}$ differences. The
results do not change significantly if we use the LMC’s C iv outflow
measurements instead; the power-law index changes from 0.22 to 0.27 in the
$v_{\rm out,bulk}$–$\Sigma_{\rm SFR}$ relation and remains unchanged in the
$v_{\rm out,bulk}$–SFR relation.

Given that the literature data points dominate the fits in Figure 12, it is
not surprising that our power-law indexes are consistent with what have been
typically found in the literature ($v_{\rm out,bulk}\propto\Sigma_{\rm
SFR}^{0.1-0.2}$, $v_{\rm out,bulk}\propto{\rm SFR}^{0.2-0.35}$; Martin 2005;
Rupke et al. 2005; Chen et al. 2010; Chisholm et al. 2015). What is remarkable
is that, in spite of spanning four orders of magnitudes in $\Sigma_{\rm SFR}$
or SFR, there is a coherent scaling relation between the outflow velocities
and star formation activities, with the LMC lying at the lower end. Figure 12
shows that dwarf galaxies like the LMC are capable of launching outflows not
only in star-bursting environments such as 30 Dor, but also in regions with
low star formation surface densities at $\Sigma_{\rm SFR}\lesssim
10^{-1}~{}M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$.

Lastly, we note that the uncertainties in the best-fit parameters in Equation
6 seem small; this is mainly driven by the fact that our power-law fits do not
account for the scatters in the outflow velocities, which are much larger than
the measurement errors. As discussed in Chisholm et al. (2015), there are a
number of factors that could cause the scatters such as galaxy inclinations,
outflow driving mechanisms (energy or momentum driven), and the CGM masses of
the host galaxies which would impact the propagation of outflows as they leave
the disks. The LMC is nearly face-on with an inclination angle of $i\sim
23.4\degree$ (Choi et al., 2022), which would lead to an increase of ${\rm
1/cos}(i)=1.08$ in the outflow velocities, assuming that the outflows have
been launched perpendicular to the LMC disk. We do not correct for the
inclination because: (1) galaxy inclinations were not systemically taken into
account in Chisholm et al. (2015), Heckman et al. (2015), and Xu et al.
(2022); and (2) the LMC outflows, as well as outflows in other galaxies
included in Figure 12, could have been launched in various angles with respect
to the galaxy disks or have opening angles that are not well constrained.

Additionally, for data points that we adopted from Heckman et al. (2015),
Chisholm et al. (2015), and Xu et al. (2022), each pointing represents a
single galaxy or a large fraction of a star-forming disk within the
instrument’s aperture; therefore, their measurements show the aperture-
averaged galactic scale outflows. For the LMC, the ULLYSES sight lines probe
localized outflows that are sensitive to individual star-forming regions.
Therefore, the scatters in our LMC measurements most likely reflect the
intricate interaction between the outflows and the ambient gas in the ISM as
well as the disk-halo interface of the LMC. In a follow-up paper, we will
continue exploring how localized outflows connect to individual star-forming
regions in the LMC.

##  6 Discussion

###  6.1 Conservative Estimates on Outflow Mass,
Outflow Rate, and Mass Loading Factor

We estimate the LMC’s bulk outflow mass $M_{\rm out}$, outflow rate
$\dot{M}_{\rm out}$, and mass loading factor $\eta~{}(\equiv\dot{M}_{\rm
out}/{\rm SFR})$ by considering the outflows from both sides of the disk. We
assume that the back side outflows, though not observable with ULLYSES, are
similar to those in the near side. The assumption is based on Barger et al.
(2016)’s star-QSO pair observation that ion absorptions due to outflows from
both sides of the LMC show similar absorption depths and velocity spans (see
Section 6.3).

The following calculations are based on measurements of C iv. We choose C iv
because there are more ULLYSES sight lines with reliable C iv measurements
(71/109) than with Si iv (44/110), as is shown in Figure 7, which allows us to
better estimate the outflow covering fraction across the LMC. Our estimates
should be treated as conservative lower-limit values, given that most of the C
iv outflows are saturated (see Figure 9) and we assume the maximum C iv
ionization fraction of $f_{\rm CIV}=0.3$ possible through either equilibrium
collisional ionization or photoionization (Gnat & Sternberg, 2007).

We simplify the LMC outflows as gas moving in a cylindrical volume with a
total mass of:

$\begin{split}M_{\rm CIV}&=2\times m_{\rm C}\times(N_{\rm CIV}\times{\rm
cos}~{}i)\times\pi R_{\rm out}^{2}\times c_{\rm f}\\\ &\gtrsim 2.7\times
10^{2}~{}M_{\odot}(\frac{N_{\rm CIV}}{10^{14}~{}\rm cm^{-2}})(\frac{R_{\rm
out}}{3.5~{}{\rm kpc}})^{2}(\frac{c_{\rm f}}{0.4})~{}~{}~{},\end{split}$ (7)

where the factor of 2 is to take into account the LMC’s back side outflows and
$m_{\rm C}$ is the mass of a carbon atom. We set $N_{\rm CIV}\approx
10^{14}~{}{\rm cm^{-2}}$, which is the characteristic column density where C
iv begins to saturate (see Figure 8), and the factor of ${\rm cos}~{}i$ is to
correct for the increased path length through the outflowing layer due to the
LMC’s inclination $i=23.4\degree$ (see Table 1). $R_{\rm out}\approx 3.5$ kpc
is the maximum in-plane radius probed by our sight lines that show clear
detections of outflows (see Figure 10).

We find a C iv outflow covering fraction of $c_{\rm f}\approx 0.4$ (14/33) by
counting the number of bins in Figures 8 and 10 with $\bar{N}_{\rm CIV}\geq
10^{14}$ cm-2 and $\bar{v}_{\rm bulk,CIV}\leq-15~{}\rm km~{}s^{-1}$, and then
dividing the value by the number of bins with reliable C iv measurements. We
choose to calculate $c_{\rm f}$ based on the spatially average 2D maps because
it avoids over-sampling outflows in high star-forming regions, such as 30 Dor,
which are probed by many ULLYSES sight lines simultaneously. Varying the bin
sizes of $0.5\degree\times 0.5\degree$ to smaller ($0.3\degree\times
0.3\degree$) or larger ($1.0\degree\times 1.0\degree$) grids only changes the
$c_{\rm f}$ value by $\sim 0.1$.

We assume that the outflows have the same metallicity as the LMC’s ISM
($Z_{\rm out}\approx 0.5~{}Z_{\odot}$) and estimate the total mass of the
outflows to be:

$\begin{split}M_{\rm out}&=1.4m_{\rm p}\times\frac{M_{\rm CIV}}{f_{\rm
CIV}\times m_{\rm C}\times Z_{\rm out}\times{\rm[C/H]_{\odot}}}\\\ &\gtrsim
8\times 10^{5}~{}M_{\odot}~{}(\frac{f_{\rm CIV}}{0.3})^{-1}(\frac{Z_{\rm
out}}{0.5~{}\rm Z_{\odot}})^{-1}~{}~{}~{},\end{split}$ (8)

where the factor of 1.4 is to account for the helium mass, and
[C/H]${}_{\odot}=10^{8.43-12.0}$ is the solar photosphere abundance (Asplund
et al., 2009).

Recent analyses on the LMC’s star formation history show that the galaxy is
currently experiencing a high star-forming episode that began $\sim$30 Myr ago
(Mazzi et al., 2021). We assume that the LMC began to launch the most recent
outflows around the same time and has continued driving outflows until the
present day with a time duration of $t_{\rm out}\sim 30$ Myr. The LMC’s mass
outflow rate and the mass loading factor are:

$\begin{split}\dot{M}_{\rm out}&=M_{\rm out}/t_{\rm out}\gtrsim
0.03~{}M_{\odot}~{}{\rm yr}^{-1}~{}(\frac{t_{\rm out}}{30~{}\rm Myr})^{-1}\\\
\eta&=\dot{M}_{\rm out}/{\rm SFR}\gtrsim 0.2\times(\frac{\rm
SFR}{0.03~{}M_{\odot}~{}{\rm yr}^{-1}})^{-1}~{}~{}~{},\end{split}$ (9)

where ${\rm SFR}\approx 0.2~{}M_{\odot}~{}{\rm yr}^{-1}$ is the global SFR of
the present day LMC adopted from Harris & Zaritsky (2009) and Mazzi et al.
(2021).

When compared with literature values, we find that our $M_{\rm out}$ and
$\dot{M}_{\rm out}$ values are consistent with Barger et al. (2016)’s
estimates for the LMC based on their C iv measurements (see their Table 4).
Note that our estimates should be treated as strictly conservative values,
given that the C iv outflows are mostly saturated in the LMC and we assume the
maximum ionization fraction.

Lastly, considering the time duration of $t_{\rm out}\sim 30$ Myr and a mean
bulk outflow velocity of $\sim 30~{}\rm km~{}s^{-1}$ in C iv, we can infer
that the bulk mass of the LMC outflows is at a height of $z_{\rm out}\sim 0.9$
kpc. This is consistent with our observation in Figure 11 that the outflowing
gas is still under the gravitational influence of the LMC’s disk and thus
shows kinematic signs of co-rotation with the disk.

###  6.2 Shielding of the LMC Outflows by A Potential Bow Shock

At a Galactocentric velocity of $321~{}\rm km~{}s^{-1}$ (Kallivayalil et al.,
2013), the LMC is moving in the MW’s halo with a Mach number of $\sim 2.1$
(Setton et al., 2023). Using an LMC-specific hydrodynamic simulation, Setton
et al. (2023) showed that the LMC’s supersonic movement is likely to generate
a bow shock leading the LMC due to ram pressure from the MW, as is illustrated
in Figure 1. The ram pressure impact has been well observed in the LMC’s
truncated H i disk (Salem et al., 2015), and it is likely that the ram
pressure also shapes the morphology of the LMC’s H$\alpha$ emission (Smart et
al., 2023; Setton et al., 2023). Although the existence of an LMC bow shock
remains to be observationally tested, below we provide indirect evidence of
this bow shock by speculating that it may have shielded the LMC outflows from
the MW’s ram pressure.

When a satellite galaxy orbits a massive host, its ISM gas is subject to ram
pressure stripping from the halo of the massive host. This is commonly seen in
dwarf galaxies closer to the MW and M31 (Putman et al., 2021), as well as in
jelly-fish galaxies in large galaxy groups and clusters (e.g. Poggianti et
al., 2016). Zhu et al. (2023) showed that when a galaxy’s disk is at an angle
of $45\degree$ against the headwinds due to ram pressure, the gas above the
galaxy disk is being swept downstream and flowing mainly parallel to the
galaxy disk (see their Figure 13). Had the LMC experienced such strong ram
pressure stripping, we would expect the outflowing gas to have been swept in
the opposite direction of the LMC’s proper motion (east to west), and the
outflow column densities are unlikely to correlate with $\Sigma_{\rm SFR}$.

