# The Timescales of Star Cluster Emergence: The Case of NGC 4449

Timothy McQuaid Department of Astronomy, University of Massachusetts Amherst,
710 North Pleasant Street, Amherst, MA 01003, USA  Department of Astronomy,
New Mexico State University, Las Cruces, NM 88003, USA  Daniela Calzetti
Department of Astronomy, University of Massachusetts Amherst, 710 North
Pleasant Street, Amherst, MA 01003, USA  Sean T. Linden Department of
Astronomy, University of Massachusetts Amherst, 710 North Pleasant Street,
Amherst, MA 01003, USA  Matteo Messa INAF – OAS, Osservatorio di Astrofisica e
Scienza dello Spazio di Bologna, via Gobetti 93/3, I-40129 Bologna, Italy
Angela Adamo Department of Astronomy, Stockholm University, Stockholm, Sweden
Bruce Elmegreen Katonah, NY 10536 USA  Kathryn Grasha Research School of
Astronomy and Astrophysics, Australian National University, Camberra,
Australia  Kelsey E. Johnson Department of Astronomy, University of Virginia,
Charlottesville, VA, USA  Linda J. Smith Space Telescope Science Institute,
3700 San Martin Drive, Baltimore, MD 21218, USA  Varun Bajaj  Space Telescope
Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA

(Accepted April 11, 2024)

###### Abstract

We survey the young star cluster population in the dwarf galaxy NGC 4449 with
the goal of investigating how stellar feedback may depend on the clusters’
properties. Using Ultraviolet(UV)-optical-NearIR(NIR) photometry obtained from
the _Hubble Space Telescope_ , we have recovered 99 compact sources exhibiting
emission in the Pa$\beta$ hydrogen recombination line. Our analysis reveals
these sources possess masses $10^{2}<M_{\odot}<10^{5}$, ages 1–20 Myr, and
color excess E(B - V) in the range 0–1.4. After selecting clusters with mass
above 3,000M⊙ to mitigate stochastic sampling of the stellar initial mass
function, we find that our IR–selected clusters have a median mass of
$\sim$7$\times{10^{3}\text{M}_{\odot}}$ and remain embedded in their
surrounding gas and dust for 5–6 Myr. In contrast, line-emitting sources
selected from existing UV/optically catalogs have a median mass of
$\sim$3.5$\times{10^{4}\text{M}_{\odot}}$ and have cleared their surroundings
by 4 Myr. We further find that the environment in NGC 4449 is too low pressure
to drive these differences. We interpret these findings as evidence that the
clearing timescale from pre–supernova and supernova feedback is cluster
mass–dependent. Even in clusters with mass$\sim$7,000 M⊙, stochastic sampling
of the upper end of the stellar initial mass function is present, randomly
decreasing the number of massive stars available to inject energy and momentum
into the surrounding medium. This effect may increase the clearing timescales
in these clusters by decreasing the effectiveness of both pre–supernova and
supernova feedback; neither models nor observations have so far explored such
dependence explicitly. Future studies and observations with, e.g., the JWST,
will fill this gap.

Interstellar dust – Young star clusters – galaxies: dwarf –
galaxies:individual (NGC 4449) – galaxies: star clusters: general – galaxies:
ISM – (ISM:) dust, extinction

##  1 Introduction

Star formation occurs predominantly in clusters that emerge from the
gravitational collapse of massive clouds of molecular gas and dust. When a
molecular cloud cools sufficiently, its gravitational potential energy
overcomes the kinetic energy of the gas, forming overdensities which then feed
the formation of new stars (Krumholz et al., 2019). The formation of massive
stars is however complicated by the presence of numerous counteracting
mechanisms. While molecular gas, under the force of gravity, accretes onto a
forming star, the star simultaneously injects energy into its surroundings
(Pellegrini et al., 2011; Dale et al., 2012; Krause et al., 2013). This
energy, referred to as stellar feedback, comes in several forms including
stellar winds, photoionization, radiation pressure and supernovae explosions.
Feedback is able to counteract the force of gravity and expel the gas and dust
surrounding the region of star formation (Lucas et al., 2020; Grudić et al.,
2022). When enough material has been cleared, star formation effectively
ceases within the cluster. Furthermore, the energy and momentum injected into
the surrounding interstellar medium drives turbulence and limits the amount of
cold, dense gas that can collapse, thus contributing to the low star formation
efficiency(Hennebelle & Chabrier, 2011; Hopkins et al., 2012; Dobbs, 2015;
Goldbaum et al., 2016). The impact of feedback on star formation is so
significant that the conversion efficiency of turning molecular gas into stars
is on the order of a few % (Ostriker et al., 2010; Hopkins et al., 2014, 2020;
Grudić et al., 2018). Therefore, stellar feedback is not only critical to
regulating star formation on a local level, but on a galactic level as well
(Krumholz & McKee, 2005; Ceverino & Klypin, 2009; Dobbs et al., 2011; Krumholz
et al., 2019).

Massive star feedback is conventionally divided into pre–supernova (pre–SN)
feedback (photoionization, direct and indirect radiation pressure and stellar
winds) and supernova feedback. Pre–SN acts on short timescales, typically
$\lesssim$4 Myr (Pellegrini et al., 2011; Dale et al., 2012; Krause et al.,
2013; Krumholz et al., 2019), and helps clear the medium before supernova
explosions occur starting around 4 Myr (Leitherer et al., 2014). Some models
indicate that radiative feedback may be key for regulating star formation
within galaxies (Hopkins et al., 2020; Bending et al., 2022).

A number of prior observational studies have concluded that pre-SN feedback is
the main mechanism for regulating star formation in clusters; these
conclusions are drawn from measurements of the timescales necessary for the
clusters to emerge from their natal clouds and become optically visible.
Several of these studies have examined UV/optical photometry from the _Hubble
Space Telescope_ (HST) of young star clusters to derive clearing timescales
$<$4-5 Myr and as short as 2 Myr, in nearby galaxies (Whitmore et al., 2011;
Hollyhead et al., 2015; Hannon et al., 2019, 2022). However, these surveys,
which only utilize optical and UV observations, are inherently limited by the
effects of dust attenuation, and may miss highly dust-attenuated clusters,
i.e., the clusters most closely associated with their natal clouds. Several
other studies have attempted to remedy this by including CO data from
ground–based observatories or 24 $\mu$m observations from the _Spitzer Space
Telescope_ (Matthews et al., 2018; Grasha et al., 2018, 2019; Kruijssen et
al., 2019; Kim et al., 2021; Chevance et al., 2022). These studies have
similarly derived a short clearing time, 3-5 Myr. The use of CO data or
infrared images is, however, limited by low resolution. For instance, CO data
typically subtends 50 pc, far greater than the 3 pc radius of young clusters
(Ryon et al., 2017; Brown & Gnedin, 2021). Although Corbelli et al. (2017)
leverages the proximity of the Local Group galaxy M 33 (distance $\sim$840
kpc) to increase the spatial resolution of the Spitzer Space Telescope’s 24
$\mu$m images and conclude that the embedded phase of star formation is short,
$\sim$2 Myr, the subtended spatial scale is still 26 pc, much larger than a
single star cluster.

Messa et al. (2021) utilized HST near-UV to near-IR photometry to study the
cluster populations of the nearby galaxy NGC 1313. They found between
40-60$\%$ of young clusters ($<$6 Myr) are missed when selection is only based
on UV and optical data. Their analysis also extended the clearing time from
2-3 Myr to 3-4 Myr. Messa et al. (2021) is however restricted by the
requirement that sources still need to be detectable at optical wavelengths in
order to determine their ages and masses. Calzetti et al. (2023), using HST
optical and IR images of NGC 4449, attempted to obtain a sample of highly
extincted clusters by selecting sources that emit in Pa$\beta$ but are
undetected at optical and shorter wavelengths. The specific selection
requirements used in that study resulted in a limited number of sources (34)
with a narrow range of properties, which leaves open the question of whether
those authors’ approach includes potential biases.

The studies above, with their variety of timescales, hint at the possibility
that the clearing timescale may be influenced by the galactic environment
and/or by the cluster’s properties. The dependence of pre–SN feedback on
galactic environment has been investigated by McLeod et al. (2021), Della
Bruna et al. (2022) and Chevance et al. (2022) for a few local spiral
galaxies. These authors find that the galactic environment does have an
influence on the effectiveness of pre–SN feedback. While Chevance et al.
(2022) conclude that presence of morphological features introduce variations
in the pre–SN clearing timescales, McLeod et al. (2021) find a dependency on
galactocentric distance. McLeod et al. (2021) conclude that supernova feedback
is enhanced by the clearing done by pre–SN feedback in the outer, less dense
regions of galaxies. Similarly, Della Bruna et al. (2022) find that the HII
regions in the center of the starburst galaxy M83 are confined by the ambient
pressure, while they are over–pressurized and expanding in the disk.

In this work we extend the analysis of Messa et al. (2021) to the nearby
Magellanic Irregular dwarf galaxy NGC 4449. NGC 4449 is located at a distance
of 4.2 Mpc (Tully et al., 2013), implying that our HST nearIR imaging data
resolves regions as small as $\lesssim$2–3 pc, i.e., comparable to a cluster’s
size (Ryon et al., 2017; Brown & Gnedin, 2021). With a star formation rate of
approximately 0.5 M${}_{\odot}\ $ yr-1 and a stellar mass of $\sim$109 M⊙ ,
NGC 4449 is located about 3$\sigma$ above the Star Formation Main Sequence for
local galaxies (Cook et al., 2014) which qualifies it as a starburst. The
metallicity of the galaxy is about 40% solar111We adopt a value
12+log(O/H)=8.69 for the solar oxygen abundance (Asplund et al., 2009). and
has a modest galactocentric gradient (Berg et al., 2012; Pilyugin et al.,
2015). There are several similarities between NGC 4449 and NGC 1313 (the
latter studied by Messa et al., 2021). The two galaxies have comparable
distances, oxygen abundances and specific SFRs (SFR/M∗) (see, Messa et al.,
2021). Within a factor $\sim$3, their masses are comparable to that of the
Large Magellanic Cloud. However, they differ in their SFR surface density,
with NGC 4449 having $\Sigma_{SFR}=0.03$ M⊙ yr-1 kpc-2, or three times the SFR
surface density of NGC 1313 in the regions targeted by HST (Messa et al.,
2021; Calzetti et al., 2023).

We leverage the same HST UV–to–nearIR imaging data analyzed in Calzetti et al.
(2023) (Figure 1), but adopt a more general selection process than those
authors to recover a larger sample of young star clusters, with a greater
diversity of ages, masses and extinctions. We select our sources based on the
presence of ionized gas emission identified from the infrared hydrogen
recombination line Pa$\beta$ ($\lambda$1.282 $\mu$m); this ensures that our
sources are typically young, $\lesssim$6–7 Myr, since beyond 7 Myr the ionized
gas emission is too faint to be detected (Leitherer et al., 1999). However, we
do not impose explicit constraints on mass or extinction, thus mitigating
selection biases on these parameters.

The paper is structured as follows: section 2 contains a description of the
imaging data, section 3 describes the construction of the source catalog, and
section 4 details the cross–validation of the sources using a JWST image of
NGC 4449. Section 5 describes the multi–wavelength flux measurements using
aperture photometry for the sources. The stellar population and dust
attenuation models are described in section 6. Results and discussion are in
section 7, while the summary and conclusions are in section 8.

Figure 1: A three color composite of NGC 4449 (blue=F336W, green=F814W,
red=Pa$\beta$ emission). The locations of the 99 cluster candidates are marked
by yellow circles. Due to the requirement that sources be detected in
Pa$\beta$, all sources lie within the field of view of the F128N filter,
traced by the white line. North is up, East is left.

