# The Scorpion Planet Survey: Wide-Orbit Giant Planets Around Young A-type
Stars

Kevin Wagner,11affiliation: Steward Observatory, University of Arizona
22affiliation: NASA NExSS Alien Earths Team 33affiliation: NASA Hubble
Fellowship Program $-$ Sagan Fellow ⋆ Dániel Apai,11affiliation: Steward
Observatory, University of Arizona 22affiliation: NASA NExSS Alien Earths Team
44affiliation: Lunar and Planetary Laboratory, University of Arizona Markus
Kasper,55affiliation: European Southern Observatory, Germany , Melissa
McClure,66affiliation: University of Leiden, Netherlands and Massimo
Robberto77affiliation: Space Telescope Science Institute

###### Abstract

The first directly imaged exoplanets indicated that wide-orbit giant planets
could be more common around A-type stars. However, the relatively small number
of nearby A-stars has limited the precision of exoplanet demographics studies
to $\gtrsim$10%. We aim to constrain the frequency of wide-orbit giant planets
around A-stars using the VLT/SPHERE extreme adaptive optics system, which
enables targeting $\gtrsim$100 A-stars between 100$-$200 pc. We present the
results of a survey of 84 A-stars within the nearby $\sim$5$-$17 Myr-old Sco
OB2 association. The survey detected three companions$-$one of which is a new
discovery (HIP75056Ab), whereas the other two (HD 95086b and HIP65426b) are
now-known planets that were included without a priori knowledge of their
existence. We assessed the image sensitivity and observational biases with
injection and recovery tests combined with Monte Carlo simulations to place
constraints on the underlying demographics. We measure a decreasing frequency
of giant planets with increasing separation, with measured values falling
between 10$-$2% for separations of 30$-$100 au, and 95% confidence-level (CL)
upper limits of $\lesssim$45$-$8% for planets on 30$-$100 au orbits, and
$\lesssim$5% between 200$-$500 au. These values are in excellent agreement
with recent surveys of A-stars in the solar neighborhood$-$supporting findings
that giant planets out to separations of $\lesssim$100 au are more frequent
around A-stars than around solar-type hosts. Finally, the relatively low
occurrence rate of super-Jupiters on wide orbits, the positive correlation
with stellar mass, and the inverse correlation with orbital separation are
consistent with core accretion being their dominant formation mechanism.

###### Subject headings:

Exoplanets (498), Exoplanet formation (492), Exoplanet systems (484), Early-
type stars (430), Direct imaging (387), Coronagraphic imaging (313)

$\star$$\star$affiliationtext: Correspondence to:
<EMAIL_ADDRESS>
##  1\. Introduction

High-contrast imaging enables studying wide-orbit giant planets around young
stars. However, the number of directly imaged planets remains low (on the
order of tens). After hundreds of nearby sun-like (FGK) stars were observed
over multiple surveys reporting null detections (e.g., Chauvin et al. 2003;
Biller et al. 2007; Kasper et al. 2007; Apai et al. 2008; Nielsen et al.
2013), the first exoplanets discovered by direct imaging$-$including the four
super-Jovian planets orbiting the A5V star HR 8799 (Marois et al., 2008, 2010)
and the giant planet orbiting the A6V star $\beta$ Pictoris (Lagrange et al.,
2010)$-$seemed to indicate that giant planets on orbits of $\sim$10 au or
greater are more common around higher-mass stars.

If the cores of wide-separation giant planets are typically assembled via the
slow, step-wise process of core accretion (e.g., Pollack et al. 1996), which
strongly depends on the local surface density of solids and orbital timescales
in protoplanetary disks (and hence on stellar mass: e.g., Pascucci et al.
2016), then wide-orbit giant planets should be more frequent around higher
mass stars. This is indeed true for close-in giant planets (e.g., Ghezzi et
al. 2018), and this trend should extend to larger radii if the majority of
wide-orbit giant planets form similarly via core accretion. The systems
mentioned above were followed up by discoveries of several other planets
around A-type stars$-$including those orbiting HD 95086 (Rameau et al., 2013),
HD 106906b (Bailey et al., 2014), 51 Eri (Macintosh et al., 2015), and
HIP65426 (Chauvin et al., 2017). It’s also worth noting that an increasing
number of planets have been imaged around lower-mass stars$-$including the two
planets orbiting the K7 (0.76 M⊙) star PDS 70 (Keppler et al., 2018; Wagner et
al., 2018; Haffert et al., 2019) and three planets around K-stars from the
Young Suns Exoplanet Survey (YSES: Bohn et al. 2020, 2021). While the number
of imaged planets around FGK stars has increased in recent years, there remain
fewer than those around A-type stars. Considering even lower-mass (M-dwarf)
stellar hosts, Lannier et al. (2016) also find a lower occurrence rate of
wide-orbit giant planets compared to those around A-stars.

The recent Gemini Planet Imager Exoplanet Survey (GPIES: Nielsen et al. 2019)
presented interim results that indicate a significant ($>$99% confidence
level, or CL) difference between the wide-orbit giant planet populations of
high-mass and low-mass stars. For planets between 5$-$13 MJup and between
$a$=10$-$100 au orbiting $>$1.5 M⊙ stars, they found a 68%-CL interval of
5.3$-$13.9%; whereas similar planets orbiting stars of any spectral type
yielded a 68%-CL interval of 2.1$-$5.4%. Similarly, the SHINE survey on
VLT/SPHERE (Vigan et al., 2021) that includes many of the same targets yielded
a 68%-CL interval of 13.3$-$36.5% for the frequency of 1$-$75 MJup and between
$a$=5$-$300 au orbiting B/A-type stars111Note that the larger value is
reflective of the larger range of parameters, particularly at lower masses and
semi-major axes for which planets are more frequent. and 3.0$–$10.5% for
planets of similar properties orbiting FGK stars. Both samples included $>$50
BA-type stars ($>$70 for GPIES) among the nearby moving groups.

The meta-analysis of directly imaged planets and brown dwarf companions by
Wagner et al. (2019) including all known systems with detected companions also
found a steeper relative companion mass function for high-mass stellar hosts
compared to low-mass stellar hosts. However, the trend may not extend to all
separations. Considering companions on wider orbits of 30$-$300 au, Bowler
(2016) found no significant difference between giant planet populations around
high-mass stars and low-mass stars ($2.8_{-2.3}^{+3.7}$ % of BA stars, $<$4.1%
of FGK stars, and $<$3.9% of M dwarfs). Nevertheless, at separations of
$\lesssim$100 au, the message appears clear: higher-mass stars have a higher
frequency of hosting giant planets.

The higher frequency of wide-orbit giant planets around A-stars influences
models of planet formation and the target lists (and expected yields) of
future surveys. However, the occurrence rate of wide-orbit giant planets
around high-mass stars remains significantly uncertain. Upper-limits range
from $\lesssim$20% (GPIES: 95%-CL, 5$-$13 MJup, 10$-$100 AU, Nielsen et al.
2019) to $\lesssim$36.5% (SHINE: 68%-CL, 1$-$75 MJup, 5$-$300 au, Vigan et al.
2021). These state-of-the-art surveys utilize the latest in extreme adaptive
optics (ExAO) technology. However, they were also designed (with good reason)
to maximize discoveries of planets on shorter periods by focusing on nearby
stars. This limits the available number of A-stars, and particularly those
with well-constrained ages. Well-constrained ages translate to better
constraints on planetary masses (and upper limits) because planetary
luminosity is correlated with both mass and age.

An opportunity to better constrain the demographics of wide-orbit giant
planets around A-type stars has recently arisen with the advent of ExAO
systems on large ground-based telescopes (e.g., Macintosh et al. 2014;
Jovanovic et al. 2015; Males et al. 2018; Beuzit et al. 2019). These systems
routinely reach contrasts of $\sim$10-6 for stars within young moving groups
at distances of $\gtrsim$100 pc, which enables homogeneous surveys of a large
number of A-stars with well-constrained ages. The Sco OB2 association (also
known as Scorpius-Centaurus-Lupus, Sco-Cen, or SCL) at $\sim$100$-$200 pc
contains the largest number of nearby age-dated A-stars (de Zeeuw et al.,
1999; Kouwenhoven et al., 2005, 2007). The ages of the stars within Sco OB2
vary between $\sim$5$-$17 Myr, depending on position within the cluster
(Pecaut & Mamajek, 2016). At such young ages, the population of recently
formed giant planets is luminous enough to be imaged down to $\sim$4$-$5 MJup
at separations $\gtrsim$30 au (e.g., Fortney et al. 2008; Baraffe et al. 2015;
Marleau et al. 2019).