Our analyses in Figures 8–11 show that the LMC outflows are well correlated
with $\Sigma_{\rm SFR}$ in ion column densities, and the outflows are co-
rotating with the LMC disk. Calculations in Section 6.1 suggest that the bulk
mass of the outflowing gas is close to the LMC disk at a height of $\approx
0.9$ kpc, which is well within the size of the bow shock predicted in Setton
et al. (2023)’s LMC simulation. Additionally, Barger et al. (2016) showed that
the LMC outflows in the near and back sides show similar absorption strengths
and velocity spans, indicating that the outflows on the near side are not
significantly suppressed. As there is no significant sign of the outflowing
gas being impacted by external forces, such as ram pressure from the MW halo
gas, we suspect that the LMC’s outflows may have been shielded by a potential
bow shock as the LMC orbits the MW supersonically.

###  6.3 Comparison with Previous LMC Gas Studies

In this section, we briefly compare our work with previous studies on the LMC
gas over $v_{\rm helio}\sim 175-375~{}\rm km~{}s^{-1}$ (see Figure 5). The key
message is that the outflows we find are correlated with the most recent star-
formation episode of the LMC, and they are gravitationally bound to the LMC
with $|v_{\rm out,bulk}|\sim 20-60~{}\rm km~{}s^{-1}$, consistent with
previous studies using smaller samples of stellar sight lines. These outflows
are not connected to the high-velocity cloud at $v_{\rm helio}\sim
90-175~{}\rm km~{}s^{-1}$ in the foreground of the LMC; we discuss the
physical properties of the high-velocity cloud and relevant studies in Section
6.4.

The LMC gas at $v_{\rm helio}\sim 175-375~{}\rm km~{}s^{-1}$ is found to be
multi-phase as seen in both emission and absorption lines. Figure 2 shows that
H i 21cm is found across the galaxy tracing large and small scale neutral gas
structures of the LMC disk such as outer arms and supergiant shells (Kim et
al., 2003; Staveley-Smith et al., 2003; Nidever et al., 2008). This neutral
gas mainly follows the rotation of the LMC stellar disk, and does not show
signs of outflows (see top left panel of Figure 9). Smart et al. (2023) found
that the H$\alpha$ emission from the LMC is more extended than the H i by
several degrees, and the H$\alpha$ gas kinematics is found to weakly trace the
H i gas rotation.

Howk et al. (2002) studied O vi obtained from the Far Ultraviolet
Spectroscopic Explorer toward 12 LMC stellar sight lines over $175\lesssim
v_{\rm LSR}\lesssim 375~{}\rm km~{}s^{-1}$ (see also Danforth et al. 2002).
The O vi column densities show a large variation, which is not correlated with
underlying structures such as H i superbubbles. They found the O vi centroid
velocities to be blueshifted from the LMC’s low ion absorption lines (e.g., Fe
ii) by $\sim-30~{}\rm km~{}s^{-1}$, suggesting the presence of highly ionized
outflows among these sight lines, consistent with our findings of outflows in
Si iv and C iv.

Barger et al. (2016) probed a relatively quiet northwest region of the LMC
using a pair of QSO-star sight lines in close projection over $165\lesssim
v_{\rm LSR}\lesssim 415~{}\rm km~{}s^{-1}$. Ion spectra from both the star and
the QSO show blueshifted outflows over $\sim 165-280~{}\rm km~{}s^{-1}$, while
only the QSO spectra show redshifted outflows at $\sim 280-415~{}\rm
km~{}s^{-1}$ on the back side of the LMC disk. They found that the outflows in
the near and back sides show similar absorption strengths and velocity spans
in O i, Si ii, Al ii, Si iii, Si iv, and C iv.

Note that the $\sim 100~{}\rm km~{}s^{-1}$ outflow speeds quoted by Barger et
al. (2016) were measured toward the edge of the ion absorption, which
represents the terminal velocities of the low-density outflowing gas. In
contrast, the outflow velocities measured in this work as well as in Howk et
al. (2002) are flux-weighted outflows representing the bulk mass of the
outflowing gas. As shown in Figure 5, we also find outflows with high terminal
velocities of $v\sim 100~{}\rm km~{}s^{-1}$ toward the ULLYSES sight lines.
However, we do not use the terminal outflow velocities in this work to avoid
potential contamination due to the high-velocity cloud in the foreground,
which we discuss in the next section.

###  6.4 The Foreground High-Velocity Cloud at $v\sim 90-175~{}\rm
km~{}s^{-1}$

A number of studies have noted the presence of a high-velocity cloud moving at
$v_{\rm helio}\sim 90-175~{}\rm km~{}s^{-1}$ in the foreground of the LMC;
hereafter, we refer to this cloud as HVC90-175. As sketched in Figure 1,
Richter et al. (2015) constrained the distance to HVC90-175 to be within 13.3
kpc from the Sun using an HST/COS spectrum of a hot white dwarf (RX
J0439.8−6809; Werner & Rauch 2015), which means HVC90-175 is located in the
inner halo of the MW and at $\delta d\sim 40$ kpc from the LMC.

We show in Section 2.4 and Figure 5 that HVC90-175’s absorption can be well
constrained to be within $v_{\rm helio}\lesssim 175~{}\rm km~{}s^{-1}$ and the
blending with the LMC absorption is relatively mild. Specifically, for Si iv
(C iv) absorption, we find only 10/44 (22/71) sight lines with non-negligible
blending between HVC90-175 and the LMC outflows near $v_{\rm helio}=175~{}\rm
km~{}s^{-1}$. In these cases, the LMC is likely to launch fast outflows with
terminal velocities of $\gtrsim 100~{}\rm km~{}s^{-1}$ that are blended with
HVC90-175. By focusing on the LMC absorption over $v_{\rm helio}=175-375~{}\rm
km~{}s^{-1}$ in this work, we minimize contamination from HVC90-175.

The origin of HVC90-175 remains debated. In Appendix A and Figures 13-14, we
show that HVC90-175 is kinematically consistent with being a MW halo cloud at
a constant velocity of $v_{\rm LSR}\sim 120~{}\rm km~{}s^{-1}$ (see also
Savage & de Boer 1981; de Boer et al. 1990; Richter et al. 1999). The spread
in line of sight velocities towards HVC90-175 can be well accounted for if we
assume a temperature of $T\sim 10^{4.2}$ K and a non-thermal broadening of
$\sigma_{\rm nth}\sim 10~{}\rm km~{}s^{-1}$ (see details in Appendix A), which
is typical for MW halo clouds (Putman et al., 2012). The distance of
HVC90-175, $d_{\odot}<13.3$ kpc, is also consistent with other MW halo clouds
(Wakker, 2001).

We note that HVC90-175 has also been suggested to be associated with the LMC
as a fast-moving, ancient outflow that was launched by the LMC’s previous
star-forming episode about $t_{\rm SFR}\sim 250-400$ Myr ago (e.g. Barger et
al., 2016; Ciampa et al., 2021). At a distance of $\delta d\sim 40$ kpc from
the LMC, it would be challenging to launch an outflow that did not decelerate,
increase opening angle, or change trajectory. As the LMC moves through the MW
halo in a nearly transverse direction (see discussion in Section 6.2), the
presumed ancient outflow would have been severely impacted by ram pressure
when traveling such a large distance. Therefore, we consider it highly
unlikely for HVC90-175 to originate from the LMC as an ancient outflow.

###  6.5 Rare Detection of Inflows

We note the rare detection of an inflow in Si iv toward a sight line,
SK-67D22, at a velocity of $+26~{}\rm km~{}s^{-1}$ with respect to the LMC’s
stellar disk, as is shown in the lower left panel of Figure 9. The
corresponding C iv line shows a similar absorption profile, although its
centroid velocity is $+8~{}\rm km~{}s^{-1}$ because of the flux-weighting
algorithm that we apply in Section 2.4. SK-67D22 is located at
$x\sim-2\degree$ and $y\sim 2\degree$ in Figure 10, which is the only bin in
the Si iv panel that shows inflow detection. We examine SK-67D22’s line
spectra (not shown here), and find that the neutral (H i) and low ionization
(S ii) gas is moving at $v_{\rm helio}\sim 290~{}\rm km~{}s^{-1}$, consistent
with the underlying stellar disk ($v_{\rm RSG}\sim 285~{}\rm km~{}s^{-1}$).
The more ionized Si iv and C iv gas is found over $v_{\rm helio}\sim
300-340~{}\rm km~{}s^{-1}$, indicating that the ionized gas is inflowing
toward the LMC disk at $v_{\rm in}\sim 10-50~{}\rm km~{}s^{-1}$ in a
relatively quiet region of the galaxy.

Similarly, there are two sight lines with S ii inflows at $v_{\rm in}\sim
15-20~{}\rm km~{}s^{-1}$ in the southeast corner of the LMC where the star
formation is less active (see Figures 3 and 10). In the heliocentric frame,
the S ii inflows are found at $v_{\rm helio}\sim 300~{}\rm km~{}s^{-1}$, which
coincides with the H i arm E of the LMC as identified by Staveley-Smith et al.
(2003); we further investigate the connection between our S ii detection and
the arm E in a follow-up paper.

Overall, the rare detections of inflows in Si iv and S ii suggests that the
inflows may still exist in the LMC, but in areas that are relatively less
sampled by the ULLYSES sight lines. Another possible explanation for the
dominant outflow detection is that the LMC is currently undergoing an active
star formation episode (Harris & Zaritsky, 2009; Mazzi et al., 2021), which
drives disk-wide outflows. Inflows may not occur until $\sim 50-60$ Myrs later
when outflows turn around, cool down, and rain back down to the disk, as seen
in hydrodynamic simulations (e.g. Kim & Ostriker, 2018).

##  7 Summary

Using 110 HST stellar sight lines from the ULLYSES DR5 (Roman-Duval et al.,
2019b), we detect prevalent slow-moving, ionized outflows ($|v_{\rm
out,bulk}|\sim 20-60~{}\rm km~{}s^{-1}$) in Si iv and C iv across the disk of
the LMC. This work is the first in a series from our HST archival program
(#HST-AR-16640, PI Zheng${}^{\ref{footnote2}}$), in which we examine how the
LMC’s multiphase gas is impacted by the interplay between its star-forming
activities and ram pressure from the MW’s CGM. We summarize the main analyses
and findings as follows.