##  2 Imaging Data

The data used in this survey is comprised of HST imaging covering from nearUV
(NUV) to nearIR (NIR) in 10 bands. With the WFC3/UVIS and ACS instruments, we
sample the NUV to optical range with broad-band filters (F275W, F336W, F435W,
F555W and F814W), a H${\alpha}$+[NII] narrow-band filter (F657N), and a
medium-band filter sampling the line-free continuum at V (F550M) (Table 1).
Further, with WFC3/IR we sample the NIR with two broad-band (F110W and F160W)
and one narrow-band (F128N) filter to cover the Pa${\beta}$ hydrogen
recombination line emission. In summary, the 10 filters spanning the NUV–NIR
include two hydrogen recombination lines (Table 1). For each IR filter, the
standard calibration pipeline CALWFC3 v. 3.5.2 was used to process individual
frames into the final images, after correction for bias, dark, and flat fields
(Messa et al., 2021). The NUV and optical images were retrieved from the
MAST222Mikulski Archive for Space Telescopes,
https://archive.stsci.edu/missions-and-data/hst archive already processed
through their respective instrumental pipelines. All images were aligned and
mosaiced to the Gaia DR2 reference frame and then resampled to 0.04”/pix
(NUV/optical) or 0.08”/pix (NIR), using DrizzlePac. The NIR images are
resampled to twice the pixel size of the NUV/optical images because the Point
Spread Function (PSF) of the WFC3/IR images is twice that of the optical and
UV images. At the distance of NGC 4449, the pixel scale of 0.04” corresponds
to 0.81 pc. Using the header keyword PHOTFLAM, images were then converted from
instrumental units (e-/s) to physical flux units (erg/s/cm2/Å). Table 2
contains each filter’s respective pivot wavelength and FWHM. Since the WFC3/IR
images have the narrowest field of view of the sample, the source search area
of NGC 4449 is limited by their coverage (Figure 1). This area covers the
central 2.8$\times$2.4 kpc2 of NGC 4449 and captures 67% of the total SFR of
the galaxy.

We produce emission line images by subtracting stellar continuum from the
narrow-band images, F658N and F128N. For the F658N image (H$\alpha$+[NII]), we
use the F550M and F814W images to perform an interpolation of the continuum
flux. For the F128N (Pa$\beta$) image, we use the F110W and F160W images for
the interpolation and the resulting continuum maps are then scaled using
isolated foreground stars within the images which do not have a contribution
from any nebular emission. The presence of Pa$\beta$ in the F110W filter
requires the subtraction to be performed iteratively to adequately remove the
continuum from the final line image. After producing the first F128N line
emission map, we subtract this map from the original F110W image to create a
line-free 1.1um continuum image. We then repeat the interpolation, scaling,
and subtraction process until the final image differs from the previous
version by less than 1%; this is achieved with two iterations. The continuum
subtracted images are then multiplied by their filter widths, given by their
full width at half maximums, and corrected for the filter’s transmission at
the galaxy’s redshift to obtain line fluxes. The line emission in the F658N
image is corrected for the [NII] contribution using a [NII]/H$\alpha$ of 0.11
for NGC 4449 (Berg et al., 2012).

Table 1: Data Sources

Instrument1 | Filters2 | Exposure Times3 | Proposal ID4
---|---|---|---
WFC3/UVIS | F275W, F336W | 2481, 2360 | 13364
ACS/WFC | F435W, F550M, F555W, F658N, F814W | 7140, 1200, 4920, 2260, 4660 | 10522, 10585
WFC3/IR | F110W, F128N, F160W | 1000, 2600, 1700 | 15330

11footnotetext: WFC3/UVIS=Wide Field Camera 3 UV–Visual channel. ACS/WFC=
Advanced Camera for Surveys Wide Field Channel. WFC3/IR=Wide Field Camera 3
Infrared channel.

22footnotetext: HST Filter names.

33footnotetext: Exposure time for filter in seconds.

44footnotetext: ID number of the GO program that obtained the images: GO–13364
(LEGUS, Legacy ExtraGalactic UV Survey), PI: Calzetti; GO–10522, PI: Calzetti;
GO–10585, PI: Aloisi; GO–15330, PI: Calzetti.

Table 2: Filter Table HST Filter | Pivot $\lambda$ | FWHM | Standard Filter
---|---|---|---
|  $\mu$m |  $\mu$m |
F275W | 0.2710 | 0.04053 | NUV
F336W | 0.3355 | 0.05116 | U
F435W | 0.4329 | 0.06911 | B
F550M | 0.5581 | 0.03845 | Medium V
F555W | 0.5360 | 0.08478 | V
F658N | 0.6584 | 0.00875 | H$\alpha$+[NII]
F814W | 0.8048 | 0.15416 | I
F110W | 1.1534 | 0.44300 | J
F128N | 1.2832 | 0.01590 | Pa$\beta$
F160W | 1.5369 | 0.26830 | H

##  3 Source Catalog

Within the area of NGC 4449 determined by the field of view of the NIR
pointing (Figure 1), we identify sources that have compact Pa${\beta}$
recombination line emission, therefore, are potentially young star clusters.
This approach is similar to the one employed by Messa et al. (2021). Those
authors use a second method for specifically isolating dusty sources by
searching for high–extinction compact sources in extinction maps. Here we only
concentrate on the compact Pa$\beta$ method, because we are interested in
isolating dusty sources that are also young. There is considerable overlap
between the catalogs selected with the two methods: 65% of the sources from
the extinction map method are in common with the sources from the compact
Pa$\beta$ method; the latter method is also very efficient, since it retrieves
84% of the sources in common between the two catalogs (Messa et al., 2021).

Star cluster candidates are extracted from both the ACS F814W image and the
continuum-subtracted Pa${\beta}$ line emission image at their respective pixel
scale (0.04”/pix and 0.08”/pix, respectively). We use SExtractor (Bertin &
Arnouts, 1996) to perform the source extraction. Input parameters are
optimized to identify sources with a $\geq$5$\sigma$ detection in 5 and 3
contiguous pixels in the F814W and F128N, respectively. This identifies
224,342 and 1240 sources in the F814W and Pa${\beta}$ images, respectively. We
then match the two catalogs within a 2 pixel (0.08” or 1 pixels in the IR
channels) tolerance, retaining 1119 common sources. Visual checks were
performed to ensure that the low tolerance was not discarding potential
matches. This initial catalog is then inspected by eye, using the full suite
of images. Apart from image artifacts and edge detections, the main source of
contamination in this catalog are poorly–subtracted sources which appear as
detections in our Pa${\beta}$ line map with Signal–to–Noise ratio S/N$\geq 5$
relative to the locally–measured background. In regions where the stellar
continuum is over(under)–subtracted, the sources that are not perfectly
subtracted will be preferentially removed (retained) in our catalog. These
sources are straightforward to visually identify and remove from the catalog
as they include negative pixel values and live in regions where the overall
background is over-subtracted. Since these false detections arise due to poor
continuum subtraction rather than genuine Pa$\beta$ emission, omitting them
does not bias our catalog against additional young clusters, nor does the
issue of over-subtraction mask any real sources. We also remove sources whose
peak Pa$\beta$ emission does not appear coincident with the peak stellar
continuum emission, since these may be the result of several sources in
superposition in a compact association or may be slightly evolved sources that
have ejected their surrounding gas. After this visual inspection we retain 464
sources.

##  4 Confirmation of Sources with JWST Data

We cross reference the locations of the remaining 464 sources to a
stellar–continuum subtracted Pa$\alpha$ ($\lambda$ 1.8756 $\mu$m) line
emission map acquired with JWST. The JWST Pa$\alpha$ image has been obtained
using the NIRCam F187N filter, continuum–subtracted using adjacent broad–band
filters (private communication, A. Adamo 2023). This image has double the
angular resolution and is deeper than our HST/WFC3/IR images, thus providing a
sanity check for our nebular emission detections. The Pa$\alpha$ hydrogen
recombination line serves as an additional marker for young star clusters
while being less susceptible, by virtue of being at longer wavelength, to the
effects of dust attenuation than Pa$\beta$. We perform a visual inspection of
the Pa$\alpha$ image at the location of our sources, discarding all sources
that may be poorly subtracted stars. In addition, we impose a detection limit
cut–off and only deem acceptable those sources that are detected in Pa$\alpha$
with S/N$\geq$3\. This last criterion removes 167 faint emission line sources,
which may be either old clusters or young low–mass ones. We do not expect that
discarding low–mass young clusters will affect our conclusions, which are
based on sources with mass$\geq$3,000 M⊙ (see section 7). We note that we do
not recover any of the sources identified by Calzetti et al. (2023); none of
the 34 sources selected by those authors show evidence for emission in
Pa$\alpha$, although they are sufficiently compact to be indistinguishable
from stellar sources and therefore part of the stellar population in this
galaxy. After applying both of our selection criteria, we are left with 99
sources (21% of the original sample) that display Pa$\alpha$ emission, with
$>$90% of them detected with S/N$>$7 in the line. This is the sample our
analysis centers on.

##  5 Photometry

Aperture photometry is performed on each of the 99 cluster candidates. We
adopt a fixed 3/1.5 (NUV+optical/NIR) pixel radius (0′′.12=2.44 pc) to ensure
that photometry is not affected by nearby contaminants. A local sky annulus in
the range 5-7/2.5-3.5 (NUV+optical/NIR) pixels is used for the background
subtraction of each source. Prior to computing the photometry, each source is
centered on the F814W using a centroiding algorithm and fixed in all filters
to this point. The F814W is used as reference since it is our reddest optical
band, with a point spread function (PSF) roughly half the size of our NIR
bands. This makes the F814W ideal as a reference since it can capture the flux
of highly extincted sources compared to shorter bands while still having
sufficient angular resolution to differentiate between the source and nearby
contaminants.

We account for flux outside of the fixed aperture by applying aperture
corrections which were determined using the methods described in Messa et al.
(2021). Briefly, to estimate the corrections, we convolve isolated stellar
sources in each band with a Moffat profile, considered to be the most accurate
light profile function for describing clusters (Bastian et al., 2013). We use
a R${}_{\textrm{eff}}$ value of 2.5 pc, which is the peak of the cluster
radius distribution (Ryon et al., 2015, 2017), to obtain an average correction
in each band for all clusters. Although the choice of R${}_{\textrm{eff}}$
affects the normalization of the SED, it does not affect its overall shape.
Thus, when fitting the SEDs to models, the only physical property that is
affected is the resulting mass of the cluster. In addition, Messa et al.
(2021) found that the resulting change in mass distribution between
R${}_{\textrm{eff}}$ values of 1.3 pc (the average radius of clusters in their
sample) and 2.5 pc is minimal. We apply the following corrections (all
expressed in AB mag): -1.54±0.09 for F160W, -1.52±0.09 for F128N, -1.39±0.09
for F110W, -1.19±0.08 for F814W, -1.16±0.08 for F658N, -1.13±0.08 for F555W,
-1.08±0.08 for F550M, -1.13±0.08 for F435W, -1.03±0.08 for F336W, -1.04±0.08
for F275W. Finally, we correct the photometric measurements for foreground
galactic extinction with the MW foreground extinction, E(B–V)=0.017. Total
uncertainty is derived from a combination of photon noise, uncertainty in
aperture corrections, and the standard deviation of the background. We check
for relative (band–to–band) photometric accuracy between broad and narrow–band
filters by measuring several stellar sources in the field of NGC 4449. The
stellar sources do not show, or in a few cases only have non–significant ($<$1
$\sigma$), emission lines, either in H$\alpha$ or Pa$\beta$, indicating that
the narrow–band filters are accurately calibrated within a few percent, and we
should not expect to detect ‘spurious’ emission lines due to mis–calibrations.
Photometry of the 99 sources is listed in Table 3 and their location is shown
in Figures 1 and 4.

Figure 2: Minimum ${\chi}^{2}_{red}$ versus age of 99 fitted sources. The
black dashed line denotes a $\chi^{2}_{red}<10$ cutoff below which there are
80 sources. The 1$\sigma$ uncertainties in age are calculated from a 1$\sigma$
interval from the minimum ${\chi}^{2}_{red}$. Figure 3: 4 SEDs of sources with
$\chi^{2}_{red}<10$ exhibiting a range of ages and extinctions. The red X
marks the best fit value. Open circular markers are upper limits and solid
circular markers are detections. (Upper Left) 1 Myr/E(B-V)=0.12mag. (Upper
Right) 6 Myr/E(B-V)=0.0mag. (Lower Left) 6 Myr/E(B-V)=1.1mag. (Lower Right)
9000 Myr/E(B-V)=0.04mag.

##  6 Models and Fitting

We fit our photometry to synthetic models to derive physical properties of the
cluster candidates including age, mass, and extinction. We utilize Yggdrasil
SED models (Zackrisson et al., 2011) in combination with dust
attenuation/extinction models to produce synthetic photometry similar to the
procedure followed in Calzetti et al. (2015a), Adamo et al. (2017), Messa et
al. (2021), and Calzetti et al. (2023).