The goal of this survey is to directly constrain the demographics of wide-
orbit giant planets around the A-stars of the Sco OB2 association in order to
inform planet formation models (e.g., Forgan et al. 2018; Emsenhuber et al.
2020), the target selections and expected yields of future surveys, and other
areas where this fundamental parameter is relevant. We also aim to discover
additional planets and substellar companions whose orbits and atmospheres are
accessible to direct characterization. Finally, the results of this survey
will be directly comparable to the demographics of planets around A-stars in
the closer (but slightly older) moving groups targeted largely by past surveys
(e.g., Nielsen et al. 2019; Vigan et al. 2021), and also to lower-mass members
of the Sco OB2 association (Bohn et al., 2020, 2021)$-$enabling trends in
exoplanet properties to be identified across host star age, mass, and
formation environment.

##  2\. Observations and Data Reduction

Our observations utilized the Spectro-Polarimetric High-Contrast Exoplanet
Research Experiment (SPHERE) instrument on the Very Large Telescope (VLT) in
Chile (Beuzit et al. 2019). SPHERE provides contrasts of 10${}^{-5}-10^{-6}$
at separations of $\sim$1” in under an hour for $\sim$7$-$8 mag stars. SPHERE
also provides diffraction-limited imaging with a resolution of
$\sim$0$\farcs$05 across wavelengths of $\sim$1$-$2.2 $\mu$m. Dual-band
imaging in a selection of narrow-band filters over a field of view of $\sim$6”
in radius (IRDIS: Vigan et al. 2010) is combined with simultaneous integral
field spectroscopy from 0.95$-$1.65 $\mu$m over a smaller field of view of
0$\farcs$8 in radius (IFS: Claudi et al. 2008). The latter enables spectral
identification of close-in exoplanet candidates, while the dual-band imaging
enables identifying companions on wider orbits through their common proper
motion.

Our observations typically utilized the IRDIFS_Ext mode of SPHERE, in which
dual-band images are acquired with IRDIS in the $K12$ filters
($\lambda_{K1}$=2.110$\pm$0.051 $\mu$m, $\lambda_{K2}$=2.251$\pm$0.055 $\mu$m)
and spatially-resolved spectra from $Y-H$ band (0.95$-$1.65 $\mu$m, $R\sim$30)
are acquired with the IFS. For some of the targets (primarily among the
systems that were observed by SPHERE for other programs) the IRDIFS mode was
used. This includes the $H23$ dual-band filter combination
($\lambda_{H2}$=1.593$\pm$0.026 $\mu$m, $\lambda_{H3}$=1.667$\pm$0.027 $\mu$m)
and IFS spectroscopy from $Y-J$ (0.95$-$1.35 $\mu$m, $R\sim$50). Each target
was observed for approximately $\sim$30$-$60 minutes of exposure time (see
Appendix A), enabling field rotation of $\sim$20$-$30∘, and resulting in
similar performance for each target.

Table 1Substellar Companions Imaged around Sco-Cen A-type Stars in This Survey

& Name Sub- Age Proj. Mass $a$ $e$ $i$ Dist.a Host SpT References group (Myr)
Sep. (MJup) (pc) SpT HIP75056Ab UCL $\sim$12 0$\hfil\lx@intercol\farcs$15
25$\pm$5 30$\pm$15 au 0.5$\pm$0.2 23$\pm$11∘ 126$\pm$2 A2V M6$-$L2 b HD 95086b
LCC 17$\pm$4 0$\hfil\lx@intercol\farcs$6 4$-$5 59${}^{+10}_{-13}$
0.14${}^{+0.07}_{-0.14}$ 150${}^{+12}_{-13}{}^{\circ}$ 86.4$\pm$0.4 A8V
L1$-$T3 c, d, e HIP65426b LCC 14$\pm$4 0$\hfil\lx@intercol\farcs$8 6$-$12
94${}^{+28}_{-45}$ 0.55${}^{+0.42}_{-0.22}$ 112${}^{+14}_{-18}{}^{\circ}$
109.2$\pm$0.8 A2V L5$-$L7 e, f aafootnotetext: Gaia Collaboration et al.
(2018), b Wagner et al. (2020), c Rameau et al. (2013), d De Rosa et al.
(2016), e Bowler et al. (2020), f Chauvin et al. (2017).

Figure 1.— Confirmed substellar companions among Sco-Cen A-type stars. These
three objects span masses of $\sim$4$-$30 MJup and separations of
$\sim$15$-$100 au. The discoveries and initial analyses of HIP75056Ab, HD
95086b, and HIP65426b can be found in Wagner et al. (2020), Rameau et al.
(2013), and Chauvin et al. (2017), respectively.

###  2.1. Target Selection

Targets were selected from the A-stars within the Sco OB2 association, which
contains the largest group of nearby A-stars with well-determined ages (de
Zeeuw et al., 1999). Typical ages and distances to member systems range from
$\sim$10$-$17 Myr and $\sim$100$-$200 pc (Pecaut & Mamajek, 2016; Gaia
Collaboration et al., 2018). The list of A-stars among this association is
thought to be complete. However, A-stars have a high ($\sim$50%) binary
fraction, which limits the available orbital phase space. Kouwenhoven et al.
(2005) and Kouwenhoven et al. (2007) performed an early AO survey of these
systems using the ADONIS system on the 3.6m New Technology Telescope. This
survey established which A-stars host stellar companions at projected
separations of $\gtrsim$0$\farcs$2.

From the list of 115 A-star systems in Kouwenhoven et al. (2005, 2007), we
selected those that may be capable of hosting wide-orbit planetary systems.
The gravitational influence of a companion star will restrict the range of
stable planetary orbits to those that are $\lesssim$25$-$30% of the stellar
companion’s periapsis (Holman & Wiegert, 1999). For distances of $\sim$100 pc,
a planet on a stable $\sim$50 au orbit translates to a minimum binary
separation of $\gtrsim$1$\farcs$5\. We removed those with smaller projected
separations from the list, which resulted in 97 systems. Among these, we
observed 63 throughout 2015$-$2019, and obtained publicly available datasets
from the European Southern Observatory (ESO) archive for another 25 systems.
This resulted in a total survey size of 88 Sco-Cen A-stars. Of these
observations, four were deemed of inferior quality, typically due to very low
field rotation ($\lesssim$5∘), and were excluded from the analysis. These
include targets with Survey-ID S26, S57, S58, and SA29.

###  2.2. Automated Data Reduction Pipeline

We reduced the data for each target in a uniform manner with a self-developed
automated pipeline designed to take raw data products from the ESO archive and
to assemble the high-level scientific products.222Our open source pipeline is
publicly available at https://github.com/astrowagner/sphere-
tools/releases/tag/v1.2. For single stars, this pipeline works well with no
intervention. However, some intervention is needed in the case of wide binary
systems (i.e., to mask the companion) or observations taken with a non-
standard approach (e.g., those lacking standard calibrations). The pipeline
results in point-spread function (PSF) subtracted images, signal to noise
ratio (SNR) maps generated according to Mawet et al. (2014), contrast curves
generated through synthetic planet injections (with a SNR threshold set to 5),
and lists of candidate sources above a given SNR threshold (set to 3.5 for
initial identification and vetting).

The data reduction pipeline began with the procedures presented in Apai et al.
(2016) and was improved sequentially throughout the survey (Wagner et al.,
2015, 2016, 2018, 2020). The pipeline is briefly summarized here. Basic
calibrations, including dark subtraction, flat field division, and
determination of the coronagraphic centering via satellite spots (typically
taken at the start of the observation), were performed for all datasets. For
relevant epochs (those prior to 2016 July), the time synchronization
correction of Maire et al. (2016) was applied to the parallactic angle
information, and for all epochs the Maire et al. (2016) astrometric and field
distortion corrections were applied. For single-star targets, the images were
then aligned in the pupil-stabilized orientation via cross-correlation. For
multi-star systems, or those with bright background contaminants, the cross-
correlation was either performed on only a central image patch (for widely
separated companions) or in the field-stabilized orientation for more closely
separated binaries in which the wings of the PSF interfere with alignment.