We study the LMC neutral and ionized gas over $v_{\rm helio}=175-375~{}\rm
km~{}s^{-1}$ in H i, S ii, Si iv, and C iv. The velocity range is chosen to
encompass the LMC gas in all directions while avoiding contamination from a
foreground high-velocity cloud at $d_{\odot}<13.3$ kpc (Figure 1). The ion
lines are chosen for their relatively less saturated line profiles over the
LMC’s velocity range. We develop a continuum-fitting algorithm based on the
concept of Gaussian Process regression, and select reliable ion spectra with
minimal contamination from stellar absorption. Our algorithm results in 91/110
reliable LMC measurements in S ii, 44/110 in Si iv, and 71/109 in C iv (see
Section 2 and Figures 3–7).

We find that the column densities ($\log N$) of the LMC’s neutral (H i) and
ionized (S ii, Si iv, C iv) gas increase with the star formation rate surface
density $\Sigma_{\rm SFR}$. Most of the Si iv and C iv measurements are
heavily saturated with $N(\rm SiIV)\geq 10^{13.6}$ cm-2 and $N(\rm CIV)\geq
10^{14.0}$ cm-2, and all ions are saturated at $\Sigma_{\rm SFR}\gtrsim
0.2~{}M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$. As $\Sigma_{\rm SFR}$ is
derived based on the H$\alpha$ emission that traces the LMC’s recent star
formation, the correlation between $\log N$ and $\Sigma_{\rm SFR}$ suggests
that the LMC’s star-forming activities may have an impact on its multiphase
gas over a short timescale of a few Myr (see Section 4.1 and Figure 8).

We compare the flux-weighted centroid velocities of the neutral (H i) and
ionized (S ii, Si iv, C iv) gas to the LMC’s stellar kinematics. The flux-
weighted centroid velocities indicate the bulk motion of the gas where most of
the mass is. Our key finding is that the velocities of the ionized gas are
systemically blue-shifted from the LMC’s stellar disk, which indicates
prevalent outflows at bulk velocities of $|v_{\rm out,bulk}|\sim 20-60~{}\rm
km~{}s^{-1}$. While Si iv and C iv outflows are detected over a wide range of
star-forming conditions with $\Sigma_{\rm SFR}\sim
10^{-2}-10^{0}~{}M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$, S ii outflows
are only found in regions with relatively low $\Sigma_{\rm SFR}$ ($\lesssim
10^{-1}~{}M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$). This indicates that
star-forming regions with high $\Sigma_{\rm SFR}$ are launching outflows that
are likely to be more ionized (see Section 4.2 and Figure 9).

We estimate the physical properties of the LMC bulk outflows using the C iv
measurements, and find strictly conservative lower limits with a total outflow
mass of $M_{\rm out}\gtrsim 8\times 10^{5}~{}M_{\odot}$, an outflow rate of
$\dot{M}_{\rm out}\gtrsim 0.03~{}M_{\odot}~{}{\rm yr}^{-1}$, and a mass
loading factor of $\eta\gtrsim 0.2$. The values are estimated for the ionized
outflows from both sides of the LMC (see Section 6.1). When comparing with
outflows detected in starburst galaxies from previous studies, we find a
universal scaling relation of $|v_{\rm out,bulk}|\propto\Sigma_{\rm
SFR}^{0.22}$ and $|v_{\rm out,bulk}|\propto{\rm SFR}^{0.29}$ (Figure 12). The
comparison shows that outflows can be launched over a wide range of star-
forming conditions with $\Sigma_{\rm SFR}\sim
10^{-2}-10^{2}~{}M_{\odot}~{}{\rm yr}^{-1}~{}{\rm kpc}^{-2}$ (see Section 5).

Lastly, we release the first 2D UV ion maps of the LMC in Figures 8–11, and
show that the Si iv and C iv outflows are stronger (in column density) in high
star-forming regions such as 30 Dor. Additionally, the Si iv and C iv outflows
show signs of co-rotation with the LMC disk. Given that there is no
significant sign of impact from external forces such as ram pressure from the
MW, we suspect that the outflows are likely to be shielded behind a potential
bow shock that is leading the LMC as the galaxy orbits the MW supersonically.
The existence and exact location of this potential bow shock remains to be
tested observationally (see Sections 4.3 and 6.2).

Y.Z. thanks John Chisholm, Grace Telford, Dan Weisz, and Alessandro Savino for
discussion at various points during the preparation of this manuscript, and
thanks David Setton, Gurtina Besla, and Ekta Patel for discussion on the
construction of Figure 1. This work is made possible based on observations
obtained with the NASA/ESA Hubble Space Telescope, retrieved from the Mikulski
Archive for Space Telescopes (MAST) at the Space Telescope Science Institute
(STScI). Support for Program number HST-AR-16640 was provided by NASA through
a grant from STScI, which is operated by the Association of Universities for
Research in Astronomy, Incorporated, under NASA contract NAS5-26555. E.D.T.
was supported by the European Research Council (ERC) under grant agreement no.
101040751. Y.F. acknowledges support from NASA award 19-ATP19-0023 and NSF
award AST-2007012. Y.Z. and K.T. started collaboration on this topic during a
program “Fundamentals of Gaseous Halos” held in 2021 at Kavli Institute for
Theoretical Physics, UC Santa Barbara. This research was supported in part by
the National Science Foundation under Grant No. NSF PHY-1748958. This work was
performed in part at the Aspen Center for Physics, which is supported by
National Science Foundation grant PHY-2210452.

Table 2: Column Density and Velocity Measurements of the LMC Gas Along the
ULLYSES DR5 Sight Lines