Yggdrasil generates single stellar population (SSP) models by utilizing the
Starburst99 (Leitherer et al., 1999) spectral synthesis models and then
combining these models with Cloudy (Ferland et al., 2013), to generate the
nebular emission lines (Zackrisson et al., 2011). The Starburst99 models
already include both stellar and nebular continuum.

The SSP models we use are generated assuming instantaneous star formation, a
Kroupa Initial Mass Function (IMF) (Kroupa, 2001) in the range 0.1-120 M⊙, and
metallicity of Z=0.008, consistent with the metallicity of NGC 4449, which is
about 40% of the solar value (Berg et al., 2012). Ages are sampled in 1 Myr
increments between 1 Myr and 10 Myr, with increasingly sparser age sampling
out to 10 Gyr. We discuss later the impact of stochastic sampling of the
stellar IMF on our source parameters determinations (Cerviño et al., 2002).

The SSP SEDs are attenuated with both a starburst attenuation curve (Calzetti
et al., 2000) and an LMC extinction curve (Fitzpatrick, 1999). We use a
foreground dust geometry (Calzetti, 2001) of the form:

$F(\lambda)_{final}=F(\lambda)_{model}10^{[-0.4E(B-V)\kappa(\lambda)]},$ (1)

where E(B-V) is the color excess and $\kappa(\lambda)$ is the
attenuation/extinction curve. The models range in E(B-V) from 0–5 mag with
increments of 0.02 mag. The dust-attenuated SED models are then convolved with
the HST filters’ transmission curves to produce synthetic photometry.

We then fit the SEDs derived from aperture photometry to these synthetic
photometry models using ${\chi}^{2}$-minimization, taking into account the
uncertainties in each band as well as upper limits. A source must possess at
least 3 bands with $\sigma$$<$0.3 for the source to be fit. To account for
highly extincted sources with weak, highly uncertain detections at shorter
wavelengths, we utilize the uncertainty in the F555W as a reference point. If
$\sigma_{F555W}$ exceeds 0.3 mag, we treat the F555W and all shorter
wavelength bands as upper limits during the fitting process. This approach
resolves the issue of poor fits caused by a lack of detection at shorter
bands. With this criteria we were able to successfully fit all of the 99
sources.

Fitting returns a distribution of reduced-$\chi^{2}$ ($\chi^{2}_{red}$) values
for each model and its corresponding physical properties. We take the model
with the minimum $\chi^{2}_{red}$ to be the best fit. With this, we find that
the models with the LMC extinction curve produce better fits than the models
with the starburst attenuation curve, in agreement with the findings of
Calzetti et al. (2023). We thus adopt the models with the LMC extinction as
our reference models. We determine an uncertainty interval for the best-fit
parameters of each source to be all fits within 1$\sigma$ of the minimum
$\chi^{2}_{red}$ value, a corresponding $\chi^{2}_{red}$ interval of 3.53
based on our 3 degrees of freedom. The list of cluster properties, ages,
masses and extinctions, with 1 $\sigma$ uncertainties and the value of the
minimum $\chi^{2}_{red}$ from the fits is given in Table 4 for all 99 sources.

Figure 2 illustrates the resulting distribution of best-fit ages and minimum
$\chi^{2}_{red}$ of our sample. We define a minimum $\chi^{2}_{red}$ cutoff of
10 to ensure a well-fit sample, following the approach of Adamo et al. (2017).
Of the 99 sources with sufficiently accurate photometry to be fit with our
method (see above), 80 sources possess $\chi^{2}_{red}<10$. Figure 3
illustrates four SEDs and their best-fits with $\chi^{2}_{red}<10$. These four
sources span the whole parameter space of age and color excess in our sample.
The location of the 80 sources with $\chi^{2}_{red}<10$ is shown in Figure 4
(upper–right panel).

Figure 4: Three color composites of NGC 4449 showing each stage of the sample
selection, with the sources marked with yellow circles. (Upper Left) Full set
of successfully fitted sources (99 sources). (Upper Right) Successfully fitted
sources with $\chi^{2}_{red}<10$ (80 sources). (Lower Left) Successfully
fitted sources with M$>$3000 M⊙ (22 sources). (Lower Right) Successfully
fitted sources with both $\chi^{2}_{red}<10$ and M$>$3000 M⊙ (15 sources).

##  7 Results and Discussion

We analyze the correlation between physical properties obtained from the SED
fitting, presenting samples selected with different cuts, and discuss their
implications.

Figure 5 illustrates the age and color-excess relation of the sources in our
sample, using both the entire sample of 99 sources (left) and the sub–sample
of 80 sources with $\chi^{2}_{red}<10$ (right). We observe a plume of sources
ranging in E(B-V) from 0 mag to $\sim$1.4 mag centered at 5-7 Myr. We also
observe a limited number (16 total, 14 with $\chi^{2}_{red}<$10) of sources
with ages $\leq$3 Myr, none of which possesses E(B-V) exceeding 0.56 mag. The
overall shape of both the full sample and the one with $\chi^{2}_{red}<10$ are
qualitatively similar.

The age–mass plots (Figure 6) show that the 5-7 Myr plume has best-fit masses
in the range $\sim$102-104.2 M⊙ with higher masses, up to 105.5 M⊙, for the
few $>$20 Myr sources. The low mass end is consistent with the mass limit we
can reach given the depth of our images. We derive the mass limit as a
function of age from the I-band detection limit, and its track is shown in
Figure 6.

While young ($\lesssim$6 Myr), moderately dusty star clusters tend to have
strong Pa$\beta$ line emission, which is well modeled by our fitting routine
(top panels of Figure 3), the fitting routine has difficulty discriminating
between the SED of a dusty, $\sim$6–8 Myr old cluster (Figure 7) and the SED
of a much older, but low dust, cluster, leading to some degeneracy in the
fits; this is clearly illustrated in the two bottom panels of Figure 3, where
a dusty 6 Myr SED (left) and an almost–dust–free 9000 Myr SED (right) are
shown. Four sources in our sample (three with $\chi^{2}_{red}<10$) are older
than 20 Myr and as old as $\sim$10 Gyr, according to their best fits. The
requirement that sources be detected in Pa$\beta$ line emission with
S/N$\geq$5 in SExtractor is intended to break the degeneracy between ”young
and dusty” and ”old and low–dust” sources. Further, a total of 10 sources with
$\chi_{red}^{2}<$10 have large age uncertainties ($>$1 Gyr); these sources are
all 6 Myr and older in age, including the three sources with best fit ages
$>$20 Myr, again demonstrating the limitations of the fitting routine when
emission lines are weak. An additional consideration is that clusters around
the 6–10 Myr age range can contain red supergiants whose brightness can
over–shine their host cluster, further diluting evidence for a young stellar
population (see discussion in Whitmore et al., 2020; Calzetti et al., 2023).
Thus, the four sources with best fit age $>$20 Myr may be younger than the
best fits indicate. For sake of simplicity, in what follows we neglect further
consideration of the four sources with best fit ages older than 20 Myr,
reducing our sample to 95 sources.

We test for biases in the SED fit results due to potential chance alignment
of, e.g., a red supergiant star in front of an otherwise low–dust star cluster
(e.g., Whitmore et al., 2020). As red supergiants appear around $\sim$6 Myr of
age, this configuration would produce an unusually red SED which may be
mistaken for a dusty one. We test this possibility by performing spot-checks
on our sample: we measure the extinction values from the line emission ratio
H$\alpha$/Pa$\alpha$ for three clusters with E(B$-$V)$>$1 mag (# 13, 45, and
82), finding that the nebular emission yields E(B$-$V) values comparable to
those obtained from the SED fits. We conclude that the results from the SED
fits are robust in terms of the physical parameters they produce.

To mitigate the effect of stochastic sampling of the IMF on the sources’
derived physical parameters, we also define a 3000 M⊙ mass cutoff which
retains 22 sources with any $\chi^{2}_{red}$ and 15 sources with
$\chi^{2}_{red}<10$ (Figure 4). The age and color-excess of the resulting
sample are presented in Figure 8. The 5-7 Myr plume is still visible, but the
sample only contains three (one) sources with age of 4 Myr or less.

###  7.1 Where are the clusters younger than 5 Myr?

Our source extraction parameters (detection in I-band and Pa$\beta$ line
emission) were chosen to ensure an unbiased sample of young clusters. Despite
this, we recovered relatively few very young ($\leq$4 Myr) sources and just
one with mass exceeding 3000 M⊙ for the $\chi^{2}_{red}<$10 sample. In order
to understand whether our selection method is biased against these very young
and very dusty (E(B–V)$>1$ mag) clusters, we perform a by–eye search of
line–emitting sources that are not already in our catalog. The requirement to
have line emission ensures that the sources are young. In order to maximize
the likelihood that the sources are also dusty, we perform the search around
detected radio sources that are located within infrared–emitting regions. The
radio sources are from the collection of Reines et al. (2008), who have
identified compact radio–emitting regions in NGC 4449 with an accuracy of
$\sim$1′′. Radio emission is less subject to dust extinction than optical and
near–infrared wavelengths, so it can ensure an unbiased sample. The only
shortcoming is that radio observations are limited to bright regions, i.e.,
massive star clusters. However, we search in the surroundings of these bright
clusters for fainter (lower mass and/or more extincted) clusters. The infrared
images we use to locate dusty regions are from the IRAC instrument on the
_Spitzer Space Telescope_ , centered at 8 $\mu$m, a wavelength that targets
dust emission; we use here the image presented in Calzetti et al. (2018). Our
by–eye search yields 32 sources, only 18 of which are new, i.e., not already
in our sample. Of these, 13 were successfully fit, and only a single source
has a best-fit $\chi^{2}_{red}<10$ and mass $>$3,000 M⊙, but it is relatively
unextincted with an E(B-V)=0.28 mag. The location of this source on the
E(B–V)–age plot is shown in Figure 9. Of the remaining 12 fitted radio
sources, 10 are low mass ($<$3,000 M⊙) and 2 have poor $\chi^{2}_{red}$$>$15
and low E(B-V). These findings illustrate that our ability to recover both
young and dusty sources in NGC 4449 is not limited by our HST WFC3 UVIS/IR
observations, whether through visual inspection or automated searches.

We investigate whether our choice of models may drive some of these results.
Whitmore et al. (2020) compares the Yggdrasil models (Zackrisson et al., 2011)
with the Bruzual & Charlot models (Bruzual & Charlot, 2003), finding that the
two sets of models generally yield comparable ages and masses. When
discrepancies occur, they are in the direction of Yggdrasil assigning older
ages ($>$40 Myr) to clusters that the Bruzual & Charlot models yield young
ages ($<$10 Myr) for. We believe this should not affect our sample, which is
selected to have nebular line emission and, thus, young ages by default. To
verify this, we fit a segment of our sources using the fitting code CIGALE
(Boquien et al., 2019), which includes the Bruzual & Charlot models and
nebular emission, both lines and continuum. We focus on the 11 sources with
$\chi^{2}_{red}<10$, masses $>$3000 M⊙, and ages between 5 and 20 Myr. The
best fits we obtain from CIGALE indicate shifts towards marginally younger
ages, down to 5 Myr, increased E(B-V) values up to 3 mag, and up to a
$\sim$2.5 factor increase in mass. This test demonstrates that the fundamental
picture remains consistent, marked by an absence of very young ($<$5 Myr),
extincted sources.

In addition, similar tests performed in Calzetti et al. (2023), using the
stellar population inference code Prospector (Johnson et al., 2021), yielded
no major changes or systematics in the ages/masses/extinctions of the sources
relative to the results from Yggdrasil. In light of this discussion, we
conclude that the choice of models/codes does not have a major impact on our
ages and extinction values.

The considerations above leave us with two possibilities: that compact and
dusty star clusters with age $\lesssim$4 Myr are either absent in NGC 4449 or
have been somehow missed by our searches. To test either of these
possibilities we investigate existing results on both the star formation
history (SFH) of NGC 4449 and the optically–selected young star clusters from
the LEGUS project (Calzetti et al., 2015b).