At this stage (when relevant) synthetic point sources were injected into the
data using unsaturated frames taken through a neutral density filter with the
target slewed $\sim$0$\farcs$5 off of the coronagraph. The PSF was then
subtracted via two independent means: via classical angular differential
imaging (cADI: Marois et al. 2006) and via projection onto eigen-images via
the Karhunen-Loève Image Processing (KLIP) algorithm (Soummer et al., 2012).
Given the difference in sensitivity to typical exoplanet spectral features
between the K1 and K2 (H2 and H3) images, the K1 (H2) images were utilized for
the subsequent analysis, except where color-based information is relevant
(i.e., vetting of initial candidates).

Figure 2.— Left: Sensitivity of the Scorpion Planet Survey in K1-band for
IRDIFS_Ext observations. Right: Sensitivity in H2-band for IRDIFS
observations. In both panels the thick black curve shows the median
sensitivity, while the blue and red curves show the best and worst cases for
background-limited sensitivity.

####  2.2.1 Candidate Identification and Follow-up Strategy

Candidates were identified both visually and via an automated SNR-based
approach. For each pixel, we measure the flux in an aperture of diameter
equivalent to the full width at half maximum (FWHM), and noise in non-
overlapping apertures at the same radius. We exclude immediately adjacent
apertures to minimize the impact of negative side-lobes of true signals
introduced by the ADI-based PSF subtraction. We then utilize Equation 9 of
Mawet et al. (2014) to calculate the SNR. Systems with candidates of
SNR$\geq$3.5 were included in subsequent follow-up imaging. Many systems are
near the galactic plane and hence the background contamination fraction is
relatively high ($\gtrsim$50% of the targets have at least one background
object within the field of view). Ultimately, we attempted to follow-up all
observations with plausible exoplanet candidates and were able to re-observe
35 out of 40 systems with identified candidates. One candidate was verified as
co-moving (HIP75056Ab, Wagner et al. 2020), and two were identified as known
planetary companions (HD 96085b and HIP65426b, Rameau et al. 2013 and Chauvin
et al. 2017). These are shown in Figure 1 and described in Table 2. The rest
were identified as background objects by their relative proper motion.

For the five systems (S54, SA6, SA10, SA11, and SA25) with detected sources
that were not re-observed (mostly within crowded fields and at separations
greater than several arcsec), we consider these sources as likely background
stars and adjusted the detection limits according to the brightest ambiguous
source. A more sophisticated treatment of the systems that were not followed
up would incorporate additional parameters for the possibility that the
detected sources may be bound. However, given the nature of these sources and
the fact that they lie in a region of parameter space that other studies have
probed with greater sensitivity, these prior probabilities are close to zero.
We also confirmed that removing these systems completely does not
significantly alter the results.

###  2.3. Sensitivity Analysis

We assessed the sensitivity of the data through simulated companion injection
and retrieval tests. After image alignment (but prior to high-pass filtering
and PSF subtraction) we injected scaled versions of the mean of the off-axis
flux calibration frames333Taken with a neutral density filter (typically ND1.0
or ND2.0, which transmit $\sim$10% and $\sim$1%, respectively). See
https://www.eso.org/sci/facilities/paranal/instruments/sphere/inst/filters.html
for more details. and proceeded with the following steps of the data
reduction. We blocked regions contaminated by bright binary companions or
background stars with NaN values, and measured the SNR at the position of
injection according to Mawet et al. (2014). We iterated upon the brightness of
the source until it was recovered within 5% of SNR=5. We repeated the process
at separations from 0$\farcs$1$-$5” and at 10 evenly distributed azimuthal
angles (beginning with PA=0∘), and then took the median of the sensitivity
over azimuth. The results are shown in Figure 2.

For $K1$ datasets, we reach a median background-limited (5-$\sigma$) contrast
of $\sim$3$\times$10-6 at separations of $\gtrsim$1$\farcs$5\. For $H2$
datasets, we reach a similar median background-limited contrast of $\sim$10-6.
These detection limits correspond to masses of $\sim$4 and $\sim$2 MJup
(Baraffe et al., 2003) for an age of 15 Myr and distance of 125 pc around a
$K$=6 ($H$=6.5) star.444The poorer background-limited sensitivity for $K1$
datasets is due to the higher sky background at longer wavelengths. In the
best cases, we reach contrasts $\sim$3$\times$ lower than the median
background limits, enabling planets as low as $\sim$1 MJup to be identified
around a handful of targets. In the worst cases, the SNR=5 background limits
are $\sim$2$-$3$\times$10-5 for some of the $K1$ datasets, which corresponds
to masses of $\sim$6 MJup. At $\sim$0$\farcs$5, we reach median-contrasts of
$\sim$10-5 in both $K1$ and $H2$, which corresponds to $\sim$5 $M_{Jup}$.
Notably, we find a relatively uniform sensitivity to $\gtrsim$5 MJup planets
at projected separations of $\gtrsim$50 au.

###  2.4. Monte Carlo Modelling

Previous studies have focused on exploring the scaling relations of planet
frequency with physical parameters such as semi-major axis and mass through
Markov Chain Monte Carlo (MCMC) simulations. Typically, power-laws in mass and
semi-major axis distributions (and sometimes additional parameters, such as
stellar mass: Nielsen et al. 2019) are explored in conjunction with frequency.
Since each power-law is unknown, as well as the frequency, these three
parameters are degenerate with one another. Here, we do not try to replicate
this approach, and instead seek to constrain only the frequency of planets (in
defined mass and semi-major axis bins) while remaining agnostic to the
underlying scaling relations. For this, we utilize a simpler Monte Carlo (MC)
simulation of our survey to translate the measured sensitivity to constraints
on the underlying planet population (following the approach of Kasper et al.
2007).

We assumed a normally distributed prior on host star distance, with distances
obtained from parallaxes reported by Gaia Collaboration et al. (2018). We
assumed ages of 10 Myr, which is near the average age of the association
(Pecaut & Mamajek, 2016). Since our sample was drawn with representation from
the different sub-groups, the spread of ages among our sample is
representative of the spread of ages among the Sco OB2 association
($\sim$5$-$17 Myr), which effectively incorporates the uncertainty in age. We
assumed uniform prior mass and semi-major axis distributions to generate a
large number of companions spanning the available parameter space. Note that
the MC serves only to simulate a large number of planets to be compared to the
detection limits, and the chosen prior distributions here do not significantly
impact our results (we verified that this is the case by also testing assumed
distributions of $f\propto$ $M^{-0.5}$, $M^{-1.0}$, and $M^{-1.5}$).

This approach is applicable at this step since we are only concerned with the
ratio of detected vs. non-detected planets in relatively small pre-determined
bins (compared to the range over which we later report the frequency). These
bins are also defined in a manner such that the sensitivity across each bin is
nearly uniform. We randomly generated planets on circular orbits with a
uniform prior in the cosine of inclination (see the Appendix of Brandt et al.
2014 and references therein for a discussion of the effects of non-zero
eccentricity, which are of secondary importance). In this manner, 4,000
companions were simulated for each star with masses between M $=0.1-35M_{Jup}$
and $a=2-550$ au. This generated a sufficient level of smoothness within the
individual maps$-$i.e., runs with greater numbers of simulated companions do
not alter or improve the results.

We utilized the models of Baraffe et al. (1998, 2003, 2015) to convert mass to
estimated luminosity. The choice of these high-entropy models is motivated by
recent work (e.g., Marleau et al. 2019) indicating that the low-entropy models
(e.g., Marley et al. 2007; Fortney et al. 2008), in which a large portion of
the accretion-generated heat escapes during formation, are an unlikely outcome
of giant planet formation. We calculated the ratio of detected vs. non-
detected companions over a pre-determined grid of mass and semi-major axis
bins555The grid was defined as $a$=[1, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80,
90, 100, 120, 150, 200, 300, 400, 500] au $\times$ $M$=[0.1, 0.5, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35] MJup. The widths of the
bins were set to 10% of the values at each grid point. to estimate the
completeness to planets around each star. These individual completeness maps
were summed to create a total estimated completeness map for the
survey$-$shown in the left panel of Figure 3. In other words, this map shows
the number of stars around which we would expect to have observed planets of a
given mass and semi-major axis if they were uniformly present.