ID | Star | RA | DEC | $x$ | $y$ | $\Sigma_{\rm SFR}$ | $v_{\rm RSG}$ | logN(H i) | $v_{\rm HI}$ | logN(S ii) |  $v_{\rm cen}$(S ii) | logN(Si iv) |  $v_{\rm cen}$(Si iv) | logN(C iv) |  $v_{\rm cen}$(C iv)
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
|  | (deg) | (deg) | (deg) | (deg) | ($\frac{M_{\odot}}{\rm yr~{}kpc^{2}}$) | ($\rm\frac{km}{s}$) | ($\rm\frac{1}{cm^{2}}$) | ($\rm\frac{km}{s}$) | ($\rm\frac{1}{cm^{2}}$) | ($\rm\frac{km}{s}$) | ($\rm\frac{1}{cm^{2}}$) | ($\rm\frac{km}{s}$) | ($\rm\frac{1}{cm^{2}}$) | ($\rm\frac{km}{s}$)
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16)
1 | SK-68D73 | 80.7491 | -68.0296 | 0.11 | 1.24 | 0.086 | 288.5 | 21.57 | 291.2 |  $>$15.77 | 294.1$\pm$25.6 |  $>$13.93 | 268.0$\pm$14.4 | unreliable | unreliable
2 | BAT99-105 | 84.6755 | -69.0987 | 1.51 | 0.12 | 1.078 | 271.7 | self-abs | self-abs |  $>$16.21 | 260.8$\pm$17.6 | unreliable | unreliable |  $>$14.93 | 228.7$\pm$13.4
3 | ST92-5-31 | 84.7985 | -69.5104 | 1.52 | -0.29 | 0.201 | 265.7 | 21.65 | 273.6 |  $>$15.83 | 268.9$\pm$5.8 | unreliable | unreliable |  $>$14.37 | 242.4$\pm$5.4
4 | SK-67D22 | 74.3644 | -67.6508 | -2.31 | 1.51 | 0.019 | 284.7 | 21.21 | 284.9 |  $>$15.46 | 283.3$\pm$13.5 | unreliable | unreliable | 13.80$\pm$0.02 | 293.1$\pm$22.7
5 | SK-66D172 | 84.2725 | -66.3597 | 1.53 | 2.86 | 0.049 | 302.5 | 21.27 | 301.9 |  $>$15.73 | 287.0$\pm$15.8 | unreliable | unreliable |  $>$14.35 | 291.0$\pm$26.9
6 | VFTS72 | 84.3936 | -69.0195 | 1.41 | 0.21 | 0.177 | 273.0 | 21.59 | 275.5 |  $>$15.78 | 277.0$\pm$8.8 | unreliable | unreliable |  $>$14.90 | 244.1$\pm$5.5
7 | BI237 | 84.0610 | -67.6553 | 1.37 | 1.58 | 0.036 | 292.7 | 21.47 | 292.0 | unreliable | unreliable | unreliable | unreliable |  $>$14.57 | 285.4$\pm$9.2
8 | SK-67D211 | 83.8079 | -67.5576 | 1.28 | 1.68 | 0.156 | 294.2 | 21.27 | 296.9 |  $>$15.68 | 288.8$\pm$24.3 | unreliable | unreliable |  $>$14.71 | 283.0$\pm$24.0
9 | VFTS-482 | 84.6679 | -69.0999 | 1.51 | 0.12 | 1.078 | 271.7 | self-abs | self-abs |  $>$16.06 | 269.6$\pm$3.5 | unreliable | unreliable | unreliable | unreliable
10 | N11-ELS-060 | 74.1756 | -66.4152 | -2.50 | 2.73 | 0.227 | 288.3 | 21.36 | 290.9 |  $>$15.73 | 264.2$\pm$5.8 | unreliable | unreliable | unreliable | unreliable
11 | ST92-5-27 | 84.8065 | -69.5014 | 1.53 | -0.28 | 0.209 | 265.8 | 21.63 | 273.2 |  $>$15.85 | 264.8$\pm$6.1 | unreliable | unreliable |  $>$14.55 | 239.6$\pm$7.6
12 | LH114-7 | 85.8042 | -67.8544 | 2.02 | 1.33 | 0.043 | 288.9 | 21.30 | 301.6 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
13 | VFTS-267 | 84.5582 | -69.1299 | 1.46 | 0.09 | 0.905 | 271.3 | 21.65 | 279.6 |  $>$15.86 | 272.0$\pm$11.7 | unreliable | unreliable |  $>$14.44 | 231.7$\pm$9.5
14 | VFTS-404 | 84.6410 | -69.1659 | 1.49 | 0.05 | 0.615 | 270.7 | 21.62 | 278.0 |  $>$16.00 | 274.5$\pm$11.0 | unreliable | unreliable |  $>$14.71 | 236.0$\pm$7.5
15 | W61-28-23 | 83.7090 | -69.7757 | 1.13 | -0.53 | 0.086 | 262.3 | 21.28 | 273.6 |  $>$15.63 | 265.8$\pm$10.5 | unreliable | unreliable |  $>$14.21 | 248.2$\pm$12.4
16 | SK-71D46 | 82.9566 | -71.0606 | 0.82 | -1.81 | 0.098 | 243.7 | 21.51 | 245.9 |  $>$15.62 | 256.8$\pm$9.4 | unreliable | unreliable | 13.96$\pm$0.02 | 229.5$\pm$11.7
17 | SK-67D166 | 82.9342 | -67.6337 | 0.95 | 1.62 | 0.043 | 293.7 | 20.82 | 294.2 | 15.13$\pm$0.01 | 261.8$\pm$7.7 | unreliable | unreliable | 13.67$\pm$0.02 | 263.7$\pm$14.1
18 | SK-67D105 | 81.5258 | -67.1824 | 0.42 | 2.09 | 0.032 | 300.6 | 21.03 | 308.4 |  $>$15.22 | 303.7$\pm$28.4 | unreliable | unreliable | 13.87$\pm$0.03 | 271.6$\pm$24.6
19 | SK-67D108 | 81.6103 | -67.6223 | 0.44 | 1.65 | 0.053 | 294.2 | 21.31 | 296.1 |  $>$15.45 | 286.8$\pm$10.6 | unreliable | unreliable |  $>$14.23 | 275.6$\pm$8.4
20 | HD38029 | 84.2299 | -69.1938 | 1.34 | 0.04 | 0.194 | 270.6 | 21.69 | 277.0 | unreliable | unreliable | 13.61$\pm$0.04 | 238.2$\pm$32.2 | unreliable | unreliable
21 | SK-67D167 | 82.9663 | -67.6615 | 0.96 | 1.59 | 0.077 | 293.3 | 21.24 | 296.8 | 15.22$\pm$0.01 | 278.2$\pm$12.3 | unreliable | unreliable |  $>$14.02 | 259.4$\pm$20.6
22 | W61-28-5 | 83.6186 | -69.7325 | 1.10 | -0.49 | 0.073 | 262.9 | 21.03 | 267.4 | unreliable | unreliable | unreliable | unreliable |  $>$14.46 | 253.4$\pm$7.6
23 | FARINA-88 | 85.0343 | -69.6548 | 1.59 | -0.44 | 0.220 | 263.4 | 21.68 | 265.0 |  $>$15.98 | 258.6$\pm$5.0 | unreliable | unreliable |  $>$14.46 | 234.6$\pm$5.1
24 | LMCE055-1 | 74.2034 | -69.6113 | -2.17 | -0.45 | 0.023 | 262.9 | 20.94 | 268.2 | 15.51$\pm$0.02 | 233.4$\pm$14.4 |  $>$14.01 | 234.6$\pm$10.5 |  $>$14.40 | 228.9$\pm$7.1
25 | SK-70D60 | 76.1699 | -70.2596 | -1.44 | -1.04 | 0.022 | 255.4 | 20.39 | 242.3 | 14.77$\pm$0.03 | 239.6$\pm$26.8 | unreliable | unreliable | 13.82$\pm$0.03 | 216.6$\pm$25.4
26 | SK-65D47 | 80.2280 | -65.4550 | -0.09 | 3.81 | 0.019 | 304.5 | self-abs | self-abs | 15.38$\pm$0.02 | 261.3$\pm$14.0 | unreliable | unreliable | 14.02$\pm$0.02 | 268.2$\pm$17.6
27 | SK-67D69 | 78.5837 | -67.1342 | -0.72 | 2.13 | 0.144 | 301.5 | 21.25 | 303.2 |  $>$15.72 | 289.9$\pm$14.8 | unreliable | unreliable |  $>$14.23 | 270.0$\pm$11.4
28 | VFTS352 | 84.6186 | -69.1886 | 1.48 | 0.03 | 0.463 | 270.4 | 21.70 | 277.7 |  $>$15.78 | 275.5$\pm$5.0 | unreliable | unreliable | unreliable | unreliable
29 | ST92-4-18 | 84.9612 | -69.4076 | 1.59 | -0.19 | 0.134 | 267.1 | 21.57 | 272.6 |  $>$15.89 | 274.9$\pm$6.5 | unreliable | unreliable | 13.79$\pm$0.02 | 221.9$\pm$13.7
30 | N11-ELS-038 | 74.1884 | -66.4197 | -2.50 | 2.72 | 0.227 | 288.3 | 21.34 | 286.5 |  $>$15.55 | 287.3$\pm$12.7 | unreliable | unreliable |  $>$14.16 | 269.4$\pm$13.5
31 | PGMW3120 | 74.1951 | -66.4130 | -2.50 | 2.73 | 0.227 | 288.4 | 21.39 | 290.4 |  $>$15.86 | 279.7$\pm$28.0 | unreliable | unreliable |  $>$14.52 | 274.1$\pm$23.9
32 | LMCE078-1 | 84.3734 | -69.2478 | 1.39 | -0.02 | 0.167 | 269.7 | 21.67 | 277.2 |  $>$15.81 | 283.1$\pm$11.9 | unreliable | unreliable |  $>$14.28 | 256.1$\pm$12.5
33 | SK-65D22 | 75.3462 | -65.8759 | -2.08 | 3.31 | 0.028 | 293.5 | 21.04 | 294.9 |  $>$15.72 | 278.3$\pm$18.6 |  $>$13.66 | 274.1$\pm$16.0 | 13.95$\pm$0.02 | 273.0$\pm$22.4
34 | SK-71D19 | 80.5656 | -71.3609 | 0.04 | -2.09 | 0.020 | 239.7 | 20.96 | 242.3 |  $>$15.51 | 240.5$\pm$14.8 | unreliable | unreliable | 14.26$\pm$0.02 | 250.2$\pm$14.3
35 | SK-69D104 | 79.7479 | -69.2152 | -0.25 | 0.06 | 0.132 | 271.2 | 21.19 | 276.7 | 15.10$\pm$0.01 | 260.6$\pm$11.9 | unreliable | unreliable | 13.62$\pm$0.05 | 215.5$\pm$36.0
36 | VFTS440 | 84.6572 | -69.0892 | 1.50 | 0.13 | 1.067 | 271.9 | self-abs | self-abs |  $>$15.90 | 274.0$\pm$44.2 | unreliable | unreliable |  $>$14.64 | 224.0$\pm$27.7
37 | N11-ELS-018 | 74.1710 | -66.4113 | -2.50 | 2.73 | 0.211 | 288.3 | 21.43 | 292.2 |  $>$15.62 | 275.5$\pm$9.9 | unreliable | unreliable |  $>$14.12 | 264.3$\pm$11.9
38 | UCAC3-42-30814 | 83.9660 | -69.3886 | 1.24 | -0.15 | 0.083 | 267.8 | 21.08 | 269.1 |  $>$15.64 | 271.7$\pm$10.7 | unreliable | unreliable |  $>$14.46 | 254.6$\pm$4.7
39 | SK-67D111 | 81.7003 | -67.4916 | 0.48 | 1.78 | 0.093 | 296.1 | 21.19 | 301.7 |  $>$15.47 | 293.0$\pm$14.4 | 13.50$\pm$0.03 | 256.6$\pm$22.4 | 13.77$\pm$0.03 | 260.6$\pm$28.0
40 | SK-71D50 | 85.1799 | -71.4835 | 1.50 | -2.27 | 0.019 | 243.4 | 21.45 | 254.7 |  $>$15.90 | 264.2$\pm$20.6 | 13.48$\pm$0.04 | 233.0$\pm$32.4 | 13.56$\pm$0.08 | 249.5$\pm$69.2
41 | SK-70D115 | 87.2069 | -70.0661 | 2.30 | -0.92 | 0.090 | 259.7 | 21.55 | 257.0 |  $>$15.90 | 277.6$\pm$8.4 |  $>$14.20 | 239.2$\pm$19.0 |  $>$14.40 | 238.4$\pm$17.3
42 | BI214 | 83.5258 | -69.4193 | 1.08 | -0.17 | 0.047 | 267.6 | 21.07 | 272.4 |  $>$15.80 | 267.7$\pm$19.6 | unreliable | unreliable |  $>$14.48 | 242.9$\pm$7.9
43 | SK-66D19 | 73.9748 | -66.4165 | -2.58 | 2.72 | 0.067 | 287.5 | 21.50 | 289.0 |  $>$15.39 | 256.3$\pm$31.3 | 13.77$\pm$0.03 | 274.8$\pm$27.3 | 14.00$\pm$0.04 | 256.8$\pm$33.4
44 | BI272 | 86.0963 | -67.2414 | 2.18 | 1.93 | 0.024 | 295.4 | 20.73 | 310.0 |  $>$15.55 | 287.5$\pm$10.3 | unreliable | unreliable | unreliable | unreliable
45 | SK-69D50 | 74.3129 | -69.3389 | -2.16 | -0.18 | 0.024 | 266.9 | 20.79 | 271.5 |  $>$15.48 | 243.0$\pm$20.3 |  $>$13.64 | 249.0$\pm$21.7 | 13.90$\pm$0.04 | 249.4$\pm$35.0
46 | SK-68D16 | 74.4075 | -68.4100 | -2.22 | 0.75 | 0.080 | 278.8 | 20.85 | 284.7 |  $>$15.47 | 267.6$\pm$10.0 | unreliable | unreliable | 13.94$\pm$0.02 | 235.4$\pm$18.4
47 | SK-67D118 | 81.8888 | -67.2918 | 0.56 | 1.97 | 0.022 | 299.0 | 20.96 | 318.1 | 15.21$\pm$0.02 | 269.5$\pm$15.7 | unreliable | unreliable | 14.03$\pm$0.03 | 272.9$\pm$24.1
48 | UCAC3-42-33014 | 85.0568 | -69.4264 | 1.62 | -0.22 | 0.117 | 266.8 | 21.35 | 277.0 |  $>$15.79 | 273.7$\pm$9.2 |  $>$14.42 | 221.4$\pm$6.6 |  $>$14.81 | 218.9$\pm$4.8
49 | SK-68D112 | 82.7835 | -68.6151 | 0.85 | 0.64 | 0.041 | 279.5 | 21.46 | 276.4 | 15.42$\pm$0.01 | 270.3$\pm$11.3 |  $>$13.69 | 233.8$\pm$8.8 | 13.66$\pm$0.03 | 223.8$\pm$26.6
50 | SK-67D191 | 83.3918 | -67.5055 | 1.13 | 1.74 | 0.044 | 295.3 | 21.39 | 297.1 |  $>$15.60 | 285.9$\pm$14.1 | unreliable | unreliable |  $>$14.52 | 276.6$\pm$19.2
51 | SK-68D155 | 85.7289 | -68.9485 | 1.90 | 0.24 | 0.039 | 273.4 | 21.76 | 280.5 |  $>$15.86 | 288.5$\pm$12.5 | 13.85$\pm$0.02 | 241.5$\pm$13.0 | unreliable | unreliable
52 | N11-ELS-013 | 74.2536 | -66.4070 | -2.47 | 2.74 | 0.238 | 288.6 | 21.59 | 295.2 |  $>$15.83 | 274.7$\pm$29.0 | unreliable | unreliable |  $>$14.42 | 275.5$\pm$32.5
53 | BI173 | 81.7915 | -69.1323 | 0.48 | 0.13 | 0.036 | 272.2 | 21.08 | 249.8 |  $>$15.75 | 235.0$\pm$11.0 | unreliable | unreliable | 13.85$\pm$0.04 | 224.4$\pm$29.5
54 | SK-67D101 | 81.4844 | -67.5080 | 0.40 | 1.76 | 0.098 | 295.9 | 21.26 | 301.6 | 15.36$\pm$0.02 | 293.3$\pm$19.1 | unreliable | unreliable | 14.03$\pm$0.02 | 263.9$\pm$19.5
55 | SK-67D168 | 82.9672 | -67.5724 | 0.96 | 1.68 | 0.026 | 294.6 |  $<$20.4 | N/A | 15.17$\pm$0.01 | 257.2$\pm$12.4 | 13.35$\pm$0.02 | 249.4$\pm$15.5 | 13.52$\pm$0.06 | 246.2$\pm$44.8
56 | LMCX-4 | 83.2065 | -66.3703 | 1.11 | 2.88 | 0.028 | 302.9 |  $<$20.4 | N/A | 14.71$\pm$0.03 | 264.2$\pm$25.7 | unreliable | unreliable | unreliable | unreliable
57 | BI184 | 82.6278 | -71.0421 | 0.71 | -1.78 | 0.081 | 244.0 | 20.94 | 262.0 |  $>$15.61 | 261.5$\pm$6.0 | unreliable | unreliable | unreliable | unreliable
58 | LH9-34 | 74.1887 | -66.4936 | -2.49 | 2.65 | 0.111 | 288.2 | 21.40 | 278.3 |  $>$15.95 | 269.2$\pm$23.8 | 13.45$\pm$0.01 | 250.3$\pm$10.5 | 13.88$\pm$0.03 | 245.8$\pm$25.9
59 | SK-71D8 | 76.8469 | -71.1983 | -1.16 | -1.96 | 0.022 | 243.8 | 20.87 | 228.9 |  $>$15.89 | 223.2$\pm$18.6 | unreliable | unreliable | 14.23$\pm$0.02 | 232.0$\pm$16.7
60 | VFTS-66 | 84.3879 | -69.0762 | 1.41 | 0.15 | 0.431 | 272.2 | 21.72 | 275.3 |  $>$15.83 | 273.4$\pm$13.1 | unreliable | unreliable | unreliable | unreliable
61 | SK-66D171 | 84.2601 | -66.6436 | 1.51 | 2.58 | 0.032 | 300.9 | 21.16 | 315.8 |  $>$15.57 | 267.3$\pm$12.7 | 13.33$\pm$0.02 | 254.8$\pm$17.1 | 13.72$\pm$0.05 | 255.0$\pm$41.3
62 | SK-69D279 | 85.4361 | -69.5875 | 1.74 | -0.39 | 0.048 | 264.2 | 21.57 | 270.7 |  $>$15.77 | 267.3$\pm$14.3 | unreliable | unreliable | unreliable | unreliable
63 | SK-70D32 | 75.0425 | -70.1860 | -1.83 | -0.99 | 0.029 | 256.0 | 20.96 | 246.4 | 15.17$\pm$0.02 | 226.6$\pm$18.8 | unreliable | unreliable | 13.65$\pm$0.05 | 203.0$\pm$41.0
64 | SK-66D17 | 73.9812 | -66.4724 | -2.57 | 2.66 | 0.060 | 287.4 | 21.30 | 280.6 |  $>$15.64 | 271.2$\pm$10.3 | unreliable | unreliable | N/A | N/A
65 | SK-68D135 | 84.4548 | -68.9171 | 1.44 | 0.31 | 0.074 | 274.5 | 21.54 | 278.3 |  $>$15.89 | 274.4$\pm$17.9 |  $>$14.36 | 255.3$\pm$16.6 | unreliable | unreliable
66 | VFTS87 | 84.4027 | -69.1255 | 1.41 | 0.10 | 0.484 | 271.5 | 21.77 | 277.0 |  $>$15.79 | 271.8$\pm$44.9 | unreliable | unreliable |  $>$14.30 | 248.9$\pm$38.2
67 | SK-71D41 | 82.6673 | -71.0936 | 0.72 | -1.83 | 0.090 | 243.2 | 21.39 | 246.9 |  $>$15.82 | 247.9$\pm$23.5 |  $>$14.21 | 235.6$\pm$19.6 |  $>$14.60 | 233.6$\pm$20.7
68 | SK-67D5 | 72.5789 | -67.6606 | -2.98 | 1.42 | 0.023 | 276.4 | 21.20 | 280.0 |  $>$15.56 | 272.6$\pm$12.4 | 13.25$\pm$0.03 | 256.5$\pm$27.0 | 13.56$\pm$0.05 | 239.2$\pm$43.7
69 | SK-69D220 | 84.1820 | -69.4965 | 1.31 | -0.26 | 0.074 | 266.2 | 21.35 | 285.5 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
70 | SK-68D52 | 76.8351 | -68.5357 | -1.32 | 0.70 | 0.029 | 280.8 | 21.23 | 254.1 |  $>$15.75 | 264.2$\pm$14.0 | 13.71$\pm$0.02 | 240.0$\pm$15.2 | 14.20$\pm$0.02 | 242.6$\pm$13.9
71 | SK-67D107 | 81.5862 | -67.4988 | 0.44 | 1.77 | 0.098 | 296.1 | 21.24 | 305.0 | 15.38$\pm$0.02 | 283.7$\pm$16.7 |  $>$14.20 | 284.2$\pm$21.0 |  $>$14.31 | 280.5$\pm$16.2
72 | SK-67D106 | 81.5634 | -67.5000 | 0.43 | 1.77 | 0.101 | 296.1 | 21.22 | 306.4 | 15.36$\pm$0.02 | 295.0$\pm$18.6 |  $>$14.16 | 274.4$\pm$23.1 |  $>$14.22 | 268.9$\pm$16.6
73 | HV5622 | 77.3717 | -68.9174 | -1.10 | 0.33 | 0.096 | 275.4 | 21.30 | 266.6 |  $>$15.68 | 258.9$\pm$8.6 | unreliable | unreliable | unreliable | unreliable
74 | N11-ELS-033 | 74.0459 | -66.4734 | -2.55 | 2.66 | 0.090 | 287.6 | 21.24 | 279.6 |  $>$15.65 | 271.3$\pm$6.7 | 13.62$\pm$0.01 | 245.8$\pm$12.1 | unreliable | unreliable
75 | SK-70D79 | 76.6553 | -70.4901 | -1.26 | -1.26 | 0.029 | 252.1 | 21.11 | 241.4 |  $>$15.62 | 226.8$\pm$19.5 |  $>$13.58 | 209.8$\pm$19.4 |  $>$14.09 | 211.9$\pm$18.5
76 | SK-69D43 | 74.0436 | -69.2606 | -2.26 | -0.11 | 0.021 | 267.1 | 20.74 | 264.1 | 15.22$\pm$0.02 | 234.6$\pm$15.8 | 13.52$\pm$0.01 | 253.2$\pm$10.7 | 13.70$\pm$0.04 | 243.1$\pm$30.2
77 | SK-68D41 | 76.3630 | -68.1674 | -1.52 | 1.05 | 0.028 | 286.0 | 21.33 | 267.6 | unreliable | unreliable | 13.59$\pm$0.02 | 257.2$\pm$12.5 | 13.94$\pm$0.03 | 249.2$\pm$25.5
78 | SK-68D140 | 84.7382 | -68.9481 | 1.54 | 0.27 | 0.143 | 273.9 | 21.68 | 275.4 |  $>$15.73 | 277.2$\pm$18.4 |  $>$14.33 | 251.0$\pm$14.5 |  $>$14.58 | 250.6$\pm$3.4
79 | SK-67D2 | 71.7686 | -67.1148 | -3.36 | 1.92 | 0.014 | 275.5 | 21.29 | 273.2 |  $>$15.72 | 272.6$\pm$20.8 | 13.67$\pm$0.02 | 254.5$\pm$14.2 | unreliable | unreliable
80 | SK-68D23A | 75.2012 | -68.0996 | -1.95 | 1.09 | 0.019 | 285.5 | 21.19 | 267.3 | 15.45$\pm$0.01 | 259.3$\pm$9.9 | unreliable | unreliable | 14.16$\pm$0.02 | 238.1$\pm$14.2
81 | SK-66D35 | 74.2685 | -66.5774 | -2.45 | 2.57 | 0.070 | 288.3 | 21.51 | 287.7 |  $>$15.79 | 261.7$\pm$16.1 | 13.57$\pm$0.02 | 258.4$\pm$14.4 | unreliable | unreliable
82 | SK-68D129 | 84.1116 | -68.9589 | 1.32 | 0.27 | 0.068 | 274.1 | 21.54 | 278.4 |  $>$15.61 | 266.4$\pm$13.5 |  $>$14.02 | 251.2$\pm$9.9 |  $>$14.44 | 245.0$\pm$4.0
83 | N206-FS-170 | 82.7622 | -70.8323 | 0.76 | -1.57 | 0.024 | 247.1 | 21.16 | 248.4 | 15.33$\pm$0.03 | 254.1$\pm$22.3 | unreliable | unreliable | unreliable | unreliable
84 | SK-71D35 | 82.5174 | -71.1323 | 0.67 | -1.87 | 0.051 | 242.7 | 21.29 | 245.7 |  $>$15.55 | 247.8$\pm$10.0 | unreliable | unreliable | unreliable | unreliable
85 | NGC1818-ROB-D1 | 76.1346 | -66.4132 | -1.72 | 2.80 | 0.017 | 295.6 | 21.27 | 302.4 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
86 | SK-67D14 | 73.6329 | -67.2568 | -2.63 | 1.87 | 0.039 | 283.3 | 21.24 | 291.1 |  $>$15.42 | 255.3$\pm$14.8 | 13.35$\pm$0.05 | 276.8$\pm$46.2 |  $>$14.08 | 262.3$\pm$17.5
87 | SK-69D52 | 74.4538 | -69.8729 | -2.06 | -0.70 | 0.017 | 259.6 | 20.56 | 258.8 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
88 | SK-68D26 | 75.3844 | -68.1786 | -1.88 | 1.02 | 0.021 | 285.5 | 21.24 | 269.2 |  $>$15.79 | 267.7$\pm$11.4 | unreliable | unreliable | unreliable | unreliable
89 | NGC2004-ELS-26 | 82.6515 | -67.2952 | 0.85 | 1.96 | 0.025 | 298.4 | 20.98 | 308.7 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
90 | SK-70D50 | 75.9412 | -70.1993 | -1.52 | -0.98 | 0.022 | 256.2 | 20.64 | 245.7 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
91 | SK-67D78 | 80.0795 | -67.3016 | -0.14 | 1.97 | 0.016 | 299.1 | 21.00 | 297.8 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
92 | SK-69D140 | 81.9141 | -69.2116 | 0.52 | 0.05 | 0.034 | 271.1 | 20.73 | 267.6 | 15.35$\pm$0.02 | 224.2$\pm$15.8 | 13.33$\pm$0.03 | 249.4$\pm$27.1 | unreliable | unreliable
93 | SK-70D16 | 73.7390 | -70.0412 | -2.28 | -0.89 | 0.020 | 256.2 | 20.51 | 244.5 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
94 | SK-68D8 | 73.4304 | -68.7148 | -2.54 | 0.41 | 0.019 | 271.5 | 20.88 | 266.6 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
95 | NGC2004-ELS-3 | 82.6684 | -67.2691 | 0.86 | 1.99 | 0.025 | 298.5 | 21.09 | 307.8 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
96 | SK-67D195 | 83.4664 | -67.1339 | 1.17 | 2.11 | 0.018 | 298.2 | 20.47 | 292.3 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
97 | SK-67D197 | 83.4961 | -67.5377 | 1.17 | 1.70 | 0.038 | 294.8 | 21.24 | 299.1 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
98 | SK-66D50 | 75.7868 | -66.9597 | -1.82 | 2.24 | 0.022 | 293.6 | 21.16 | 286.3 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
99 | SK-67D207 | 83.7311 | -67.3519 | 1.27 | 1.89 | 0.027 | 296.2 | 20.74 | 302.8 | unreliable | unreliable | unreliable | unreliable | unreliable | unreliable
100 | SK-67D20 | 73.8806 | -67.5007 | -2.51 | 1.64 | 0.016 | 283.2 | 20.70 | 283.5 | 15.09$\pm$0.05 | 262.7$\pm$41.5 |  $>$13.79 | 274.5$\pm$20.2 |  $>$14.41 | 269.2$\pm$25.0
101 | SK-68D15 | 74.3504 | -68.3992 | -2.24 | 0.76 | 0.082 | 278.7 | 21.01 | 285.8 | 15.43$\pm$0.02 | 267.4$\pm$14.1 |  $>$14.00 | 241.1$\pm$16.9 | 13.98$\pm$0.01 | 230.7$\pm$10.4
102 | SK-66D51 | 75.7871 | -66.6826 | -1.84 | 2.52 | 0.021 | 294.1 | 20.46 | 299.8 |  $>$15.27 | 292.8$\pm$25.9 |  $>$13.69 | 267.5$\pm$14.2 | unreliable | unreliable
103 | SK-65D55 | 80.4904 | -65.8167 | 0.02 | 3.45 | 0.021 | 304.3 |  $<$20.4 | N/A | 14.93$\pm$0.03 | 253.4$\pm$23.8 |  $>$13.54 | 272.7$\pm$18.6 |  $>$14.10 | 271.6$\pm$14.2
104 | SK-71D21 | 80.5939 | -71.5995 | 0.05 | -2.33 | 0.034 | 238.3 | 21.18 | 247.0 |  $>$15.46 | 255.3$\pm$16.2 | 13.39$\pm$0.04 | 229.7$\pm$35.0 | 13.72$\pm$0.04 | 238.9$\pm$33.3
105 | SK-67D104 | 81.5165 | -67.4992 | 0.41 | 1.77 | 0.102 | 296.1 | 21.18 | 302.4 | 15.26$\pm$0.02 | 286.9$\pm$21.3 |  $>$14.07 | 283.6$\pm$10.4 |  $>$14.10 | 270.5$\pm$11.1
106 | SK-69D175 | 82.8563 | -69.0940 | 0.86 | 0.16 | 0.048 | 272.6 | 21.05 | 275.3 |  $>$15.57 | 268.2$\pm$14.5 | 13.23$\pm$0.04 | 225.1$\pm$32.3 | unreliable | unreliable
107 | SK-69D191 | 83.5802 | -69.7529 | 1.09 | -0.51 | 0.075 | 262.6 | 21.19 | 261.4 |  $>$15.84 | 255.6$\pm$16.2 |  $>$13.97 | 253.1$\pm$9.2 |  $>$14.36 | 249.1$\pm$5.3
108 | SK-69D246 | 84.7224 | -69.0336 | 1.53 | 0.18 | 0.728 | 272.7 | 21.65 | 275.6 |  $>$15.92 | 266.3$\pm$20.2 |  $>$14.16 | 229.7$\pm$16.3 |  $>$14.42 | 225.4$\pm$9.4
109 | HD269927C | 84.7421 | -69.4888 | 1.50 | -0.27 | 0.198 | 266.0 | 21.61 | 276.6 |  $>$15.96 | 270.2$\pm$19.7 |  $>$14.25 | 218.8$\pm$15.4 | unreliable | unreliable
110 | SK-67D266 | 86.4664 | -67.2405 | 2.33 | 1.92 | 0.023 | 295.4 | self-abs | self-abs | unreliable | unreliable |  $>$13.49 | 284.7$\pm$41.7 | unreliable | unreliable