The SFH of NGC 4449 has been derived by McQuinn et al. (2010), McQuinn et al.
(2012) and Sacchi et al. (2018), using resolved stellar populations detected
in the ACS/WFC optical images from the Hubble Space Telescope, and by Cignoni
et al. (2018), using both UV (WFC3) and optical (ACS/WFC) images. All authors
recover similar shapes for the SFH, although there are differences in the
details, especially in the youngest age bins. We concentrate on the results of
Sacchi et al. (2018), as these authors include some of the most recent stellar
evolution models in their analysis (similarly to Cignoni et al., 2018) and
divide the galaxy in sub–regions, helping a better match with our WFC3/IR
footprint. Our footprint coincides with the ‘Center’ region and part of the
‘Clump1’ and ‘Clump2’ regions identified by Sacchi et al. (2018). Sacchi et
al. (2018) finds that those central regions experienced a mild burst of star
formation, that begun about 20 Myr ago, ended about 5–6 Myr ago and peaked
around 14 Myr ago. Since the end of this burst, about 5–6 Myr ago, star
formation in the central region of NGC 4449 has been negligible, as inferred
from resolved star populations. This result is consistent with the one
obtained by Cignoni et al. (2018) who recovered the SFH of the whole galaxy
over the past $\sim$200 Myr. However, as the earliest stages of star formation
produce clustered systems (unresolved star clusters and associations, see Lada
& Lada, 2003; Elmegreen, 2010; Elmegreen & Hunter, 2010) analyses that
concentrate on resolved stars may miss such sources, and underestimate the SFH
in the youngest age bins and specifically over the most recent few Myr.

To further test this explanation, we investigate the young star clusters
identified in NGC 4449 by the LEGUS project (Calzetti et al., 2015b) and
discussed in Calzetti et al. (2023). The LEGUS star cluster candidates were
selected from UV,U,B,V,I images obtained with the WFC3/UVIS and ACS/WFC
instruments on the HST. Whitmore et al. (2020) added candidates to the NGC
4449 cluster catalog based on inspection of the HST H$\alpha$ images. The
requirement that a cluster needs to be detected in at least four bands in
order to be attributed an age, mass and extinction from the SED fitting of its
photometry constrains the selection to clusters that are detected in at least
the UV (WFC3/F275W) or U (WFC3/F336W) (Adamo et al., 2017). This sets also an
upper limit to the maximum extinction of the clusters, determined by Whitmore
et al. (2020) to be E(B-V)$\lesssim$1 mag. The approach used by Adamo et al.
(2017) to the SED fitting is similar to the one adopted in this work, enabling
a direct comparison. Calzetti et al. (2023) isolated 53 star cluster from the
LEGUS catalog, with the following characteristics: mass$\geq$3,000 M⊙,
age$\leq$10 Myr, and $\chi^{2}_{red}\leq$10 from the SED fits.

We inspected the location of these 53 star clusters on the narrow–band images
H$\alpha$ and Pa$\beta$ to identify all LEGUS young clusters that are
coincident with hydrogen recombination line emission, either compact or
filamentary. We found only 21 such star clusters. The physical parameters
(age, mass, extinction) of these 21 LEGUS cluster are plotted in Figures 6 and
9, where we include the small offset in mass due to the different choices in
the distance of NGC 4449 (3.9 Mpc for LEGUS versus 4.2 Mpc in this work). An
interesting property of the LEGUS line–emitting clusters is that they are all
$\leq$4 Myr old and they extend to high masses, with median mass
$\sim$3.5$\times$104 M⊙. For comparison, the high–fidelity
($\chi^{2}_{red}\leq$10) Pa$\beta$–emitting clusters we identify in our sample
with mass $\geq$3,000 M⊙ and age $\leq$10 Myr (8 clusters) have ages
$\gtrsim$5 Myr (with one at 4 Myr age) and median mass $\sim$7$\times$103 M⊙,
albeit with large uncertainties. Thus, our Pa$\beta$–emitting compact clusters
are both systematically older and are a factor $\sim$5 less massive than the
line–emitting clusters in the LEGUS sample.

Figure 10 shows that the $\leq$10 Myr old, Pa$\beta$–emitting sources are also
on average dustier than the line–emitting LEGUS sources, as expected from the
selection criteria of each sample. While both sets of sources can have low
extinction values, the LEGUS clusters have a maximum E(B$-$V)=0.85 mag, with a
median value $\bar{E}(B-V)$=0.4 mag, while the Pa$\beta$–emitting sources in
our sample have maximum E(B$-$V)=1.44 mag with median $\bar{E}(B-V)$=1.04 mag
or a little over twice that of the LEGUS clusters. The age–dust degeneracy may
slightly affect the mass assigned to a source if the age is incorrect. Much of
this uncertainty is captured in the 1$\sigma$ uncertainties reported for our
sources. For reference, in the age range 1–6.5 Myr, the mass inferred from the
I–band varies by 0.33 dex and from the H–band by 0.19 dex, too small of a
variation to affect the results presented in Figure 10.

In addition to differences in median age, mass, and extinction, the two
samples of star clusters also differ in numbers. We find 21 line–emitting
LEGUS clusters and 8 Pa$\beta$–emitting clusters that are $\leq$10 Myr old,
$\geq$3,000 M⊙ and with SED fits yielding $\chi^{2}_{red}<10$. However, when
limiting the comparisons to the same mass range (3,000–15,000 M⊙) for both
samples, the numbers become comparable, since there are only 10 LEGUS clusters
in this mass interval. Thus, there are no major discrepancies in the numbers
recovered in the two samples, when the mass range is controlled for. One
implication is that about half of the LEGUS clusters that are still retaining
their gas are more massive than 15,000 M⊙, but they require larger dust column
densities ($\bar{E}(B-V)$=0.54 mag) than their lower mass counterparts
($\bar{E}(B-V)$=0.28 mag, Figure10) to do so.

Why have we missed the LEGUS young clusters in our catalog? The line–emitting
LEGUS star clusters are typically more extended and/or too faint in Pa$\beta$
than allowed by our selection criteria, leading to these clusters being missed
by our detection algorithm. In addition, many clusters are coincident with
filamentary and/or diffuse line emission and are excluded by our matching
criteria.

We now consider the sample of young, UV/optically–selected sources derived by
Whitmore et al. (2020), who use an approach to the SED fits that differs from
the default LEGUS one (Adamo et al., 2017). In Whitmore et al. (2020), the
five–band LEGUS photometry is fit with Bruzual & Charlot models (Bruzual &
Charlot, 2003), but without inclusion of nebular continuum or line emission;
the authors add a prediction for the H$\alpha$ emission from the ionizing
photons flux in the models, that they use to fit the photometry in the F658N
filter. With these assumptions, Whitmore et al. (2020) derive a slightly
different set of young sources for NGC 4449 from the one discussed above. We
find 18 sources from the catalog of Whitmore et al. (2020) that are good
quality (flag Qual=1), younger than 10 Myr, more massive than 3,000 M⊙,333We
rescale the masses derived in Whitmore et al. (2020) from their adopted
distance of 4.31 Mpc to the 4.2 Mpc adopted here for NGC 4449. and are within
the WFC3/IR footprint. All 18 sources are already present in the LEGUS
catalog, but with several differences in the derived physical properties.
Seven of the $>$3,000 M⊙ clusters from the catalog of Whitmore et al. (2020)
have masses $<$3,000 M⊙ in the LEGUS catalog. This is a direct consequence of
the approach implemented by Whitmore et al. (2020): neglecting the nebular
continuum at young ages yields overestimated stellar masses (Reines et al.,
2010). Of the remaining 11 clusters, seven have derived ages $\sim$5 Myr in
Whitmore et al. (2020) and 10$\leq$age$\leq$30 Myr in LEGUS. Visual inspection
of the H$\alpha$ image reveals that only one of the seven is truly associated
with H$\alpha$ emission; the remaining six clusters are contaminated by
neighboring strongly emitting line sources and do not appear to be associated
with in–situ H$\alpha$ emission. The bona–fide H$\alpha$–emitting source is
$\sim$5.5 Myr old, with mass $\sim$2.5$\times$105 M⊙ and E(B–V)$\sim$0.44 mag.
The remaining 4 sources (of 18) have derived ages $\sim$4–5 Myr in the catalog
of Whitmore et al. (2020) and $\sim$2–3 Myr in LEGUS, suggesting, again, that
differences in the handling of the nebular continuum and lines may be at the
root of the observed differences. The overall picture remains, however,
unchanged, as already discussed: the young, UV/optically–selected sources have
ages$\lesssim$5 Myr and low extinctions when associated with line emission. We
retain below the direct comparison with the LEGUS results, as our approach to
SED fitting is similar to the one adopted in that project.

The picture that emerges from this analysis is that, in the central region of
NGC 4449, star formation has continued for the past few Myr, producing mainly
star clusters. Massive clusters retain their gas (and dust) for the first
$\sim$4 Myr and then disperse it. Lower mass clusters can retain their gas and
dust for a longer period of time, and appear compact (in line emission) and
dusty up to $\sim$6 Myr and possibly longer. The age limit for our analysis,
$\sim$7 Myr, is discussed earlier and is due to two concomitant effects: (1)
beyond 7 Myr line emission becomes too faint to be recognized as such, and (2)
there are age degeneracies in the SED fits for ages $>$6–7 Myr.

We observe that the massive clusters in the LEGUS sample retain their gas only
for the first $\sim$4 Myr, implying that timescales may be too short to
observe their dustier progenitors. Conversely, we speculate that the
progenitors of the lower–mass, Pa$\beta$ emitting, compact clusters we
identify are buried in dust and are undetectable in our near–IR bands, similar
to what occurs in the Milky Way (Gutermuth et al., 2009; Megeath et al.,
2022). This may account for why we do not find young and dusty star clusters
in our sample. Alternatively, stochastic sampling of the IMF may affect our
age determinations (see next sections), although we attempt to mitigate its
impact by selecting clusters with mass $\geq$3,000 M⊙.

###  7.2 Comparison with NGC 1313

The study of Messa et al. (2021) of the late–type spiral NGC 1313 is the
closest one in methodology to the present study. These authors also find that
the Pa$\beta$–selected young star clusters in NGC 1313 are low–mass systems
and show a plume of high extinctions concentrated around 4–10 Myr, and that
the LEGUS star clusters for this galaxy identify typically lower–extinction
sources. However, Messa et al. (2021) do not isolate the LEGUS clusters that
are coincident with ionized gas emission and do not compare the LEGUS stellar
masses with the stellar masses of their Pa$\beta$ selected sources. Despite
these differences, those authors conclude that when including
infrared–selected star clusters the clearing timescales extend from $\sim$2
Myr to $\sim$4 Myr. Our timescales appear slightly longer, $\approx$6 Myr, for
the Pa$\beta$–detected clusters. This difference, if confirmed by further
studies, may be due to the higher pressure environment of NGC 4449 relative to
that of NGC 1313. Based on the SFR surface density presented in the
Introduction (section 1), the environmental pressure in NGC 4449 is about 2-3
times higher than in NGC 1313.

###  7.3 Consequences for the Timescale of Cluster Emergence

Our relatively unbiased sample of Pa$\beta$–emitting compact sources in the
galaxy NGC 4449 shows a dearth of sources younger than 4 Myr, while, at the
same time, we find that sources in the age range 5–6 Myr can display high
values of dust extinction, up to E(B–V)$\sim 1.4$ mag. As discussed in section
4, none of the sources identified in Calzetti et al. (2023) with
E(B$-$V)$\gtrsim$2 mag are confirmed by our more detailed inspection using the
JWST Pa$\alpha$ image, although we still find, like those authors, that we do
not recover very young ($\leq$4 Myr) sources. Conversely, the UV(U)–bright
clusters identified in the LEGUS sample are associated with line emission only
when $\lesssim$4 Myr old.

As presented in Section 3, the original sample from SExtractor was visually
inspected to remove stars, asterisms, image artifacts, and also sources where
the Pa$\beta$ line emission is not coincident with the stellar continuum
emission. This last criterion de facto removes from the sample sources where
the ionized gas has been displaced from the location of the ionizing star
cluster. This can happen, for instance, if supernova or pre–supernova feedback
has occurred in the cluster, indicating that our Pa$\beta$–emitting sample
will be missing sources where feedback has been active.