###  2.5. Statistical Methods

Two-dimensional Gaussian profiles were constructed for each companion based on
their available constraints on mass and semi-major axis. These were normalized
such that the sum over each is equal to unity to represent a single detected
companion. These were then summed to create a detection probability map to be
compared to the completeness map (Figure 3, left). We consider only masses
above $\sim$5 MJup and separations of $a\geq$30 au in order to restrict the
analysis to the region in which the sensitivity is relatively uniform. We then
explored the consistency of the detections across several ranges of semi-major
axes666Specifically, we assesed the frequency of giant planets of M=5$-$35
MJup in semi-major axis bins of 30$-$40 au, 40$-$60 au, 60$-80$ au, 80$-$120
au, 120$-$200 au, 200$-$300 au, and 300$-$450 au. These correspond to regions
with near-uniform sensitivity. with the average expected number of stars
around which we would have been capable of detecting such planets within the
set range.

We calculated confidence intervals via the binomial distribution, which gives
the probability of a number of successful events occurring among a certain
number of trials with a given underlying frequency. For this, we rounded the
number of detected companions and average completeness to the nearest integer.
For each bin, we report the measured detection rate, along with the
frequencies that are consistent with the data at both the 68%-CL and 95%-CL.
For bins in which the number of detected companions rounds to zero (i.e.,
those at $>$120 au), we report only 95%-CL upper limits (Figure 3).

##  3\. Results

Figure 3.— Left: Sensitivity of the Scorpion Planet Survey from Monte Carlo
simulations. The contours identify the number of stars around which a planet
of a given mass and semi-major axis would have resulted in a detection if they
were uniformly present. Right: Frequency vs. semi-major axis. The blue points
show the measured frequency, whereas the vertical (black) uncertainty shows
the 68% confidence interval. The blue ranges denote upper and lower 95%
confidence values (i.e., their range denotes the 90% confidence interval). The
horizontal bars represent the range of the semi-major axis bins. A power-law
of $f\propto a^{-1.7}$ is shown for comparison (i.e., not as a best-fit). The
vertical dotted line denotes the inner-most region ($\sim$23 au) at which we
would expect to have found $\geq$1 companion if they were uniformly present.

###  3.1. Detected Companions

While the main result of our survey is its statistical analysis, the number of
detected companions is small enough to briefly consider their properties on an
individual basis. The detected companions are described in Table 2 and shown
in Figure 1. They span masses of several times that of Jupiter to $\sim$25
MJup and projected separations of $\sim$15$-$90 au. Their established
properties and relevant references are summarized briefly below.

####  3.1.1 HIP75056Ab

HIP75056Ab is the most massive substellar companion among the sample and that
of the shortest orbital period. This $\sim$20$-$30 MJup companion at
$\sim$0$\farcs$15 to an A2V star was discovered as part of our survey (Wagner
et al., 2020). It orbits the primary component of a wide-separation
(5$\farcs$2) binary with a mass ratio of $q\sim$ 0.16 within the Upper
Centaurus Lupus (UCL) sub-group of Sco OB2. HIP75056Ab was detected in 2015
and 2019$-$establishing that it is co-moving with the system on an orbit of
$a$=30$\pm$15 au and $e$=0.5$\pm$0.2. From IFS data combined with the
companion’s $K1$ and $K2$ photometry, Wagner et al. (2020) inferred a spectral
type of M8$\pm$1 and a temperature of $\sim$2300$\pm$300 K. From our
sensitivity and completeness analysis, we estimate that we would be sensitive
to $\sim$3 such companions if they had an occurrence rate of one per star. The
detection of this companion in a region of parameter space with such
relatively low sensitivity is likely indicative of a relatively high frequency
of companions at smaller separations, and not a higher relative frequency of
more massive companions due to the sharply decreasing frequency of companions
more massive than $\sim$10$-$20 MJup at all separations (e.g., Wagner et al.
2019).

####  3.1.2 HD 95086b

HD 95086b is the least-massive companion detected in our survey. This
$\sim$4$-$5 MJup exoplanet was discovered by Rameau et al. (2013) with
VLT/NaCo. The host is a single A8 pre-main sequence star toward the near-side
of the Lower Centaurus-Crux (LCC) subgroup of the Sco OB2 association. The
planet is on an orbit with $a\sim$ 40$-$70 au and $e\lesssim$ 0.2 (Bowler et
al., 2020). The system also hosts a debris disk (Su et al., 2017) with a
background galaxy in projected separation (Zapata et al., 2018). From our
sensitivity and completeness analysis, we estimate that we would have the
capability to detect such a companion around $\sim$7 stars.

####  3.1.3 HIP65426b

At a projected separation of $\sim$90 au, HIP65426b is the widest-separation
companion detected in our survey. This $\sim$6$-$12 MJup companion was
discovered by Chauvin et al. (2017) as part of the SHINE survey (Vigan et al.,
2021). This companion is the most similar in properties among those detected
here to the iconic HR 8799b (Marois et al., 2008), which is the type of
companion whose frequency our survey was designed to constrain ($M\sim$ 5
MJup, $a\sim$ 70 au). From our sensitivity analysis, we estimate that we would
have the capability to detect a companion such as HIP65426b around $\sim$27
stars. A trend that small-separation companions (between $\sim$5$-$30 MJup)
are more frequent may already be apparent.

###  3.2. Wide-Orbit Giant Planet Frequency

This survey was designed primarily to constrain the frequency of wide-orbit
super-Jupiters, with HR 8799-b at $\sim$70 au as the prime example (Marois et
al., 2008, 2010). In other words, we are primarily focused on those between
$M$=5$-$15 MJup and $a=60-80$ au. Within this range, we measured a frequency
of 3.4${}^{+5.4}_{-2.5}$% and establish a 95%-CL upper limit of $\leq$13.9%.
This is somewhat lower than (but consistent with) values from other recent
surveys, for reasons that will be discussed in §4. We also explored frequency
constraints at different separations and across a broader range of masses
(5$-$35 MJup). The measurements of giant planet frequency vs. semi-major axis
are shown in the right panel of Figure 3. For companions with M=5$-$35 MJup
and $a\sim$ 30$-$40 au (80$-$120 au), we measured observed frequencies of
$\sim$10% ($\sim$2%), with a 95% upper-limit of $\leq$45% ($\leq$8%).
Consistent with the results of most other surveys, we measure a decreasing
frequency of companions with semi-major axis.

###  3.3. Protoplanetary and Debris Disks

While our observations were designed to directly image exoplanets, they are
also sensitive to scattered light from circumstellar dust. Here, we briefly
and qualitatively describe those results. We obtained the first images of the
debris disk around HD 110058 (Kasper et al., 2015) and the spiral
protoplanetary disk around HD 100453 (Wagner et al., 2015), which were both
inferred to exist through their infrared spectral energy distributions
(SEDs).777However, we did not detect several other disks among our sample that
were also identified through their SEDs. The presence of such disks
(especially the relatively massive disk round HD 100453) is evidence of the
youth of Sco OB2, which is in line with recent estimates of the maximum age of
$\lesssim$17 Myr (Pecaut & Mamajek, 2016). While these disks lower the
sensitivity to planets within these systems (accounted for via synthetic
injections), they also open additional opportunities for planet formation
research (e.g., Dong et al. 2016; Wagner et al. 2018; Nealon et al. 2020),
through, for instance, studying disk structures as tracers of dynamical
processes.

###  3.4. HD 131399Ab

In Wagner et al. (2016), we reported the detection of a planetary-mass
companion on an $\sim$80 au orbit within the HD 131399 quadruple system (at
the time thought to be a triple: Lagrange et al. 2017). Subsequent
observations determined that this object is likely to be a high-velocity
background star with its own significant proper motion aligned with the system
(Nielsen et al., 2017). Here, we report additional observations in support of
this hypothesis. Our extended time baseline covers several years and displays
a clear parallax difference between this object and the HD 131399 system that
is most notably seen in the plot of position angle vs. time (Figure 4). This
confirms that it is indeed a background object. HD 131399 and its planet-
hosting classification history carry a useful lesson, as assumptions of a
stationary background are still commonly assumed. The background object has an
approximate proper motion of $-$5 mas/yr and $-$11 mas/yr in right ascension
and declination, respectively, and an parallax of $\lesssim$1 mas (i.e., a
distance of $\gtrsim$1 kpc and a 2D relative velocity of $\gtrsim$57 km/s with
respect to the Sun). This motion is aligned with the system such that initial
common proper motion tests yielded an incorrect classification of the object
as a likely bound companion.