††footnotetext: Note:
Cols (1)–(4): IDs, star names, right ascensions (RA) and declinations (DEC) of
the targets from the ULLYSES program (DR5).
Cols (5)–(6): $x$ and $y$ coordiantes of each star in the plane of the LMC
based on the orthographic projection method outlined in Choi et al. (2022).
Col (7): Star formation rate surface density measured toward each sight line
based on an H$\alpha$ emission map from Gaustad et al. (2001); see Section 3.1
for further details.
Col (8): Heliocentric velocities of the LMC’s young stellar disk at the
locations of the corresponding sight lines, measured based on the kinematic
model of a population of young supergiant stars (RSGs); see Section 3.3 for
further details.
Cols (9)–(10): Column densities and flux-weighted centroid velocities of the H
i gas, measured based on an H i 21cm data cube from Kim et al. (2003). We find
H i self absorption in five sight lines, BAT99-105 (ID 2), VFTS-482 (ID 9),
SK-65D47 (ID 26), VFTS440 (ID 36), and SK-67D266 (ID 110), which we note as
“self-abs” in the corresponding rows. Additionally, we do not find significant
H i detection along three sight lines, SK-67D168 (ID 55), LMCX-4 (ID 56), and
SK-65D55 (ID 103), for which we indicate $3\sigma$ upper limits in $\log$ N(H
i) based on the data cube’s sensitivity level; see Section 3.2 for further
details.
Cols (11)–(16): Column densities and flux-weighted centroid velocities of S
ii, Si iv, and C iv integrated from $v_{\rm helio}=175$ to 375 $\rm
km~{}s^{-1}$ based on the AOD method (see Section 2.4 for details). A
measurement is labelled as “unreliable” when our algorithm does not find a
reliable continuum placement over the MW+LMC interstellar absorption velocity
range; see Section 2.5 for further details. For $\log N$ values, we always use
the weaker lines (S ii 1250, Si iv 1402, C iv 1550) when the stronger lines
are saturated. The only exception is Si iv measured toward SK-68D73, which
only has STIS/E140H coverage extending up to Si iv 1393 Å but not 1402 Å; in
this case, we use the $\log N$ measurement from Si iv 1393Å for this star.
When both lines of the doublet are non-saturated, we take the mean $\log N$
values of the doublet lines and the $\log N$ errors are combined in
quadrature.