Pre–SN feedback acts on short timescales ($<$4 Myr) before the occurrence of
supernovae around $\sim$4 Myr (Pellegrini et al., 2011; Dale et al., 2012;
Krause et al., 2013; Krumholz et al., 2019; Leitherer et al., 2014). In star
clusters where pre–SN feedback is efficient, the surrounding ionized gas is
ejected, creating a ‘shell-like’ morphology for the gas emission (Whitmore et
al., 2011; Hollyhead et al., 2015; Hannon et al., 2022). These clusters are
likely to be young ($\lesssim$4 Myr), but also have low extinction, which
explains why they have been prominently identified in UV–optical samples
(Messa et al., 2021; Hannon et al., 2022).

When selecting for compact–emission sources in the near infrared, like our
sample, we succeed in locating star clusters with a large range of dust
extinction properties, including very dusty ones with E(B–V)$>$1 mag. However,
this sample of compact, line–emitting sources lacks very young ($\lesssim$4
Myr) clusters, raising the question of how effective pre–SN and supernova
feedback is in the $\sim$3,000–15,000 M⊙ mass regime of our clusters. All of
our clusters contain massive stars (presence of ionized gas emission), yet
they show compact morphology and include many dusty sources, suggesting that
neither pre–SN feedback nor supernova feedback are effective clearing
mechanisms in these sources. Even considering a range of supernova timescales,
$\sim$4-8 Myr, due to incomplete sampling of the IMF (Chevance et al., 2022;
Stanway & Eldridge, 2023), pre–SN feedback should still have performed a
significant amount of clearing in these clusters.

Using the calculations presented in Calzetti et al. (2023) adapted to our
range of extinctions (A${}_{V}\sim$0.8–4.5 mag), we derive external pressures
in the range Pext/$k_{B}$=(0.1–2.3)$\times$106 K cm-3, which are significantly
smaller, by about an order of magnitude, than the pressure supplied by
photoionization, direct radiation and stellar winds from the star clusters
(Calzetti et al., 2023). Thus, at face value, the host environment of our
clusters does not represent an impediment to the clusters’ ability to remove
gas and dust from their immediate surroundings.

However, the Pa$\beta$–emitting clusters, albeit selected to be $>$3,000 M⊙,
are a factor $\sim$7 lower mass than the LEGUS line–emitting clusters, with a
median mass of 7,000 M⊙. Therefore, we can expect stochastic sampling of the
stellar IMF to have an impact on the cluster’s properties, due to the effect
this has on the number and mass range of the massive stars contained in the
clusters (Cerviño et al., 2002; Stanway & Eldridge, 2023). Stanway & Eldridge
(2023), using the binary population models BPASS, find that a 7,000 M⊙ cluster
typically produces stars with maximum mass around 18 M⊙, with a 16th-84th
percentile range of $\sim$9–25 M⊙. These are mid–O/early–B type stars, which,
although still able to produce ionizing photons and stellar winds, generate
rates that are an order of magnitude or more below those of a fully sampled
IMF (Leitherer et al., 1999; Vink & Sander, 2021). Chevance et al. (2022)
shows that, in models, stochastic sampling plays a role in the spread of
expected delay times for supernovae to occur (see their Figure 4), covering
the range $\sim$4-10 Myr for a 3,000 $M_{\odot}$ cluster, but settling at
$\sim$4 Myr for clusters heavier than $\sim$2$\times$104 M⊙. Stochastic
sampling can also affect our age determinations, since we use deterministic
stellar population models to derive our ages, masses and extinctions. However,
deterministic models generally produce younger ages than stochastic models
(Krumholz et al., 2015), thus our clusters may well span a wider range of ages
than those we derive, but will also tend to be older.

Since external pressure in NGC 4449 is about an order a magnitude too low to
be a factor in determining the clearing timescale, the picture that emerges
from these arguments is that pre–SN and supernova feedback are not effective
on the same timescale in all star clusters, but will depend on the cluster’s
mass. The clearing timescale of 5–6 Myr we find is aligned with expectations
from stochastic sampling of the IMF in the mass range of our line–emitting
star clusters.

##  8 Summary and Conclusions

We have utilized multi-wavelength HST photometry, including narrowband filters
corresponding to H$\alpha$ and Pa$\beta$ hydrogen recombination lines, to
build a sample of 99 young cluster candidates in the nearby dwarf-starburst
NGC 4449. Leveraging a JWST/NIRCam Pa$\alpha$ image as well as our own images,
we visually confirmed that these sources possess compact morphologies with
coincident line emission. We also found that none of the sources identified by
Calzetti et al. (2023) shows line emission in the JWST Pa$\alpha$ image. After
deriving the SEDs of the candidates using aperture photometry and fitting them
to synthetic SED models, we were able to derive the ages, masses, and
extinctions for all 99 candidates. We isolate a final catalog of 15 sources
with $\chi_{red}^{2}$$<$10 and mass $>$3000 M⊙ to minimize the effects of
stochastic sampling of the stellar IMF. Our analysis of the resulting sources
reveals a notable plume of sources at 5-6 Myr. We also observe relatively few
very young sources, younger than $\sim$4 Myr, and only one with
mass$\geq$3,000 M⊙. A critical evaluation of our selection method indicates
that we are unlikely to have missed from our sample very young ($\lesssim$4
Myr) sources with compact line emission.

A comparison with the UV–optically selected young star clusters from the LEGUS
sample shows that, while our clusters retain their gas up to an age of at
least 5–6 Myr, possibly longer, the LEGUS clusters lose their gas by 4 Myr
age. The main difference between the two samples is that the young LEGUS
clusters extend to masses much higher than those in our sample. Considering
only clusters with mass $\geq$3,000 M⊙, the median mass for the LEGUS clusters
is $\sim$3.5$\times$104 M⊙, while for our sample it is $\sim$7$\times$103 M⊙.
These results suggest that pre–SN and SN feedback are effective in massive
star clusters, which, as a result, do not retain their gas (and dust) beyond
$\sim$4 Myr. Conversely, they appear to be less effective and require longer
timescales in the lower–mass star clusters (i.e., those in our sample), and
these clusters still retain gas and dust up to the maximum age our SED fits
can reliably determine ($\lesssim$7 Myr). We suggest that the progenitors of
these lower–mass clusters are still sufficiently buried in their natal cloud
to have escaped detection in our sample. Only after about 5–6 Myr do these
dusty clusters begin to emerge from their natal clouds.

The scenario implied by our data is that the clearing timescales due to pre–SN
and supernova feedback are cluster mass–dependent; the timescales are not
universally short but increase for decreasing star cluster mass. This is
likely due to both pre–SN and supernova feedback being impacted by stochastic
sampling of the stellar IMF, whose effect increases for decreasing mass. If
correct, this scenario would have important repercussions on a galaxy’s
ability to leak ionizing photons, since the steep cluster mass function
implies that galaxies contain many low–mass clusters and relatively few
massive star clusters, at least at low redshift (e.g. Adamo et al., 2017;
Messa et al., 2018; Cook et al., 2019). Observations of nearby galaxies with
the JWST possess improved sensitivity and angular resolution in the NIR and
will likely provide an improved sample of star clusters covering the full
range of ages, masses, and extinctions in the local universe, enabling
accurate determinations of the clusters’ clearing timescales.

The authors thank the FEAST team (JWST Cycle 1 program # 1783) for providing
the stellar–continuum subtracted Pa$\alpha$ image of NGC 4449. Based on
observations made with the NASA/ESA Hubble Space Telescope, obtained at the
Space Telescope Science Institute, which is operated by the Association of
Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555.
These observations are associated with program # 15330. Support for program #
15330 was provided by NASA through a grant from the Space Telescope Science
Institute. Based also on observations made with the NASA/ESA Hubble Space
Telescope, and obtained from the Hubble Legacy Archive, which is a
collaboration between the Space Telescope Science Institute (STScI/NASA), the
Space Telescope European Coordinating Facility (ST-ECF/ESA) and the Canadian
Astronomy Data Centre (CADC/NRC/CSA). Most of the data presented in this paper
were obtained from the Mikulski Archive for Space Telescopes (MAST) at the
Space Telescope Science Institute. The specific observations analyzed can be
accessed via https://doi.org/10.17909/8k2f-7205 (catalog
https://doi.org/10.17909/8k2f-7205) This research has made use of the
NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion
Laboratory, California Institute of Technology, under contract with the
National Aeronautics and Space Administration.

Figure 5: Best fit age versus E(B-V). (Left) All 99 fitted sources. (Right)
The 80 sources with $\chi^{2}_{red}<10$. Figure 6: Mass limit (black solid
line) as a function of age, as determined from the I-band detection limit of
our images. We set a 3000 M⊙ lower limit (black dashed line) to avoid impact
on our physical parameters from stochastic sampling of the IMF (see Section
6). (Left) Mass versus age for all 99 fitted sources. (Right) The same plot as
to the left, only for the sources with $\chi^{2}_{red}<$10\. In the right
panel, we also show the location of the LEGUS star clusters with
$\chi^{2}_{red}<$10, mass$\geq$3,000 M⊙ and coincident with ionized gas
emission (magenta triangles). See Adamo et al. (2017) for a discussion of the
LEGUS star clusters. Figure 7: Cutouts corresponding to the location of source
ID 82 with a best fit E(B-V) of 1.1 mag and an age of 6 Myr whose SED is
displayed in the lower left of Figure 3. H$\alpha$, P$\beta$, and P$\alpha$
are continuum-free emission line maps. Each box has dimensions of
$0.5^{\prime\prime}\times 0.5^{\prime\prime}$. Figure 8: Age and color excess
plots of all sources with masses $>$3000 M⊙. (Left) 22 sources with any value
of the $\chi^{2}_{red}$. (Right) 15 sources with $\chi^{2}_{red}<10$. Figure
9: (Left) Best fit age versus color excess for the clusters with
$\chi^{2}_{red}<10$ and mass $>$3000 M⊙, and with the inclusion of the single
visually identified source with comparable characteristics (black triangle).
This is a relatively unextincted source with E(B-V)=0.28, suggesting that the
approach of visual identification is insufficient at returning a sample of
young, highly extincted clusters. (Right) The same clusters as in the left
panel, with the addition of the LEGUS clusters with $\chi^{2}_{red}<10$, mass
$>$3000 M⊙ and coincident with ionized gas emission (magenta triangles with
error bars). See Adamo et al. (2017) for a discussion of the LEGUS star
clusters. Figure 10: Color versus mass plot. Blue circles are sources with
$\chi^{2}_{red}<10$, mass $>$3000 M⊙, and age $\leq$10 Myr (8 sources).
Magenta triangles represent the sample of line–emitting LEGUS clusters.