Figure 4.— Evolution of relative position angle vs. time of the background
object near HD 131399A. The response to the foreground system’s (HD 131399’s)
greater parallax is most notably apparent in the 2017 and 2018 years, which
confirms this previous exoplanet candidate as a background contaminant.

##  4\. Discussion

The primary scientific motivation of this survey is to constrain the frequency
of wide-orbit super-Jovian planets orbiting A-stars. Few planet formation
models, if any, predict a significant ($\gtrsim$10%) occurrence rate of
$\sim$5$-$15 MJup planets at $\sim$70 au around hosts of any stellar type.
Therefore, this parameter is a fundamental test of the existing models (e.g.,
Pollack et al. 1996; Boss 1997; Mordasini et al. 2009, 2012; Forgan et al.
2018; Emsenhuber et al. 2020, etc.) and one that is becoming better
constrained as the instrumentation progresses.

The frequency of wide-orbit giant planets around A-type stars is also relevant
to determine the future yield of exoplanet imaging surveys. For this, trends
with separation play a crucial role, as more parameter space is becoming
accessible at smaller separations as AO technology improves along with
increasing primary mirror diameters. The evidence so far suggests quite
strongly that giant planet occurrence rates increase toward smaller
separations (until a few au: Fernandes et al. 2019; Fulton et al. 2021).
However, the frequency of such companions remains largely uncertain. This is
often the reason why surveys report occurrence rates over a wide range of
parameters. In order to utilize a survey’s best sensitivity, constraints must
necessarily come from the outer regions ($\gtrsim$30 au). Often, ranges for
reported frequencies extend over hundreds of au, and are easily misinterpreted
as implying greater precision than actually exists for planets at the smaller
ends of the semi-major axis and mass ranges.

Recently, Nielsen et al. (2019) and Vigan et al. (2021) reported frequencies
for giant planets orbiting BA-stars of 9${}^{+5}_{-4}$% (M=5$-$13 MJup,
$a$=10$-$100 au), and 23.0${}^{+13.5}_{-9.7}$% (M=1$-$75 MJup, $a$=5$-$300
au), respectively. Over the same range as Nielsen et al. (2019), we measure an
occurrence rate of 12.8${}^{+10.4}_{-7.2}$%. These relatively high estimates
and uncertainties are reflective of the ranges involved and the higher
frequency of planets with small masses and at small separations. The meta-
analysis of Bowler (2016), which considered only companions on $a\geq$30 au
orbits and found a relatively low occurrence rate of wide-orbit planets
orbiting BA-stars ($\sim$3%), is also indicative of the bias of higher
occurrence rates at small separations.

Considering planets with orbital semimajor axes of $a$=60$-$80 au and masses
of M$=$5$-$15 MJup, we find an occurrence rate of 3.4${}^{+5.4}_{-2.5}$% and a
95%-CL upper limit of $<$13.9%. In comparison, for M$=$5$-$35 MJup companions
at separations of $a=$30$-$40 au (i.e., those at shorter orbital periods and
including larger masses), we measure an occurrence rate of $\sim$10% and
establish a 95%-CL upper limit of $\lesssim$45%. This illustrates the power of
establishing sensitivity at smaller separations to dramatically increase the
yield of direct imaging planet surveys. These results are also consistent with
the majority of directly imaged planetary-mass companions at a$\lesssim$100 au
forming via core accretion (e.g., Kratter et al. 2010; Wagner et al. 2019). In
contrast, giant planets formed via gravitational instability should be more
common at larger radii and around less massive stars, as instabilities are
more likely in the cold outer disk regions (e.g., Boss 1997).

Finally, our results offer a complementary sample to past studies that were
largely focused on lower-mass stellar hosts in the solar neighborhood, and
also to other on-going surveys of both higher and lower-mass stars in Sco OB2.
Taken in combination with GPIES (Nielsen et al., 2019) and SHINE (Vigan et
al., 2021), the BEASTS survey of B-type stars (Janson et al., 2021), and YSES
survey (Bohn et al., 2020, 2021) of solar-type stars (both also focused on Sco
OB2), these surveys provide a rich and comprehensive view of wide-orbit giant
plants around nearby young stars. Together, they inform our understanding of
exoplanet demographics, and perhaps of equal importance, enable the yield of
future exoplanet imaging surveys to be enhanced by targeting stars around
which detectable planets are more likely to exist.

##  5\. Summary and Conclusions

We conducted a uniform blind survey for substellar companoions around 88
A-type stars in the Scorpius-Centaurus OB association with the VLT/SPHERE
extreme-AO system. We detected three substellar companions$-$one of which,
HIP75056Ab, marks a new discovery. We computed the sensitivity to companions
across a two-dimensional grid of separations and position angles with forward
modelling of injected (simulated) planetary signals in the data, and utilized
these results to explore the consistency with underlying planet populations
through Monte Carlo simulations.

We find that giant planets on wide orbits (like HR 8799b and HIP65426b) with
masses of 5$-$15 MJup and semi-major axes of 60$-$80 au have an occurrence
rate of 3.4${}^{+5.4}_{-2.5}$% and a 95%-CL upper limit of $\leq$13.9%. For
companions between M=5$-$35 MJup, we measure frequencies between 10$-$2% for
separations of 30$-$100 au, and 95% confidence-level (CL) upper limits of
$\lesssim$45$-$8% for planets on 30$-$100 au orbits, and $\lesssim$5% between
200$-$500 au. In line with other recent studies (e.g., Nielsen et al. 2019;
Wagner et al. 2019; Vigan et al. 2021), we confirm the trend of increasing
occurrence rates as a function of smaller separations. When compared to other
studies of less-massive stars (e.g., Chauvin et al. 2003; Biller et al. 2007;
Kasper et al. 2007; Stone et al. 2018; Nielsen et al. 2019; Vigan et al.
2021), we confirm a trend of more massive stars having a higher frequency of
wide-orbit giant planets. This is consistent with exoplanet population trends
among inner planetary systems (e.g., Ghezzi et al. 2018; Mulders et al. 2021)
and also with core accretion being the dominant formation mechanism among this
population (e.g., Kratter et al. 2010; Wagner et al. 2019). Consequently,
further progress to enable sensitivity to high contrast ratios at small
separations and a dedicated focus on young A-type stars should significantly
raise the yield of exoplanet imaging surveys.

##  6\. Acknowledgments

This work is based on observations performed with ESO’s Very Large Telescope
and SPHERE instrument under program IDs 095.C-0298, 095.C-0389, 096.C-0241,
296.C-5036, 097.C-0545, 097.C-0060, 097.C-0826, 097.C-0865, 097.C-0949,
097.C-1019, 099.C-0247, 099.C-0300, 099.C-0402, 0101.C-0513, 0103.C-0628,
198.C-0209, and 1100.C-0481. The results reported herein benefited from
collaborations and/or information exchange within NASA’s Nexus for Exoplanet
System Science (NExSS) research coordination network sponsored by NASA’s
Science Mission Directorate. K.W. acknowledges support from NASA through the
NASA Hubble Fellowship grant HST-HF2-51472.001-A awarded by the Space
Telescope Science Institute, which is operated by the Association of
Universities for Research in Astronomy, Incorporated, under NASA contract
NAS5-26555.