Note. — [1]: The stellar rotation is fitted for a population of young red
supergiants and evolved old red giant branch and asymptotic giant branch
stars. [2]: The LMC’s kinematic center is derived based on $\sim$10,000 red-
giant branch stars, asymptotic giant branch stars, and red supergiant stars.

##  Appendix A The Foreground High-Velocity Cloud at $v\sim 90-175~{}\rm
km~{}s^{-1}$

Section 6.4 shows that HVC90-175’s absorption can be constrained to be at
$v_{\rm helio}\lesssim 175~{}\rm km~{}s^{-1}$ and it is located at
$d_{\odot}<13.3$ kpc (Richter et al., 2015; Werner & Rauch, 2015). The cloud
is observed in H i (de Boer et al., 1990; Staveley-Smith et al., 2003),
H$\alpha$ emission (Ciampa et al., 2021), molecular hydrogen (Richter et al.,
1999; Tchernyshyov, 2022), and UV absorption tracing ionized gas (e.g. Savage
& de Boer, 1981; Lehner & Howk, 2007; Lehner et al., 2009; Barger et al.,
2016; Roman-Duval et al., 2019a). Here we further discuss HVC90-175 based on
UV measurements from the literature and newly obtained in this work. We show
that the kinematics of HVC90-175 is consistent with being a MW halo cloud at
$v_{\rm LSR}\sim 120~{}\rm km~{}s^{-1}$ with a temperature of $T\sim 10^{4.2}$
K and a non-thermal broadening of $\sigma_{\rm nth}\sim 10~{}\rm km~{}s^{-1}$.
As noted in Section 1, in the general direction of the LMC, the $v_{\rm
helio}$ and $v_{\rm LSR}$ velocities are offset by $v_{\rm helio}-v_{\rm
LSR}\sim 10~{}\rm km~{}s^{-1}$.