## References

  * Adamo et al. (2017) Adamo, A., Ryon, J. E., Messa, M., et al. 2017, ApJ, 841, 131, doi: 10.3847/1538-4357/aa7132
  * 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
  * Bastian et al. (2013) Bastian, N., Schweizer, F., Goudfrooij, P., Larsen, S. S., & Kissler-Patig, M. 2013, MNRAS, 431, 1252, doi: 10.1093/mnras/stt253
  * Bending et al. (2022) Bending, T. J. R., Dobbs, C. L., & Bate, M. R. 2022, MNRAS, 513, 2088, doi: 10.1093/mnras/stac965
  * Berg et al. (2012) Berg, D. A., Skillman, E. D., Marble, A. R., et al. 2012, ApJ, 754, 98, doi: 10.1088/0004-637X/754/2/98
  * Bertin & Arnouts (1996) Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164
  * Boquien et al. (2019) Boquien, M., Burgarella, D., Roehlly, Y., et al. 2019, A&A, 622, A103, doi: 10.1051/0004-6361/201834156
  * Brown & Gnedin (2021) Brown, G., & Gnedin, O. Y. 2021, MNRAS, 508, 5935, doi: 10.1093/mnras/stab2907
  * Bruzual & Charlot (2003) Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x
  * Calzetti (2001) Calzetti, D. 2001, PASP, 113, 1449, doi: 10.1086/324269
  * Calzetti et al. (2000) Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682, doi: 10.1086/308692
  * Calzetti et al. (2015a) Calzetti, D., Johnson, K. E., Adamo, A., et al. 2015a, ApJ, 811, 75, doi: 10.1088/0004-637X/811/2/75
  * Calzetti et al. (2015b) Calzetti, D., Lee, J. C., Sabbi, E., et al. 2015b, AJ, 149, 51, doi: 10.1088/0004-6256/149/2/51
  * Calzetti et al. (2018) Calzetti, D., Wilson, G. W., Draine, B. T., et al. 2018, ApJ, 852, 106, doi: 10.3847/1538-4357/aaa1e2
  * Calzetti et al. (2023) Calzetti, D., Linden, S. T., McQuaid, T., et al. 2023, arXiv e-prints, arXiv:2302.11117, doi: 10.48550/arXiv.2302.11117
  * Cerviño et al. (2002) Cerviño, M., Valls-Gabaud, D., Luridiana, V., & Mas-Hesse, J. M. 2002, A&A, 381, 51, doi: 10.1051/0004-6361:20011266
  * Ceverino & Klypin (2009) Ceverino, D., & Klypin, A. 2009, ApJ, 695, 292, doi: 10.1088/0004-637X/695/1/292
  * Chevance et al. (2022) Chevance, M., Kruijssen, J. M. D., Krumholz, M. R., et al. 2022, MNRAS, 509, 272, doi: 10.1093/mnras/stab2938
  * Cignoni et al. (2018) Cignoni, M., Sacchi, E., Aloisi, A., et al. 2018, ApJ, 856, 62, doi: 10.3847/1538-4357/aab041
  * Cook et al. (2014) Cook, D. O., Dale, D. A., Johnson, B. D., et al. 2014, MNRAS, 445, 899, doi: 10.1093/mnras/stu1787
  * Cook et al. (2019) Cook, D. O., Lee, J. C., Adamo, A., et al. 2019, MNRAS, 484, 4897, doi: 10.1093/mnras/stz331
  * Corbelli et al. (2017) Corbelli, E., Braine, J., Bandiera, R., et al. 2017, A&A, 601, A146, doi: 10.1051/0004-6361/201630034
  * Dale et al. (2012) Dale, J. E., Ercolano, B., & Bonnell, I. A. 2012, MNRAS, 424, 377, doi: 10.1111/j.1365-2966.2012.21205.x
  * Della Bruna et al. (2022) Della Bruna, L., Adamo, A., McLeod, A. F., et al. 2022, A&A, 666, A29, doi: 10.1051/0004-6361/202243395
  * Dobbs (2015) Dobbs, C. L. 2015, MNRAS, 447, 3390, doi: 10.1093/mnras/stu2585
  * Dobbs et al. (2011) Dobbs, C. L., Burkert, A., & Pringle, J. E. 2011, MNRAS, 417, 1318, doi: 10.1111/j.1365-2966.2011.19346.x
  * Elmegreen (2010) Elmegreen, B. G. 2010, in Star Clusters: Basic Galactic Building Blocks Throughout Time and Space, ed. R. de Grijs & J. R. D. Lépine, Vol. 266, 3–13, doi: 10.1017/S1743921309990809
  * Elmegreen & Hunter (2010) Elmegreen, B. G., & Hunter, D. A. 2010, ApJ, 712, 604, doi: 10.1088/0004-637X/712/1/604
  * Ferland et al. (2013) Ferland, G. J., Porter, R. L., van Hoof, P. A. M., et al. 2013, Rev. Mexicana Astron. Astrofis., 49, 137.  https://arxiv.org/abs/1302.4485
  * Fitzpatrick (1999) Fitzpatrick, E. L. 1999, PASP, 111, 63, doi: 10.1086/316293
  * Goldbaum et al. (2016) Goldbaum, N. J., Krumholz, M. R., & Forbes, J. C. 2016, ApJ, 827, 28, doi: 10.3847/0004-637X/827/1/28
  * Grasha et al. (2018) Grasha, K., Calzetti, D., Bittle, L., et al. 2018, MNRAS, 481, 1016, doi: 10.1093/mnras/sty2154
  * Grasha et al. (2019) Grasha, K., Calzetti, D., Adamo, A., et al. 2019, MNRAS, 483, 4707, doi: 10.1093/mnras/sty3424
  * Grudić et al. (2022) Grudić, M. Y., Guszejnov, D., Offner, S. S. R., et al. 2022, MNRAS, 512, 216, doi: 10.1093/mnras/stac526
  * Grudić et al. (2018) Grudić, M. Y., Hopkins, P. F., Faucher-Giguère, C.-A., et al. 2018, MNRAS, 475, 3511, doi: 10.1093/mnras/sty035
  * Gutermuth et al. (2009) Gutermuth, R. A., Megeath, S. T., Myers, P. C., et al. 2009, ApJS, 184, 18, doi: 10.1088/0067-0049/184/1/18
  * Hannon et al. (2019) Hannon, S., Lee, J. C., Whitmore, B. C., et al. 2019, MNRAS, 490, 4648, doi: 10.1093/mnras/stz2820
  * Hannon et al. (2022) —. 2022, MNRAS, 512, 1294, doi: 10.1093/mnras/stac550
  * Hennebelle & Chabrier (2011) Hennebelle, P., & Chabrier, G. 2011, ApJ, 743, L29, doi: 10.1088/2041-8205/743/2/L29
  * Hollyhead et al. (2015) Hollyhead, K., Bastian, N., Adamo, A., et al. 2015, MNRAS, 449, 1106, doi: 10.1093/mnras/stv331
  * Hopkins et al. (2020) Hopkins, P. F., Grudić, M. Y., Wetzel, A., et al. 2020, MNRAS, 491, 3702, doi: 10.1093/mnras/stz3129
  * Hopkins et al. (2014) Hopkins, P. F., Kereš, D., Oñorbe, J., et al. 2014, MNRAS, 445, 581, doi: 10.1093/mnras/stu1738
  * Hopkins et al. (2012) Hopkins, P. F., Quataert, E., & Murray, N. 2012, MNRAS, 421, 3488, doi: 10.1111/j.1365-2966.2012.20578.x
  * Johnson et al. (2021) Johnson, B. D., Leja, J., Conroy, C., & Speagle, J. S. 2021, ApJS, 254, 22, doi: 10.3847/1538-4365/abef67
  * Kim et al. (2021) Kim, J., Chevance, M., Kruijssen, J. M. D., et al. 2021, MNRAS, 504, 487, doi: 10.1093/mnras/stab878
  * Krause et al. (2013) Krause, M., Fierlinger, K., Diehl, R., et al. 2013, A&A, 550, A49, doi: 10.1051/0004-6361/201220060
  * Kroupa (2001) Kroupa, P. 2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x
  * Kruijssen et al. (2019) Kruijssen, J. M. D., Schruba, A., Chevance, M., et al. 2019, Nature, 569, 519, doi: 10.1038/s41586-019-1194-3
  * Krumholz & McKee (2005) Krumholz, M. R., & McKee, C. F. 2005, ApJ, 630, 250, doi: 10.1086/431734
  * Krumholz et al. (2019) Krumholz, M. R., McKee, C. F., & Bland-Hawthorn, J. 2019, ARA&A, 57, 227, doi: 10.1146/annurev-astro-091918-104430
  * Krumholz et al. (2015) Krumholz, M. R., Adamo, A., Fumagalli, M., et al. 2015, ApJ, 812, 147, doi: 10.1088/0004-637X/812/2/147
  * Lada & Lada (2003) Lada, C. J., & Lada, E. A. 2003, ARA&A, 41, 57, doi: 10.1146/annurev.astro.41.011802.094844
  * Leitherer et al. (2014) Leitherer, C., Ekström, S., Meynet, G., et al. 2014, ApJS, 212, 14, doi: 10.1088/0067-0049/212/1/14
  * Leitherer et al. (1999) Leitherer, C., Schaerer, D., Goldader, J. D., et al. 1999, ApJS, 123, 3, doi: 10.1086/313233
  * Lucas et al. (2020) Lucas, W. E., Bonnell, I. A., & Dale, J. E. 2020, MNRAS, 493, 4700, doi: 10.1093/mnras/staa451
  * Matthews et al. (2018) Matthews, A. M., Johnson, K. E., Whitmore, B. C., et al. 2018, ApJ, 862, 147, doi: 10.3847/1538-4357/aac958
  * McLeod et al. (2021) McLeod, A. F., Ali, A. A., Chevance, M., et al. 2021, MNRAS, 508, 5425, doi: 10.1093/mnras/stab2726
  * McQuinn et al. (2012) McQuinn, K. B. W., Skillman, E. D., Dalcanton, J. J., et al. 2012, ApJ, 759, 77, doi: 10.1088/0004-637X/759/1/77
  * McQuinn et al. (2010) McQuinn, K. B. W., Skillman, E. D., Cannon, J. M., et al. 2010, ApJ, 721, 297, doi: 10.1088/0004-637X/721/1/297
  * Megeath et al. (2022) Megeath, S. T., Gutermuth, R. A., & Kounkel, M. A. 2022, PASP, 134, 042001, doi: 10.1088/1538-3873/ac4c9c
  * Messa et al. (2018) Messa, M., Adamo, A., Östlin, G., et al. 2018, MNRAS, 473, 996, doi: 10.1093/mnras/stx2403
  * Messa et al. (2021) Messa, M., Calzetti, D., Adamo, A., et al. 2021, ApJ, 909, 121, doi: 10.3847/1538-4357/abe0b5
  * Ostriker et al. (2010) Ostriker, E. C., McKee, C. F., & Leroy, A. K. 2010, ApJ, 721, 975, doi: 10.1088/0004-637X/721/2/975
  * Pellegrini et al. (2011) Pellegrini, E. W., Baldwin, J. A., & Ferland, G. J. 2011, ApJ, 738, 34, doi: 10.1088/0004-637X/738/1/34
  * Pilyugin et al. (2015) Pilyugin, L. S., Grebel, E. K., & Zinchenko, I. A. 2015, MNRAS, 450, 3254, doi: 10.1093/mnras/stv932
  * Reines et al. (2008) Reines, A. E., Johnson, K. E., & Goss, W. M. 2008, AJ, 135, 2222, doi: 10.1088/0004-6256/135/6/2222
  * Reines et al. (2010) Reines, A. E., Nidever, D. L., Whelan, D. G., & Johnson, K. E. 2010, ApJ, 708, 26, doi: 10.1088/0004-637X/708/1/26
  * Ryon et al. (2015) Ryon, J. E., Bastian, N., Adamo, A., et al. 2015, MNRAS, 452, 525, doi: 10.1093/mnras/stv1282
  * Ryon et al. (2017) Ryon, J. E., Gallagher, J. S., Smith, L. J., et al. 2017, ApJ, 841, 92, doi: 10.3847/1538-4357/aa719e
  * Sacchi et al. (2018) Sacchi, E., Cignoni, M., Aloisi, A., et al. 2018, ApJ, 857, 63, doi: 10.3847/1538-4357/aab844
  * Stanway & Eldridge (2023) Stanway, E. R., & Eldridge, J. J. 2023, MNRAS, 522, 4430, doi: 10.1093/mnras/stad1185
  * Tully et al. (2013) Tully, R. B., Courtois, H. M., Dolphin, A. E., et al. 2013, AJ, 146, 86, doi: 10.1088/0004-6256/146/4/86
  * Vink & Sander (2021) Vink, J. S., & Sander, A. A. C. 2021, MNRAS, 504, 2051, doi: 10.1093/mnras/stab902
  * Whitmore et al. (2011) Whitmore, B. C., Chandar, R., Kim, H., et al. 2011, ApJ, 729, 78, doi: 10.1088/0004-637X/729/2/78
  * Whitmore et al. (2020) Whitmore, B. C., Chandar, R., Lee, J., et al. 2020, ApJ, 889, 154, doi: 10.3847/1538-4357/ab59e5
  * Zackrisson et al. (2011) Zackrisson, E., Rydberg, C.-E., Schaerer, D., Östlin, G., & Tuli, M. 2011, ApJ, 740, 13, doi: 10.1088/0004-637X/740/1/13