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##  7\. Appendix A: Target List

Table 2Targets $-$ Sco-Cen A-type Stars

Target | R.A. | Dec. | HIP # | $K_{s}$ | SpT | Sub- | Date Obs. | Exp. Time | Field | Filter | DIT | Survey
---|---|---|---|---|---|---|---|---|---|---|---|---
# | (J2000) | (J2000) |  |  |  | group | (YYYY-MM-DD) | (min) |  Rot. (∘) |  | (sec) | ID
1 | 10 19 05.1 | -64 40 35 | 50520 | 6.23 | A1V | LCC | 2015-04-26 | 42.1 | 19.1 | $K12$ | 8.00 | S11
2 | 10 57 03.0 | -68 40 02 | 53524 | 6.76 | A8III | LCC | 2019-04-13 | 101 | 33.8 | $K12$ | 96.0 | SA1_2
3 | 11 05 45.7 | -47 26 32 | 54231 | 6.75 | A0V | LCC | 2015-02-06 | 68.3 | 39.2 | $H23$ | 64.0 | SA2
4 | 11 17 58.1 | -64 02 33 | 55188 | 7.43 | A2V | LCC | 2016-05-20 | 25.6 | 9.77 | $K12$ | 16.0 | S41
5 | 11 27 29.4 | -39 52 35 | 55899 | 7.07 | A0V | LCC | 2018-02-26 | 76.8 | 61.0 | $K12$ | 96.0 | SA3
6 | 11 33 05.6 | -54 19 29 | 56354 | 5.78 | A9V | LCC | 2015-04-10 | 23.5 | 12.5 | $K12$ | 8.00 | S6
|  |  |  |  |  |  | 2016-01-21 | 23.5 | 7.65 | $H23$ | 8.00 | S6_3
|  |  |  |  |  |  | 2016-01-23 | 23.5 | 7.39 | $K12$ | 8.00 | S6_2
7 | 11 41 00.2 | -54 32 56 | 56993 | 7.38 | A0V | LCC | 2016-06-05 | 22.4 | 12.1 | $K12$ | 16.0 | S44
|  |  |  |  |  |  | 2019-04-09 | 27.9 | 15.2 | $K12$ | 8.00 | S44_2
8 | 11 51 13.1 | -43 55 59 | 57809 | 6.61 | A0V | LCC | 2017-05-15 | 32.0 | 22.7 | $K12$ | 16.0 | S47
9 | 11 59 23.7 | -57 10 05 | 58465 | 6.32 | A2V | LCC | 2016-03-30 | 64.0 | 27.9 | $H23$ | 64.0 | SA5
10 | 12 09 38.8 | -58 20 59 | 59282 | 7.00 | A3V | LCC | 2017-05-04 | 68.3 | 34.8 | $K12$ | 64.0 | SA6
11 | 12 11 05.9 | -56 24 05 | 59397 | 7.01 | A2V | LCC | 2017-06-01 | 31.5 | 14.5 | $K12$ | 16.0 | S51
12 | 12 11 14.8 | -52 13 03 | 59413 | 7.46 | A6V | LCC | 2015-04-10 | 17.9 | 13.1 | $K12$ | 8.00 | S23
|  |  |  |  |  |  | 2015-06-16 | 25.6 | 13.5 | $K12$ | 16.0 | S23_2
|  |  |  |  |  |  | 2019-05-29 | 29.3 | 16.4 | $K12$ | 8.00 | S23_3
13 | 12 12 10.3 | -63 27 15 | 59502 | 6.80 | A2V | LCC | 2018-03-24 | 32.0 | 12.3 | $K12$ | 16.0 | S63
|  |  |  |  |  |  | 2019-05-26 | 29.9 | 12.3 | $K12$ | 8.00 | S63_2
14 | 12 17 06.3 | -65 41 35 | 59898 | 5.99 | A0V | LCC | 2016-04-06 | 67.2 | 23.9 | $H23$ | 64.0 | SA7
15 | 12 24 51.9 | -72 36 14 | 60561 | 6.59 | A0V | LCC | 2017-06-13 | 57.6 | 24.4 | $K12$ | 96.0 | SA8
16 | 12 28 19.3 | -64 20 28 | 60851 | 5.98 | A0Vn | LCC | 2017-05-15 | 32.0 | 12.1 | $K12$ | 16.0 | S48
|  |  |  |  |  |  | 2019-04-26 | 27.5 | 12.1 | $K12$ | 8.00 | S48_2
|  |  |  |  |  |  | 2019-05-29 | 27.7 | 12.3 | $K12$ | 8.00 | S48_3
17 | 12 33 19.9 | -54 58 52 | 61265 | 7.44 | A2V | LCC | 2016-04-23 | 19.2 | 10.9 | $K12$ | 16.0 | S35
|  |  |  |  |  |  | 2019-04-09 | 29.1 | 15.1 | $K12$ | 8.00 | S35_2
18 | 12 38 07.3 | -55 55 52 | 61639 | 6.94 | A1/A2V | LCC | 2017-06-01 | 31.5 | 14.9 | $K12$ | 16.0 | S52
19 | 12 39 46.2 | -49 11 56 | 61782 | 7.56 | A0V | LCC | 2015-04-04 | 19.9 | 14.6 | $K12$ | 8.00 | S1
|  |  |  |  |  |  | 2015-04-13 | 58.4 | 34.4 | $H23$ | 16.0 | S1_2*
20 | 12 57 26.2 | -67 57 39 | 63236 | 6.66 | A2IV/V | LCC | 2017-06-15 | 51.2 | 33.9 | $K12$ | 96.0 | SA10
21 | 13 05 02.0 | -64 26 30 | 63839 | 6.66 | A0V | LCC | 2016-04-17 | 83.2 | 30.5 | $H23$ | 64.0 | SA11
22 | 13 10 58.4 | -52 34 01 | 64320 | 6.22 | Ap | LCC | 2015-04-09 | 21.6 | 12.8 | $K12$ | 8.00 | S4
|  |  |  |  |  |  | 2017-06-22 | 31.2 | 16.3 | $K12$ | 16.0 | S4_2
23 | 13 18 24.9 | -45 45 53 | 64925 | 6.88 | A0V | LCC | 2016-05-20 | 25.6 | 14.3 | $K12$ | 16.0 | S42
24 | 13 18 34.9 | -51 17 09 | 64933 | 6.29 | A0V | LCC | 2015-05-01 | 18.8 | 13.6 | $K12$ | 8.00 | S15
|  |  |  |  |  |  | 2019-05-26 | 29.9 | 17.1 | $K12$ | 8.00 | S15_2
25 | 13 20 26.8 | -49 13 25 | 65089 | 7.37 | A7/A8V | LCC | 2017-07-01 | 31.7 | 18.4 | $K12$ | 16.0 | S55
|  |  |  |  |  |  | 2018-04-08 | 27.7 | 16.0 | $K12$ | 16.0 | S55_2
26 | 13 21 57.1 | -51 16 56 | 65219 | 6.52 | A3/A4 | LCC | 2015-07-10 | 25.6 | 13.0 | $K12$ | 16.0 | S27
|  |  |  |  |  |  | 2019-05-31 | 29.6 | 17.5 | $K12$ | 8.00 | S27_2
27 | 13 24 08.6 | -53 47 35 | 65394 | 7.25 | A1Vn | LCC | 2018-04-10 | 76.8 | 35.5 | $K12$ | 96.0 | SA12
28 | 13 24 36.1 | -51 30 16 | 65426 | 6.78 | A2V | LCC | 2017-02-09 | 59.7 | 49.1 | $K12$ | 64.0 | SA13
29 | 13 29 36.2 | -47 52 33 | 65822 | 6.68 | A1V | LCC | 2015-06-11 | 24.5 | 15.1 | $K12$ | 16.0 | S20
30 | 13 32 39.2 | -44 27 01 | 66068 | 7.04 | A1/A2V | LCC | 2017-05-07 | 25.6 | 17.5 | $K12$ | 64.0 | SA14
31 | 13 38 42.9 | -44 30 59 | 66566 | 7.36 | A1V | LCC | 2015-05-15 | 50.1 | 33.6 | $H23$ | 64.0 | SA15
32 | 13 40 37.7 | -44 19 49 | 66722 | 6.32 | A0V | UCL | 2018-02-22 | 32.0 | 21.9 | $K12$ | 16.0 | S60
|  |  |  |  |  |  | 2019-05-25 | 20.9 | 22.7 | $K12$ | 8.00 | S60_2
|  |  |  |  |  |  | 2019-06-27 | 28.5 | 22.8 | $K12$ | 8.00 | S60_3
|  |  |  |  |  |  | 2019-06-30 | 29.6 | 21.7 | $K12$ | 16.0 | S60_4
33 | 13 44 16.0 | -51 00 45 | 67036 | 6.69 | A0p | LCC | 2015-07-17 | 25.6 | 13.3 | $K12$ | 16.0 | S30
34 | 13 56 20.0 | -54 07 57 | 68080 | 6.28 | A1V | UCL | 2016-06-05 | 32.3 | 17.0 | $H23$ | 16.0 | SA17
35 | 14 01 45.7 | -39 26 19 | 68532 | 7.03 | A3IV/V | UCL | 2015-04-09 | 23.3 | 23.5 | $K12$ | 8.00 | S5
|  |  |  |  |  |  | 2019-05-09 | 29.6 | 27.9 | $K12$ | 8.00 | S5_2
|  |  |  |  |  |  | 2019-05-29 | 29.5 | 29.9 | $K12$ | 8.00 | S5_3
36 | 14 04 42.1 | -50 04 17 | 68781 | 7.38 | A2V | UCL | 2016-03-30 | 42.7 | 27.8 | $H23$ | 64.0 | SA18
37 | 14 06 08.2 | -44 41 21 | 68867 | 7.17 | A0V | UCL | 2017-06-24 | 32.0 | 22.0 | $K12$ | 16.0 | S53
38 | 14 06 58.2 | -47 35 21 | 68958 | 6.72 | Ap… | UCL | 2015-04-15 | 23.3 | 15.7 | $K12$ | 8.00 | S7
|  |  |  |  |  |  | 2018-03-15 | 27.7 | 16.8 | $K12$ | 16.0 | S7_2
39 | 14 24 37.0 | -47 10 40 | 70441 | 7.31 | A1V | UCL | 2016-07-23 | 42.7 | 25.2 | $H23$ | 32.0 | SA19
40 | 14 27 33.6 | -46 12 49 | 70697 | 7.17 | A0V | UCL | 2015-05-11 | 72.5 | 51.6 | $H23$ | 64.0 | SA20
41 | 14 28 51.9 | -47 59 32 | 70809 | 6.54 | Ap… | UCL | 2017-07-24 | 38.1 | 33.1 | $K12$ | 16.0 | S56
42 | 14 29 58.4 | -56 07 52 | 70904 | 6.39 | A6V | UCL | 2015-06-09 | 25.6 | 12.0 | $K12$ | 16.0 | S18
|  |  |  |  |  |  | 2017-05-15 | 32.0 | 14.7 | $K12$ | 16.0 | S18_2
43 | 14 30 10.0 | -43 51 50 | 70918 | 6.35 | A0/A1V | UCL | 2017-06-24 | 30.9 | 22.9 | $K12$ | 16.0 | S54