Figure 13: Multiphase ion velocities of HVC90-175 as a function of R.A. in the
Local Standard of Rest (LSR; left), Galactic Standard of Red (GSR; middle),
and LMC Standard of Rest (LMCSR; right) frames (see Equation A1). HVC90-175 is
detected in low ions such as O i (yellow circles; Lehner et al. 2009, see also
Roman-Duval et al. 2019a) as well as in S ii, Si iv, and C iv (this work).
HVC90-175’s gas kinematics is consistent with being a MW halo cloud at $v_{\rm
LSR}=120~{}\rm km~{}s^{-1}$ with a temperature of $T=10^{4.2}$ K and a non-
thermal broadening of $\sigma_{\rm nth}\sim 10~{}\rm km~{}s^{-1}$. The
distance to HVC90-175 is constrained to be $d_{\odot}<13.3$ kpc (Richter et
al., 2015; Werner & Rauch, 2015).  Figure 14: 2D velocity distributions of
HVC90-175 in the same orthographic projection as in Figure 11. In the left
panel, we find that the O i gas, as measured by Lehner et al. (2009), is
moving faster at $v_{\rm LSR}\sim 150-175~{}\rm km~{}s^{-1}$ toward the west.
Meanwhile, S ii (middle), Si iv (not shown), and C iv (right) show nearly
constant $v_{\rm LSR}$ at $\sim 120~{}\rm km~{}s^{-1}$ across the surface of
the cloud.

In Figure 13, we show HVC90-175’s velocity distributions in O i, S ii, Si iv,
and C iv as a function of R.A.. The S ii, Si iv, and C iv are flux-weighted
centroid velocities integrated over a velocity range of $v_{\rm
helio}=90-175~{}\rm km~{}s^{-1}$ using the same ULLYSES sight lines as
discussed in Section 2.4; here we prioritize using the stronger lines of S ii
1253, Si iv 1393, and C iv 1548 unless the absorption is saturated. The O i
data points are adopted from Lehner et al. (2009), who studied HVC90-175
toward 139 FUSE sight lines in the direction of the LMC.

The ion velocities are shown in three reference frames: the Local Standard of
Rest (LSR; left panel), the Galactic Standard of Rest (GSR; middle panel), and
the LMC Standard of Rest (LMCSR; right panel). The velocity conversions among
these frames are based on the equations given by Lehner et al. (2009) in their
section 3.2:

$\begin{split}v_{\rm LMCSR}&=v_{\rm GSR}+86{\rm cos\ell}{\rm cos}b+268{\rm
sin\ell}{\rm cos}b-252{\rm sin}b\\\ v_{\rm GSR}&=v_{\rm LSR}+220{\rm
sin}\ell{\rm cos}b~{}~{}~{}.\\\ \end{split}$ (A1)

We note that the O i distribution in the right panel (VLMCSR) reproduces
Lehner et al.’s figure 5, in which they first noted that HVC90-175 shows an
apparent velocity gradient with R.A. in the LMCSR frame (see also Roman-Duval
et al. 2019a).

When we examine the velocity distributions with R.A. in all three reference
frames, we find that HVC90-175’s velocity gradient can only be mildly observed
in the LSR (left panel) and the GSR frames (middle panel). In Figure 14, we
show the 2D $v_{\rm LSR}$ distribution of the HVC90-175’s gas across the
surface of the LMC in the same orthographic projection as in Figure 11. For S
ii and C iv there is no apparent trend in $v_{\rm LSR}$; while in O i, the
southwest half (bottom right corner in the left panel) is moving faster, which
is in the opposition direction of the LMC’s rotation.

To better understand HVC90-175’s ion velocities, we model a hypothetical MW
halo cloud lying in front of the LMC at a constant velocity of $v_{\rm
LSR}=120~{}\rm km~{}s^{-1}$ and show the modelled velocity distributions as
gray contours in Figure 13. The velocities of the modeled halo cloud are
calculated for the same ULLYSES sight lines used in this work, and the
contours are generated using the kdeplot function from the seaborn package.

The spreads in the velocities are calculated by assuming a temperature of
$T=10^{4.2}$ K for our S ii measurements and a non-thermal broadening of
$\sigma_{\rm nth}=10~{}\rm km~{}s^{-1}$, which corresponds to a total velocity
dispersion of $\sigma_{v}\equiv\sqrt{(k_{\rm B}T/m_{\rm S})+\sigma_{\rm
nth}^{2}}=10.2~{}\rm km~{}s^{-1}$. The choice of $\sigma_{\rm nth}=10~{}\rm
km~{}s^{-1}$ is based on what is typically measured for non-thermal broadening
in the ionized CGM gas (Chen et al., 2023). We note that the match between the
modeled and the observed data in Figure 13 would be further improved if we
assumed a higher temperature ($T\gtrsim 10^{4.2}$ K), adopted measurements
from the lighter ions such as O i, Si iv or C iv, or more contribution from
non-thermal broadening.

The right panel of Figure 13 shows that the modeled halo cloud, which has a
constant $v_{\rm LSR}=120~{}\rm km~{}s^{-1}$, exhibits a similar velocity
gradient with R.A. as the observed ion data. We thus demonstrate that the
velocity gradient with R.A. in HVC90-175 is mainly caused by velocity
transformation between rest frames. The kinematic signature of HVC90-175 is
consistent with that of a foreground cloud moving at $v_{\rm LSR}\sim
120~{}\rm km~{}s^{-1}$ with $T\sim 10^{4.2}$ K and a non-thermal broadening of
$\sigma_{\rm nth}\sim 10~{}\rm km~{}s^{-1}$, which are physical properties
commonly found in known high-velocity clouds in the MW and ionized CGM gas in
low-redshift galaxies (Wakker, 2001; Putman et al., 2012; Chen et al., 2023).

Additionally, previous studies have measured the metallicity of HVC90-175 to
be at [O i/H i] $=-0.51^{+0.12}_{-0.16}$ dex (or $Z=10^{-0.51}\sim
0.3~{}Z_{\odot}$; Lehner et al. 2009) and an ion ratio of $N_{\rm
OVI}$/$N_{\rm CIV}\sim 1-10$ (Lehner & Howk, 2007). HVC90-175’s metallicity is
consistent with those of high-velocity clouds in the MW such as Complex C
($Z\sim 0.1-0.3~{}\rm Z_{\odot}$; Shull et al. 2011), and its ion ratio is
also consistent with those measured in the MW’s ionized gas in various
directions ($N_{\rm OVI}$/$N_{\rm CIV}\sim 1-7$; Sembach et al. 2003, see
their table 11).

Lastly, we note that there is a small sample of O i absorbers at R.A.$\sim 5$
hour or $\sim 75\degree$ that cannot be accounted for by our MW halo cloud
model in Figure 13. The O i gas at this R.A. is moving at $v_{\rm LSR}\sim
150-175~{}\rm km~{}s^{-1}$ and located at the west half of the LMC disk, which
are shown as pink-purple pixels in Figure 14. Coincidently, the LMC’s neutral
and ionized gas in this area is also moving at similar velocity of $v_{\rm
LSR}\sim 200~{}\rm km~{}s^{-1}$, as shown in Figure 11. It is possible that
the excess O i absorbers with $v_{\rm LSR}\sim 150-175~{}\rm km~{}s^{-1}$ near
R.A.$\sim$5 hour are either an extension of the LMC’s gas with low $v_{\rm
LSR}$ or blended with the LMC at similar velocities.