Table 3: Source Location and Photometry

(ID) | (RA(2000),DEC(2000)) | (F275W) | (F336W) | (F435W) | (F550M) | (F555W) | (F658N) | (F814W) | (F110W) | (F128N) | (F160W)
---|---|---|---|---|---|---|---|---|---|---|---
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12)
1 | 12:28:12.74 +44:04:58.75 | 21.25$\pm$0.08 | 21.42$\pm$0.08 | 21.63$\pm$0.08 | 21.94$\pm$0.08 | 21.78$\pm$0.08 | 20.35$\pm$0.11 | 22.39$\pm$0.08 | 23.28$\pm$0.14 | 22.53$\pm$0.16 | 23.69$\pm$0.23
2 | 12:28:11.01 +44:05:02.02 | 23.49$\pm$0.12 | 23.56$\pm$0.11 | 23.15$\pm$0.1 | 22.54$\pm$0.1 | 22.68$\pm$0.1 | 21.59$\pm$0.12 | 21.51$\pm$0.09 | 21.53$\pm$0.15 | 20.99$\pm$0.15 | 21.22$\pm$0.16
3 | 12:28:08.85 +44:05:09.30 | 22.46$\pm$0.09 | 22.12$\pm$0.09 | 21.99$\pm$0.09 | 21.9$\pm$0.09 | 21.53$\pm$0.08 | 18.7$\pm$0.09 | 21.31$\pm$0.08 | 21.63$\pm$0.1 | 20.09$\pm$0.1 | 21.81$\pm$0.11
4 | 12:28:13.86 +44:05:12.03 | 20.72$\pm$0.08 | 20.9$\pm$0.08 | 20.75$\pm$0.08 | 21.08$\pm$0.08 | 20.98$\pm$0.08 | 20.05$\pm$0.09 | 21.35$\pm$0.08 | 22.79$\pm$0.16 | 22.16$\pm$0.12 | 23.22$\pm$0.16
5 | 12:28:08.95 +44:05:13.16 | 25.96$\pm$1.09 | 26.36$\pm$1.45 | 25.34$\pm$0.84 | 23.29$\pm$0.17 | 23.55$\pm$0.21 | 22.52$\pm$0.33 | 22.25$\pm$0.1 | 22.27$\pm$0.18 | 21.98$\pm$0.18 | 21.95$\pm$0.21
6 | 12:28:08.77 +44:05:12.83 | 25.06$\pm$0.38 | 25.38$\pm$0.49 | 25.22$\pm$0.68 | 23.65$\pm$0.17 | 23.52$\pm$0.2 | 20.49$\pm$0.16 | 22.54$\pm$0.1 | 22.95$\pm$0.21 | 22.25$\pm$0.18 | 22.58$\pm$0.16
7 | 12:28:09.29 +44:05:15.05 | 20.56$\pm$0.08 | 20.69$\pm$0.08 | 20.9$\pm$0.08 | 21.13$\pm$0.08 | 21.14$\pm$0.08 | 19.12$\pm$0.09 | 21.67$\pm$0.09 | 22.93$\pm$0.25 | 21.88$\pm$0.2 | 23.53$\pm$0.45
8 | 12:28:09.39 +44:05:15.30 | 24.26$\pm$0.59 | 26.43$\pm$5.55 | 23.77$\pm$0.3 | 22.3$\pm$0.1 | 22.76$\pm$0.16 | 22.21$\pm$1.09 | 21.42$\pm$0.08 | 21.71$\pm$0.2 | 21.28$\pm$0.18 | 21.39$\pm$0.14
9 | 12:28:09.95 +44:05:15.87 | 23.12$\pm$0.14 | 23.27$\pm$0.18 | 23.33$\pm$0.22 | 23.51$\pm$0.27 | 23.2$\pm$0.22 | 21.17$\pm$0.24 | 22.16$\pm$0.12 | 21.95$\pm$0.34 | 21.47$\pm$0.21 | 21.39$\pm$0.32
10 | 12:28:10.69 +44:05:17.36 | 28.54$\pm$4.58 | 24.8$\pm$0.2 | 23.29$\pm$0.1 | 22.68$\pm$0.1 | 22.63$\pm$0.09 | 22.0$\pm$0.29 | 22.04$\pm$0.1 | 22.39$\pm$0.28 | 22.01$\pm$0.3 | 22.22$\pm$0.39
11 | 12:28:09.32 +44:05:17.01 | 20.82$\pm$0.08 | 20.94$\pm$0.08 | 21.15$\pm$0.08 | 21.49$\pm$0.08 | 21.35$\pm$0.08 | 19.07$\pm$0.08 | 21.8$\pm$0.08 | 22.84$\pm$0.27 | 21.66$\pm$0.14 | 23.5$\pm$0.82
12 | 12:28:09.71 +44:05:20.59 | 22.99$\pm$0.12 | 22.78$\pm$0.1 | 22.29$\pm$0.11 | 21.47$\pm$0.1 | 21.72$\pm$0.1 | 21.6$\pm$0.24 | 20.43$\pm$0.08 | 20.55$\pm$0.14 | 20.12$\pm$0.15 | 20.24$\pm$0.13
13 | 12:28:09.53 +44:05:21.01 | 25.86$\pm$0.8 | 25.63$\pm$0.62 | 26.44$\pm$1.31 | 23.7$\pm$0.14 | 23.91$\pm$0.16 | 21.97$\pm$0.44 | 21.91$\pm$0.09 | 21.78$\pm$0.12 | 21.09$\pm$0.12 | 21.33$\pm$0.12
14 | 12:28:08.91 +44:05:22.52 | 22.84$\pm$0.09 | 22.61$\pm$0.08 | 22.57$\pm$0.09 | 22.66$\pm$0.09 | 22.27$\pm$0.08 | 19.69$\pm$0.08 | 22.44$\pm$0.09 | 23.08$\pm$0.11 | 21.76$\pm$0.12 | 23.76$\pm$0.29
15 | 12:28:13.04 +44:05:22.83 | 21.55$\pm$0.08 | 21.57$\pm$0.08 | 21.84$\pm$0.08 | 21.88$\pm$0.08 | 22.07$\pm$0.08 | 22.1$\pm$0.2 | 22.19$\pm$0.08 | 23.14$\pm$0.12 | 22.72$\pm$0.22 | 23.36$\pm$0.23
16 | 12:28:12.37 +44:05:23.19 | 19.42$\pm$0.08 | 19.46$\pm$0.08 | 19.56$\pm$0.08 | 19.74$\pm$0.08 | 20.04$\pm$0.08 | 18.74$\pm$0.08 | 20.12$\pm$0.08 | 20.98$\pm$0.11 | 20.2$\pm$0.1 | 21.37$\pm$0.14
17 | 12:28:10.08 +44:05:23.52 | 25.78$\pm$1.64 | 24.23$\pm$0.39 | 23.83$\pm$0.31 | 23.53$\pm$0.22 | 23.36$\pm$0.22 | 21.83$\pm$0.53 | 22.54$\pm$0.11 | 22.62$\pm$0.2 | 22.03$\pm$0.24 | 22.26$\pm$0.22
18 | 12:28:09.99 +44:05:24.66 | 20.71$\pm$0.09 | 20.53$\pm$0.09 | 20.46$\pm$0.09 | 20.64$\pm$0.09 | 19.81$\pm$0.09 | 17.58$\pm$0.1 | 20.45$\pm$0.09 | 20.94$\pm$0.15 | 19.46$\pm$0.13 | 21.23$\pm$0.14
19 | 12:28:13.94 +44:05:30.56 | 25.5$\pm$0.25 | 25.1$\pm$0.14 | 24.65$\pm$0.12 | 24.21$\pm$0.14 | 23.86$\pm$0.1 | 21.25$\pm$0.09 | 23.53$\pm$0.11 | 23.82$\pm$0.28 | 22.52$\pm$0.14 | 23.92$\pm$0.48
20 | 12:28:11.36 +44:05:30.90 | 26.35$\pm$1.09 | 26.07$\pm$1.09 | 25.47$\pm$1.09 | 26.07$\pm$2.0 | 25.6$\pm$1.09 | 22.26$\pm$0.29 | 21.96$\pm$0.11 | 21.82$\pm$0.16 | 21.15$\pm$0.13 | 21.26$\pm$0.15
21 | 12:28:10.43 +44:05:30.19 | 20.22$\pm$0.08 | 20.1$\pm$0.08 | 19.89$\pm$0.08 | 19.55$\pm$0.08 | 19.64$\pm$0.08 | 18.88$\pm$0.08 | 18.81$\pm$0.08 | 19.0$\pm$0.12 | 18.52$\pm$0.11 | 18.72$\pm$0.12
22 | 12:28:10.67 +44:05:32.23 | 24.26$\pm$0.18 | 24.05$\pm$0.18 | 23.8$\pm$0.23 | 23.13$\pm$0.18 | 22.97$\pm$0.16 | 20.79$\pm$0.14 | 21.14$\pm$0.09 | 20.84$\pm$0.17 | 20.12$\pm$0.15 | 20.23$\pm$0.17
23 | 12:28:09.38 +44:05:35.08 | 21.98$\pm$0.09 | 22.16$\pm$0.09 | 22.21$\pm$0.09 | 21.81$\pm$0.08 | 21.92$\pm$0.09 | 21.07$\pm$0.1 | 20.98$\pm$0.08 | 21.17$\pm$0.11 | 20.71$\pm$0.1 | 20.82$\pm$0.11
24 | 12:28:11.51 +44:05:37.62 | 26.03$\pm$1.09 | 26.01$\pm$1.09 | 25.07$\pm$1.09 | 22.94$\pm$0.24 | 23.22$\pm$0.29 | 21.98$\pm$0.28 | 20.6$\pm$0.08 | 20.44$\pm$0.1 | 19.91$\pm$0.1 | 20.16$\pm$0.1
25 | 12:28:10.80 +44:05:36.80 | 20.35$\pm$0.08 | 20.28$\pm$0.08 | 20.22$\pm$0.08 | 20.41$\pm$0.08 | 19.98$\pm$0.09 | 17.96$\pm$0.09 | 20.57$\pm$0.08 | 21.38$\pm$0.21 | 19.92$\pm$0.12 | 22.04$\pm$0.52
26 | 12:28:11.40 +44:05:37.21 | 23.01$\pm$0.15 | 22.59$\pm$0.13 | 22.7$\pm$0.2 | 22.43$\pm$0.17 | 21.92$\pm$0.19 | 19.37$\pm$0.14 | 21.08$\pm$0.09 | 20.85$\pm$0.13 | 20.16$\pm$0.12 | 20.28$\pm$0.13
27 | 12:28:17.87 +44:05:37.69 | 21.82$\pm$0.08 | 21.92$\pm$0.08 | 21.9$\pm$0.08 | 22.06$\pm$0.08 | 21.9$\pm$0.1 | 20.55$\pm$0.2 | 22.37$\pm$0.09 | 23.1$\pm$0.15 | 21.95$\pm$0.15 | 23.32$\pm$0.14
28 | 12:28:12.25 +44:05:38.92 | 21.19$\pm$0.09 | 20.68$\pm$0.08 | 20.11$\pm$0.08 | 20.2$\pm$0.08 | 20.13$\pm$0.08 | 19.41$\pm$0.08 | 20.3$\pm$0.08 | 21.12$\pm$0.14 | 20.78$\pm$0.13 | 21.19$\pm$0.14
29 | 12:28:11.13 +44:05:40.50 | 24.82$\pm$0.32 | 24.73$\pm$0.52 | 23.34$\pm$0.22 | 21.36$\pm$0.09 | 21.64$\pm$0.1 | 20.31$\pm$0.13 | 19.92$\pm$0.08 | 19.95$\pm$0.12 | 19.41$\pm$0.11 | 19.63$\pm$0.13
30 | 12:28:10.93 +44:05:40.63 | 20.42$\pm$0.08 | 19.2$\pm$0.08 | 18.78$\pm$0.08 | 18.59$\pm$0.08 | 18.61$\pm$0.08 | 16.76$\pm$0.08 | 18.32$\pm$0.08 | 19.1$\pm$0.1 | 18.48$\pm$0.09 | 19.22$\pm$0.09
31 | 12:28:11.31 +44:05:42.29 | 21.73$\pm$0.09 | 21.79$\pm$0.09 | 21.98$\pm$0.13 | 22.38$\pm$0.16 | 22.11$\pm$0.14 | 21.07$\pm$0.12 | 22.21$\pm$0.15 | 22.51$\pm$0.36 | 22.01$\pm$0.26 | 22.31$\pm$0.38