Note. — Survey IDs were assigned in numerical order as observations were
completed, except for those obtained from the archive (those beginning with
’SA’), which were assigned in order of increasing R.A. (Right Ascension). The
other abbreviations in the column headers are Dec. (Declination), SpT
(Spectral Type), and DIT (Detector Integration Time).

Table 3Targets $-$ Sco-Cen A-type Stars (Continued)

Target | R.A. | Dec. | HIP # | $K_{s}$ | SpT | Sub- | Date Obs. | Exp. Time | Field | Filter | DIT | Survey
---|---|---|---|---|---|---|---|---|---|---|---|---
# | (J2000) | (J2000) |  |  |  | group | (YYYY-MM-DD) | (min) |  Rot. (∘) |  | (sec) | ID
44 | 14 32 57.1 | -42 24 20 | 71140 | 7.13 | A7/A8IV | UCL | 2015-04-29 | 42.7 | 34.8 | $K12$ | 8.00 | S12
|  |  |  |  |  |  | 2019-06-29 | 29.9 | 25.3 | $K12$ | 8.00 | S12_2
45 | 14 34 33.4 | -46 18 17 | 71271 | 7.57 | A0V | UCL | 2017-05-29 | 65.1 | 48.1 | $H$ | 32.0 | SA21
46 | 14 40 19.3 | -45 47 38 | 71727 | 6.89 | A0p | UCL | 2015-04-06 | 21.1 | 24.0 | $K12$ | 8.00 | S3
|  |  |  |  |  |  | 2019-06-27 | 29.6 | 21.5 | $K12$ | 8.00 | S3_2
47 | 14 45 20.6 | -36 08 52 | 72140 | 7.09 | A1IV/V | UCL | 2016-04-06 | 25.6 | 30.7 | $K12$ | 16.0 | S34
|  |  |  |  |  |  | 2019-05-26 | 29.9 | 35.5 | $K12$ | 8.00 | S34_2
48 | 14 45 57.6 | -44 52 03 | 72192 | 6.71 | A0V | UCL | 2015-06-11 | 25.6 | 17.3 | $K12$ | 16.0 | S21
49 | 14 50 58.7 | -42 49 21 | 72627 | 6.53 | A2V | UCL | 2016-04-23 | 24.0 | 19.6 | $K12$ | 16.0 | S36
|  |  |  |  |  |  | 2019-05-26 | 29.9 | 23.4 | $K12$ | 8.00 | S36_2
50 | 14 54 25.3 | -34 08 34 | 72940 | 6.85 | A1V | UCL | 2015-06-12 | 25.6 | 36.8 | $K12$ | 16.0 | S22
|  |  |  |  |  |  | 2016-03-06 | 33.6 | 38.5 | $K12$ | 32.0 | S22_2
|  |  |  |  |  |  | 2016-03-17 | 29.9 | 36.6 | $K12$ | 32.0 | S22_3
|  |  |  |  |  |  | 2016-05-07 | 29.9 | 39.6 | $K12$ | 32.0 | S22_4
|  |  |  |  |  |  | 2017-05-31 | 27.7 | 23.1 | $Y23$ | 16.0 | S22_5
|  |  |  |  |  |  | 2017-07-22 | 27.7 | 3.89 | $H23$ | 16.0 | S22_6
|  |  |  |  |  |  | 2017-07-23 | 26.1 | 5.59 | $K12$ | 32.0 | S22_7
|  |  |  |  |  |  | 2017-07-23 | 24.5 | 3.78 | $Ks$ | 32.0 | S22_8
|  |  |  |  |  |  | 2017-07-24 | 27.2 | 10.7 | $J23$ | 16.0 | S22_9
|  |  |  |  |  |  | 2017-07-28 | 26.9 | 10.2 | $H23$ | 16.0 | S22_10
|  |  |  |  |  |  | 2018-03-14 | 27.5 | 38.9 | $H23$ | 16.0 | S22_11
|  |  |  |  |  |  | 2018-07-14 | 34.1 | 38.0 | $K12$ | 16.0 | S22_12
51 | 14 54 54.5 | -36 25 49 | 72984 | 7.05 | A0/A1V | UCL | 2018-03-16 | 40.0 | 52.4 | $K12$ | 16.0 | S61
52 | 14 56 54.5 | -35 41 44 | 73145 | 7.54 | A2IV | UCL | 2015-05-14 | 67.2 | 72.5 | $H23$ | 64.0 | SA22
53 | 14 59 54.6 | -46 14 53 | 73393 | 7.21 | A0V | UCL | 2015-07-17 | 25.6 | 14.2 | $K12$ | 16.0 | S29
|  |  |  |  |  |  | 2019-05-10 | 29.9 | 21.2 | $K12$ | 8.00 | S29_2
54 | 15 06 33.2 | -30 55 07 | 73937 | 6.05 | Ap | UCL | 2018-03-16 | 32.0 | 21.9 | $K12$ | 16.0 | S62
55 | 15 17 10.7 | -34 34 37 | 74797 | 7.55 | A2IV | UCL | 2015-04-29 | 23.5 | 33.9 | $K12$ | 8.00 | S13
|  |  |  |  |  |  | 2019-06-27 | 22.3 | 30.3 | $K12$ | 8.00 | S13_2
56 | 15 19 22.3 | -34 01 57 | 74985 | 7.53 | A0V | UCL | 2017-05-15 | 31.2 | 45.1 | $K12$ | 16.0 | S49
57 | 15 20 13.4 | -34 55 32 | 75056 | 7.31 | A2V | UCL | 2015-06-19 | 21.3 | 13.8 | $K12$ | 16.0 | S25
|  |  |  |  |  |  | 2019-06-29 | 28.5 | 41.6 | $K12$ | 8.00 | S25_2
58 | 15 20 31.4 | -28 17 14 | 75077 | 6.97 | A1V | UCL | 2016-04-03 | 16.4 | 25.7 | $H23$ | 4.00 | SA23
59 | 15 21 30.1 | -38 13 07 | 75151 | 6.65 | A+… | UCL | 2015-07-18 | 25.6 | 18.7 | $K12$ | 16.0 | S31
60 | 15 25 30.2 | -36 11 58 | 75509 | 7.40 | A2V | UCL | 2018-04-10 | 76.8 | 75.6 | $K12$ | 96.0 | SA25
61 | 15 30 48.4 | -45 25 28 | 75957 | 7.24 | A0V | UCL | 2015-07-12 | 41.9 | 27.8 | $K12$ | 16.0 | S28
|  |  |  |  |  |  | 2019-05-01 | 29.9 | 21.7 | $K12$ | 8.00 | S28_2
62 | 15 46 51.6 | -36 56 13 | 77295 | 7.64 | A2IV/V | UCL | 2017-05-15 | 32.0 | 32.2 | $K12$ | 16.0 | S50
63 | 15 48 52.1 | -29 29 00 | 77457 | 7.33 | A7IV | US | 2015-06-09 | 25.6 | 12.0 | $K12$ | 16.0 | S18
|  |  |  |  |  |  | 2017-05-15 | 32.0 | 14.7 | $K12$ | 16.0 | S18_2
64 | 15 56 47.9 | -23 11 03 | 78099 | 7.35 | A0V | US | 2015-05-09 | 27.7 | 8.19 | $H23$ | 64.0 | SA26
65 | 15 57 59.3 | -31 43 44 | 78196 | 7.08 | A0V | US | 2015-06-03 | 57.6 | 84.6 | $H23$ | 64.0 | SA27
66 | 16 01 26.6 | -25 11 55 | 78494 | 7.11 | A2m… | US | 2015-05-18 | 22.1 | 2.34 | $K12$ | 8.00 | S16
67 | 16 01 58.9 | -37 32 04 | 78533 | 6.99 | Ap | UCL | 2016-07-14 | 24.3 | 24.8 | $K12$ | 16.0 | S45
|  |  |  |  |  |  | 2019-05-12 | 36.3 | 32.1 | $K12$ | 8.00 | S45_2
68 | 16 02 04.8 | -36 44 38 | 78541 | 6.99 | A0V | UCL | 2015-05-19 | 23.5 | 12.5 | $K12$ | 8.00 | S17
69 | 16 04 44.5 | -39 26 05 | 78756 | 7.16 | Ap | UCL | 2016-04-23 | 25.1 | 18.4 | $K12$ | 16.0 | S37
|  |  |  |  |  |  | 2016-08-07 | 22.7 | 20.9 | $K12$ | 16.0 | S37_2
|  |  |  |  |  |  | 2017-05-29 | 31.5 | 28.3 | $K12$ | 16.0 | S37_3
70 | 16 05 43.4 | -21 50 20 | 78847 | 7.32 | A0V | US | 2015-06-17 | 25.6 | 2.59 | $K12$ | 16.0 | S24
71 | 16 05 46.3 | -39 50 36 | 78853 | 7.50 | A5V | UCL | 2016-04-23 | 19.5 | 23.9 | $K12$ | 16.0 | S38
72 | 16 07 29.9 | -23 57 02 | 78996 | 7.46 | A9V | US | 2015-06-19 | 25.6 | 0.558 | $K12$ | 16.0 | S26
73 | 16 11 52.7 | -22 32 42 | 79366 | 7.47 | A3V | US | 2017-07-28 | 29.9 | 2.65 | $K12$ | 16.0 | S59
74 | 16 18 05.5 | -31 39 06 | 79860 | 7.88 | A0V | US | 2015-07-31 | 18.7 | 37.6 | $K12$ | 16.0 | S33
75 | 16 18 16.2 | -28 02 30 | 79878 | 7.06 | A0V | US | 2016-07-02 | 40.4 | 17.9 | $H23$ | 8.00 | SA28
76 | 16 20 04.0 | -20 02 42 | 80019 | 7.08 | A0V | US | 2015-04-21 | 23.5 | 355. | $K12$ | 8.00 | S9
|  |  |  |  |  |  | 2019-05-31 | 29.9 | 36.2 | $K12$ | 8.00 | S9_2
77 | 16 20 28.1 | -21 30 32 | 80059 | 7.44 | A7III/IV | US | 2017-07-25 | 32.0 | 4.21 | $K12$ | 16.0 | S57
78 | 16 23 56.7 | -33 11 58 | 80324 | 7.33 | A0V | US | 2018-06-18 | 59.2 | 53.1 | $H23$ | 96.0 | SA29
79 | 16 25 35.1 | -23 24 19 | 80474 | 5.76 | A | US | 2017-07-27 | 32.0 | 0.252 | $K12$ | 16.0 | S58
80 | 16 27 14.6 | -39 49 22 | 80591 | 7.82 | A5V | UCL | 2015-04-05 | 20.4 | 30.0 | $K12$ | 8.00 | S2
|  |  |  |  |  |  | 2017-05-29 | 26.4 | 28.8 | $K12$ | 16.0 | S2_2
81 | 16 29 54.6 | -24 58 46 | 80799 | 7.46 | A2V | US | 2016-08-07 | 25.3 | 2.09 | $K12$ | 16.0 | S46