## References

  * Ambikasaran et al. (2015) Ambikasaran, S., Foreman-Mackey, D., Greengard, L., Hogg, D. W., & O’Neil, M. 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 252, doi: 10.1109/TPAMI.2015.2448083
  * Ambrocio-Cruz et al. (2016) Ambrocio-Cruz, P., Le Coarer, E., Rosado, M., et al. 2016, MNRAS, 457, 2048, doi: 10.1093/mnras/stw054
  * Andersson et al. (2023) Andersson, E. P., Agertz, O., Renaud, F., & Teyssier, R. 2023, MNRAS, 521, 2196, doi: 10.1093/mnras/stad692
  * Asplund et al. (2009) Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481, doi: 10.1146/annurev.astro.46.060407.145222
  * Barger et al. (2016) Barger, K. A., Lehner, N., & Howk, J. C. 2016, ApJ, 817, 91, doi: 10.3847/0004-637X/817/2/91
  * Besla et al. (2007) Besla, G., Kallivayalil, N., Hernquist, L., et al. 2007, ApJ, 668, 949, doi: 10.1086/521385
  * Book et al. (2008) Book, L. G., Chu, Y.-H., & Gruendl, R. A. 2008, ApJS, 175, 165, doi: 10.1086/523897
  * Brüns et al. (2005) Brüns, C., Kerp, J., Staveley-Smith, L., et al. 2005, A&A, 432, 45, doi: 10.1051/0004-6361:20040321
  * Chen et al. (2023) Chen, H.-W., Qu, Z., Rauch, M., et al. 2023, ApJ, 955, L25, doi: 10.3847/2041-8213/acf85b
  * Chen et al. (2010) Chen, Y.-M., Tremonti, C. A., Heckman, T. M., et al. 2010, AJ, 140, 445, doi: 10.1088/0004-6256/140/2/445
  * Chisholm et al. (2016) Chisholm, J., Tremonti, C. A., Leitherer, C., Chen, Y., & Wofford, A. 2016, MNRAS, 457, 3133, doi: 10.1093/mnras/stw178
  * Chisholm et al. (2015) Chisholm, J., Tremonti, C. A., Leitherer, C., et al. 2015, ApJ, 811, 149, doi: 10.1088/0004-637X/811/2/149
  * Choi et al. (2022) Choi, Y., Olsen, K. A. G., Besla, G., et al. 2022, ApJ, 927, 153, doi: 10.3847/1538-4357/ac4e90
  * Ciampa et al. (2021) Ciampa, D. A., Barger, K. A., Lehner, N., et al. 2021, ApJ, 908, 62, doi: 10.3847/1538-4357/abd320
  * Danforth et al. (2002) Danforth, C. W., Howk, J. C., Fullerton, A. W., Blair, W. P., & Sembach, K. R. 2002, ApJS, 139, 81, doi: 10.1086/338239
  * Davis et al. (2023) Davis, J. D., Tremonti, C. A., Swiggum, C. N., et al. 2023, ApJ, 951, 105, doi: 10.3847/1538-4357/accbbf
  * de Boer et al. (1990) de Boer, K. S., Morras, R., & Bajaja, E. 1990, A&A, 233, 523
  * Erb et al. (2012) Erb, D. K., Quider, A. M., Henry, A. L., & Martin, C. L. 2012, ApJ, 759, 26, doi: 10.1088/0004-637X/759/1/26
  * Foreman-Mackey et al. (2013) Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067
  * Freedman et al. (2001) Freedman, W. L., Madore, B. F., Gibson, B. K., et al. 2001, ApJ, 553, 47, doi: 10.1086/320638
  * Gaustad et al. (2001) Gaustad, J. E., McCullough, P. R., Rosing, W., & Van Buren, D. 2001, PASP, 113, 1326, doi: 10.1086/323969
  * Gnat & Sternberg (2007) Gnat, O., & Sternberg, A. 2007, ApJS, 168, 213, doi: 10.1086/509786
  * Hainich et al. (2019) Hainich, R., Ramachandran, V., Shenar, T., et al. 2019, A&A, 621, A85, doi: 10.1051/0004-6361/201833787
  * Harris et al. (2020) Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2
  * Harris & Zaritsky (2009) Harris, J., & Zaritsky, D. 2009, AJ, 138, 1243, doi: 10.1088/0004-6256/138/5/1243
  * Hawcroft et al. (2023) Hawcroft, C., Sana, H., Mahy, L., et al. 2023, arXiv e-prints, arXiv:2303.12165, doi: 10.48550/arXiv.2303.12165
  * Heckman et al. (2015) Heckman, T. M., Alexandroff, R. M., Borthakur, S., Overzier, R., & Leitherer, C. 2015, ApJ, 809, 147, doi: 10.1088/0004-637X/809/2/147
  * Heckman et al. (2000) Heckman, T. M., Lehnert, M. D., Strickland, D. K., & Armus, L. 2000, ApJS, 129, 493, doi: 10.1086/313421
  * Hopkins et al. (2014) Hopkins, P. F., Kereš, D., Oñorbe, J., et al. 2014, MNRAS, 445, 581, doi: 10.1093/mnras/stu1738
  * Howk et al. (2002) Howk, J. C., Sembach, K. R., Savage, B. D., et al. 2002, ApJ, 569, 214, doi: 10.1086/339322
  * Hunter (2007) Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55
  * Jenkins (1996) Jenkins, E. B. 1996, ApJ, 471, 292, doi: 10.1086/177969
  * Kallivayalil et al. (2013) Kallivayalil, N., van der Marel, R. P., Besla, G., Anderson, J., & Alcock, C. 2013, ApJ, 764, 161, doi: 10.1088/0004-637X/764/2/161
  * Kennicutt & Evans (2012) Kennicutt, R. C., & Evans, N. J. 2012, ARA&A, 50, 531, doi: 10.1146/annurev-astro-081811-125610
  * Kim & Ostriker (2018) Kim, C.-G., & Ostriker, E. C. 2018, ApJ, 853, 173, doi: 10.3847/1538-4357/aaa5ff
  * Kim et al. (2020) Kim, C.-G., Ostriker, E. C., Fielding, D. B., et al. 2020, ApJ, 903, L34, doi: 10.3847/2041-8213/abc252
  * Kim et al. (1998) Kim, S., Staveley-Smith, L., Dopita, M. A., et al. 1998, ApJ, 503, 674, doi: 10.1086/306030
  * Kim et al. (2003) —. 2003, ApJS, 148, 473, doi: 10.1086/376980
  * Kudritzki & Puls (2000) Kudritzki, R.-P., & Puls, J. 2000, ARA&A, 38, 613, doi: 10.1146/annurev.astro.38.1.613
  * Lehner & Howk (2007) Lehner, N., & Howk, J. C. 2007, MNRAS, 377, 687, doi: 10.1111/j.1365-2966.2007.11631.x
  * Lehner et al. (2009) Lehner, N., Staveley-Smith, L., & Howk, J. C. 2009, ApJ, 702, 940, doi: 10.1088/0004-637X/702/2/940
  * Li et al. (2017) Li, M., Bryan, G. L., & Ostriker, J. P. 2017, ApJ, 841, 101, doi: 10.3847/1538-4357/aa7263
  * Martin (2005) Martin, C. L. 2005, ApJ, 621, 227, doi: 10.1086/427277
  * Mazzi et al. (2021) Mazzi, A., Girardi, L., Zaggia, S., et al. 2021, MNRAS, 508, 245, doi: 10.1093/mnras/stab2399
  * McKee & Ostriker (1977) McKee, C. F., & Ostriker, J. P. 1977, ApJ, 218, 148, doi: 10.1086/155667
  * McLeod et al. (2019) McLeod, A. F., Dale, J. E., Evans, C. J., et al. 2019, MNRAS, 486, 5263, doi: 10.1093/mnras/sty2696
  * Medallon & Welty (2023) Medallon, S., & Welty, D. 2023, in STIS Instrument Handbook for Cycle 31 v. 22, Vol. 22, 22
  * Morton (2003) Morton, D. C. 2003, ApJS, 149, 205, doi: 10.1086/377639
  * Nidever et al. (2008) Nidever, D. L., Majewski, S. R., & Butler Burton, W. 2008, ApJ, 679, 432, doi: 10.1086/587042
  * Oh et al. (2022) Oh, S.-H., Kim, S., For, B.-Q., & Staveley-Smith, L. 2022, ApJ, 928, 177, doi: 10.3847/1538-4357/ac5905
  * Olsen & Massey (2007) Olsen, K. A. G., & Massey, P. 2007, ApJ, 656, L61, doi: 10.1086/512484
  * Olsen et al. (2011) Olsen, K. A. G., Zaritsky, D., Blum, R. D., Boyer, M. L., & Gordon, K. D. 2011, ApJ, 737, 29, doi: 10.1088/0004-637X/737/1/29
  * Peeples et al. (2019) Peeples, M. S., Corlies, L., Tumlinson, J., et al. 2019, ApJ, 873, 129, doi: 10.3847/1538-4357/ab0654
  * Poggianti et al. (2016) Poggianti, B. M., Fasano, G., Omizzolo, A., et al. 2016, AJ, 151, 78, doi: 10.3847/0004-6256/151/3/78
  * Putman et al. (2012) Putman, M. E., Peek, J. E. G., & Joung, M. R. 2012, ARA&A, 50, 491, doi: 10.1146/annurev-astro-081811-125612
  * Putman et al. (2021) Putman, M. E., Zheng, Y., Price-Whelan, A. M., et al. 2021, ApJ, 913, 53, doi: 10.3847/1538-4357/abe391
  * Rasmussen & Williams (2006) Rasmussen, C. E., & Williams, C. K. I. 2006, Gaussian Processes for Machine Learning
  * Rathjen et al. (2021) Rathjen, T.-E., Naab, T., Girichidis, P., et al. 2021, MNRAS, 504, 1039, doi: 10.1093/mnras/stab900
  * Reichardt Chu et al. (2022) Reichardt Chu, B., Fisher, D. B., Nielsen, N. M., et al. 2022, MNRAS, 511, 5782, doi: 10.1093/mnras/stac420
  * Richter et al. (2015) Richter, P., de Boer, K. S., Werner, K., & Rauch, T. 2015, A&A, 584, L6, doi: 10.1051/0004-6361/201527451
  * Richter et al. (1999) Richter, P., de Boer, K. S., Widmann, H., et al. 1999, Nature, 402, 386, doi: 10.1038/46492
  * Roman-Duval et al. (2019a) Roman-Duval, J., Jenkins, E. B., Williams, B., et al. 2019a, ApJ, 871, 151, doi: 10.3847/1538-4357/aaf8bb
  * Roman-Duval et al. (2019b) —. 2019b, ApJ, 871, 151, doi: 10.3847/1538-4357/aaf8bb
  * Roman-Duval et al. (2020) Roman-Duval, J., Proffitt, C. R., Taylor, J. M., et al. 2020, Research Notes of the American Astronomical Society, 4, 205, doi: 10.3847/2515-5172/abca2f
  * Rubin et al. (2014) Rubin, K. H. R., Prochaska, J. X., Koo, D. C., et al. 2014, ApJ, 794, 156, doi: 10.1088/0004-637X/794/2/156
  * Rupke et al. (2005) Rupke, D. S., Veilleux, S., & Sanders, D. B. 2005, ApJS, 160, 115, doi: 10.1086/432889
  * Russell & Dopita (1992) Russell, S. C., & Dopita, M. A. 1992, ApJ, 384, 508, doi: 10.1086/170893
  * Salem et al. (2015) Salem, M., Besla, G., Bryan, G., et al. 2015, ApJ, 815, 77, doi: 10.1088/0004-637X/815/1/77
  * Savage & de Boer (1981) Savage, B. D., & de Boer, K. S. 1981, ApJ, 243, 460, doi: 10.1086/158613
  * Savage & Sembach (1991) Savage, B. D., & Sembach, K. R. 1991, ApJ, 379, 245, doi: 10.1086/170498
  * Savage & Sembach (1996) —. 1996, ARA&A, 34, 279, doi: 10.1146/annurev.astro.34.1.279
  * Schaye et al. (2015) Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521, doi: 10.1093/mnras/stu2058
  * Sembach et al. (2003) Sembach, K. R., Wakker, B. P., Savage, B. D., et al. 2003, ApJS, 146, 165, doi: 10.1086/346231
  * Setton et al. (2023) Setton, D. J., Besla, G., Patel, E., et al. 2023, arXiv e-prints, arXiv:2308.10963, doi: 10.48550/arXiv.2308.10963
  * Shull et al. (2011) Shull, J. M., Stevans, M., Danforth, C., et al. 2011, ApJ, 739, 105, doi: 10.1088/0004-637X/739/2/105
  * Skibba et al. (2012) Skibba, R. A., Engelbracht, C. W., Aniano, G., et al. 2012, ApJ, 761, 42, doi: 10.1088/0004-637X/761/1/42
  * Smart et al. (2023) Smart, B. M., Haffner, L. M., Barger, K. A., et al. 2023, ApJ, 948, 118, doi: 10.3847/1538-4357/acc06e
  * Soderblom (2023) Soderblom, D. R. 2023, in COS Instrument Handbook v. 15.0, Vol. 15, 15
  * Staveley-Smith et al. (2003) Staveley-Smith, L., Kim, S., Calabretta, M. R., Haynes, R. F., & Kesteven, M. J. 2003, MNRAS, 339, 87, doi: 10.1046/j.1365-8711.2003.06146.x
  * Tan & Fielding (2023) Tan, B., & Fielding, D. B. 2023, arXiv e-prints, arXiv:2305.14424, doi: 10.48550/arXiv.2305.14424
  * Tchernyshyov (2022) Tchernyshyov, K. 2022, ApJ, 931, 78, doi: 10.3847/1538-4357/ac68e0
  * The Astropy Collaboration et al. (2018) The Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, ArXiv e-prints.  https://arxiv.org/abs/1801.02634