Note. — Continued from Table 2. Survey IDs were assigned in numerical order as
observations were completed, except for those obtained from the archive (those
beginning with ’SA’), which were assigned in order of increasing R.A. (Right
Ascension). The other abbreviations in the column headers are Dec.
(Declination), SpT (Spectral Type), and DIT (Detector Integration Time).

Table 4Targets $-$ Sco-Cen A-type Stars (Continued)

Target | R.A. | Dec. | HIP # | $K_{s}$ | SpT | Sub- | Date Obs. | Exp. Time | Field | Filter | DIT | Survey
---|---|---|---|---|---|---|---|---|---|---|---|---
# | (J2000) | (J2000) |  |  |  | group | (YYYY-MM-DD) | (min) |  Rot. (∘) |  | (sec) | ID
82 | 16 31 11.7 | -38 22 59 | 80897 | 7.78 | A0V | UCL | 2015-07-19 | 23.5 | 19.5 | $K12$ | 16.0 | S32
|  |  |  |  |  |  | 2017-05-28 | 32.0 | 29.7 | $K12$ | 16.0 | S32_2
83 | 16 34 10.5 | -38 23 25 | 81136 | 5.21 | A7/A8 | UCL | 2016-04-23 | 26.3 | 28.4 | $K12$ | 8.00 | S39
|  |  |  |  |  |  | 2019-05-01 | 29.9 | 32.3 | $K12$ | 8.00 | S39_2
84 | 16 41 52.0 | -40 27 58 | 81751 | 8.29 | A9V | UCL | 2015-04-29 | 23.5 | 22.0 | $K12$ | 8.00 | S14
|  |  |  |  |  |  | 2017-06-22 | 31.7 | 27.2 | $K12$ | 16.0 | S14_2
85 | 16 44 21.1 | -44 22 43 | 81949 | 7.32 | A3V | UCL | 2016-04-23 | 25.1 | 15.7 | $K12$ | 16.0 | S40
86 | 16 52 31.9 | -42 59 30 | 82560 | 6.58 | A0V | UCL | 2016-05-20 | 25.6 | 17.5 | $K12$ | 16.0 | S43
|  |  |  |  |  |  | 2019-04-28 | 29.9 | 24.1 | $K12$ | 8.00 | S43_2
87 | 17 03 22.2 | -40 05 16 | 83457 | 6.49 | A9V | UCL | 2015-04-15 | 23.2 | 19.4 | $K12$ | 8.00 | S8
|  |  |  |  |  |  | 2016-04-23 | 21.3 | 11.4 | $K12$ | 16.0 | S8_2
88 | 17 06 20.2 | -37 13 39 | 83693 | 5.69 | A2IV | UCL | 2015-04-23 | 21.9 | 28.0 | $K12$ | 8.00 | S10
|  |  |  |  |  |  | 2016-08-11 | 25.5 | 25.8 | $K12$ | 8.00 | S10_2
|  |  |  |  |  |  | 2017-05-29 | 30.1 | 32.1 | $K12$ | 16.0 | S10_3

Note. — Continued from Table 3. Survey IDs were assigned in numerical order as
observations were completed, except for those obtained from the archive (those
beginning with ’SA’), which were assigned in order of increasing R.A. (Right
Ascension). The other abbreviations in the column headers are Dec.
(Declination), SpT (Spectral Type), and DIT (Detector Integration Time).