32 | 12:28:13.09 +44:05:42.65 | 21.24$\pm$0.1 | 20.85$\pm$0.09 | 21.0$\pm$0.09 | 21.42$\pm$0.09 | 19.57$\pm$0.08 | 17.03$\pm$0.08 | 20.58$\pm$0.08 | 20.98$\pm$0.14 | 19.2$\pm$0.11 | 21.5$\pm$0.14
33 | 12:28:11.16 +44:05:41.33 | 25.1$\pm$0.4 | 24.89$\pm$0.3 | 23.5$\pm$0.17 | 22.21$\pm$0.11 | 22.41$\pm$0.12 | 21.3$\pm$0.15 | 20.71$\pm$0.09 | 20.53$\pm$0.14 | 20.01$\pm$0.13 | 20.14$\pm$0.14
34 | 12:28:12.18 +44:05:42.61 | 21.36$\pm$0.1 | 21.14$\pm$0.1 | 20.55$\pm$0.1 | 20.24$\pm$0.1 | 20.26$\pm$0.1 | 19.8$\pm$0.1 | 19.32$\pm$0.08 | 19.27$\pm$0.11 | 18.7$\pm$0.1 | 18.85$\pm$0.1
35 | 12:28:12.89 +44:05:44.24 | 20.87$\pm$0.09 | 20.97$\pm$0.09 | 21.09$\pm$0.1 | 21.45$\pm$0.1 | 21.13$\pm$0.1 | 19.29$\pm$0.09 | 21.51$\pm$0.1 | 22.02$\pm$0.15 | 21.2$\pm$0.16 | 21.9$\pm$0.16
36 | 12:28:13.08 +44:05:43.88 | 20.18$\pm$0.08 | 20.18$\pm$0.08 | 20.19$\pm$0.08 | 20.38$\pm$0.08 | 20.25$\pm$0.09 | 19.63$\pm$0.2 | 20.79$\pm$0.08 | 21.86$\pm$0.19 | 20.92$\pm$0.22 | 22.36$\pm$0.35
37 | 12:28:13.69 +44:05:44.76 | 19.25$\pm$0.08 | 19.49$\pm$0.08 | 19.66$\pm$0.08 | 20.07$\pm$0.08 | 19.94$\pm$0.08 | 19.85$\pm$0.12 | 20.79$\pm$0.08 | 21.93$\pm$0.13 | 21.63$\pm$0.16 | 22.48$\pm$0.15
38 | 12:28:11.94 +44:05:50.12 | 21.62$\pm$0.08 | 21.75$\pm$0.08 | 21.9$\pm$0.09 | 21.38$\pm$0.09 | 21.43$\pm$0.09 | 20.51$\pm$0.11 | 20.24$\pm$0.08 | 20.21$\pm$0.14 | 19.73$\pm$0.13 | 19.84$\pm$0.13
39 | 12:28:11.81 +44:05:52.17 | 24.41$\pm$0.18 | 24.91$\pm$0.35 | 24.6$\pm$0.33 | 25.67$\pm$1.22 | 24.35$\pm$0.36 | 22.55$\pm$0.58 | 22.06$\pm$0.09 | 21.7$\pm$0.12 | 21.1$\pm$0.11 | 21.2$\pm$0.11
40 | 12:28:11.70 +44:05:51.00 | 19.12$\pm$0.08 | 18.92$\pm$0.08 | 19.41$\pm$0.08 | 19.44$\pm$0.08 | 19.36$\pm$0.08 | 17.02$\pm$0.08 | 19.17$\pm$0.08 | 20.04$\pm$0.1 | 19.12$\pm$0.09 | 20.1$\pm$0.1
41 | 12:28:12.03 +44:05:52.65 | 26.58$\pm$4.82 | 24.85$\pm$1.09 | 25.79$\pm$3.04 | 23.3$\pm$0.24 | 23.2$\pm$0.34 | 20.93$\pm$0.42 | 20.36$\pm$0.08 | 19.94$\pm$0.12 | 19.37$\pm$0.11 | 19.54$\pm$0.13
42 | 12:28:11.82 +44:05:53.83 | 21.67$\pm$0.09 | 21.8$\pm$0.09 | 22.09$\pm$0.1 | 22.53$\pm$0.11 | 21.97$\pm$0.09 | 19.64$\pm$0.1 | 22.56$\pm$0.15 | 23.26$\pm$0.49 | 21.91$\pm$0.25 | 23.42$\pm$0.75
43 | 12:28:11.38 +44:05:53.26 | 24.76$\pm$0.17 | 24.9$\pm$0.18 | 24.03$\pm$0.13 | 23.21$\pm$0.11 | 23.35$\pm$0.11 | 22.45$\pm$0.18 | 21.93$\pm$0.08 | 21.51$\pm$0.1 | 20.91$\pm$0.1 | 21.12$\pm$0.09
44 | 12:28:12.03 +44:05:53.29 | 24.43$\pm$0.64 | 24.79$\pm$1.12 | 23.19$\pm$0.29 | 21.35$\pm$0.1 | 21.57$\pm$0.11 | 20.19$\pm$0.14 | 19.88$\pm$0.08 | 19.94$\pm$0.12 | 19.43$\pm$0.11 | 19.52$\pm$0.11
45 | 12:28:12.45 +44:05:54.83 | 26.69$\pm$1.48 | 26.43$\pm$1.33 | 27.18$\pm$4.37 | 23.76$\pm$0.21 | 23.98$\pm$0.26 | 22.85$\pm$0.41 | 21.55$\pm$0.08 | 21.26$\pm$0.12 | 20.74$\pm$0.12 | 20.76$\pm$0.12
46 | 12:28:19.11 +44:06:00.88 | 22.26$\pm$0.08 | 22.32$\pm$0.08 | 22.46$\pm$0.08 | 22.76$\pm$0.08 | 22.26$\pm$0.08 | 20.12$\pm$0.1 | 22.94$\pm$0.09 | 23.74$\pm$0.16 | 22.42$\pm$0.15 | 24.26$\pm$0.23
47 | 12:28:13.79 +44:06:02.82 | 22.02$\pm$0.08 | 21.54$\pm$0.08 | 21.44$\pm$0.08 | 21.44$\pm$0.08 | 21.38$\pm$0.08 | 19.91$\pm$0.08 | 21.33$\pm$0.08 | 22.31$\pm$0.18 | 21.64$\pm$0.13 | 22.26$\pm$0.24
48 | 12:28:18.60 +44:06:05.50 | 21.85$\pm$0.08 | 21.92$\pm$0.08 | 22.12$\pm$0.08 | 22.36$\pm$0.08 | 21.92$\pm$0.08 | 19.75$\pm$0.09 | 22.59$\pm$0.08 | 23.73$\pm$0.19 | 22.28$\pm$0.16 | 24.67$\pm$0.49
49 | 12:28:12.96 +44:06:06.54 | 26.24$\pm$0.73 | 26.18$\pm$0.81 | 23.97$\pm$0.17 | 22.22$\pm$0.09 | 22.52$\pm$0.09 | 21.28$\pm$0.12 | 20.89$\pm$0.08 | 20.82$\pm$0.11 | 20.4$\pm$0.1 | 20.45$\pm$0.11
50 | 12:28:14.00 +44:06:12.67 | 25.16$\pm$0.22 | 25.41$\pm$0.35 | 24.11$\pm$0.15 | 22.89$\pm$0.1 | 22.95$\pm$0.13 | 21.79$\pm$0.31 | 22.1$\pm$0.09 | 22.45$\pm$0.22 | 21.81$\pm$0.2 | 22.3$\pm$0.23
51 | 12:28:15.02 +44:06:18.90 | 22.03$\pm$0.08 | 21.76$\pm$0.08 | 21.66$\pm$0.08 | 21.51$\pm$0.08 | 21.48$\pm$0.08 | 19.4$\pm$0.08 | 21.4$\pm$0.08 | 22.16$\pm$0.12 | 21.29$\pm$0.11 | 22.22$\pm$0.15
52 | 12:28:10.63 +44:06:20.64 | 20.64$\pm$0.08 | 20.49$\pm$0.08 | 20.86$\pm$0.08 | 20.84$\pm$0.08 | 20.79$\pm$0.08 | 18.59$\pm$0.08 | 20.71$\pm$0.08 | 21.6$\pm$0.14 | 20.9$\pm$0.1 | 21.45$\pm$0.15
53 | 12:28:16.00 +44:06:29.31 | 20.2$\pm$0.08 | 19.87$\pm$0.08 | 19.82$\pm$0.08 | 20.15$\pm$0.08 | 18.29$\pm$0.08 | 16.0$\pm$0.08 | 19.5$\pm$0.08 | 19.84$\pm$0.11 | 18.24$\pm$0.1 | 20.37$\pm$0.11
54 | 12:28:15.75 +44:06:27.98 | 25.19$\pm$0.17 | 24.46$\pm$0.11 | 24.34$\pm$0.1 | 24.44$\pm$0.16 | 23.78$\pm$0.1 | 20.86$\pm$0.12 | 23.74$\pm$0.1 | 24.27$\pm$0.36 | 22.58$\pm$0.2 | 23.94$\pm$0.4
55 | 12:28:15.87 +44:06:29.97 | 21.36$\pm$0.08 | 21.38$\pm$0.08 | 21.37$\pm$0.08 | 21.45$\pm$0.08 | 21.44$\pm$0.09 | 21.56$\pm$0.36 | 21.72$\pm$0.08 | 22.82$\pm$0.15 | 22.48$\pm$0.27 | 23.36$\pm$0.17
56 | 12:28:16.35 +44:06:32.06 | 22.8$\pm$0.09 | 22.87$\pm$0.09 | 23.21$\pm$0.09 | 23.4$\pm$0.1 | 23.07$\pm$0.09 | 21.08$\pm$0.12 | 23.78$\pm$0.11 | 24.97$\pm$0.44 | 23.52$\pm$0.23 | 27.32$\pm$5.02
57 | 12:28:10.23 +44:06:35.85 | 20.34$\pm$0.08 | 20.32$\pm$0.08 | 19.7$\pm$0.08 | 19.78$\pm$0.08 | 19.72$\pm$0.08 | 18.62$\pm$0.08 | 20.1$\pm$0.08 | 21.1$\pm$0.1 | 20.43$\pm$0.09 | 21.43$\pm$0.09
58 | 12:28:10.86 +44:06:43.57 | 21.78$\pm$0.08 | 21.84$\pm$0.08 | 22.0$\pm$0.08 | 22.38$\pm$0.08 | 21.82$\pm$0.08 | 19.55$\pm$0.08 | 22.53$\pm$0.08 | 23.27$\pm$0.11 | 22.04$\pm$0.11 | 23.7$\pm$0.12
59 | 12:28:15.11 +44:06:45.30 | 24.3$\pm$0.14 | 23.87$\pm$0.09 | 23.62$\pm$0.09 | 23.9$\pm$0.15 | 22.34$\pm$0.08 | 20.03$\pm$0.08 | 23.12$\pm$0.09 | 23.26$\pm$0.12 | 21.89$\pm$0.11 | 23.54$\pm$0.16
60 | 12:28:12.25 +44:06:46.88 | 22.22$\pm$0.08 | 22.4$\pm$0.08 | 22.49$\pm$0.08 | 22.93$\pm$0.09 | 22.63$\pm$0.09 | 21.78$\pm$0.19 | 23.28$\pm$0.09 | 24.48$\pm$0.21 | 24.09$\pm$0.46 | 24.85$\pm$0.36
61 | 12:28:13.90 +44:06:49.11 | 20.52$\pm$0.08 | 20.75$\pm$0.08 | 20.86$\pm$0.08 | 21.16$\pm$0.08 | 21.06$\pm$0.09 | 20.45$\pm$0.2 | 21.53$\pm$0.08 | 22.58$\pm$0.15 | 22.08$\pm$0.25 | 22.92$\pm$0.18
62 | 12:28:13.05 +44:06:49.87 | 22.04$\pm$0.08 | 21.75$\pm$0.08 | 21.61$\pm$0.08 | 21.56$\pm$0.08 | 21.62$\pm$0.08 | 20.18$\pm$0.08 | 21.95$\pm$0.08 | 22.53$\pm$0.14 | 21.55$\pm$0.1 | 22.49$\pm$0.19
63 | 12:28:15.92 +44:06:29.60 | 25.24$\pm$0.32 | 24.83$\pm$0.25 | 24.56$\pm$0.21 | 24.52$\pm$0.25 | 24.39$\pm$0.21 | 21.11$\pm$0.17 | 24.38$\pm$0.22 | 24.54$\pm$0.37 | 22.77$\pm$0.17 | 24.68$\pm$0.36
64 | 12:28:12.95 +44:04:59.27 | 19.11$\pm$0.08 | 19.3$\pm$0.08 | 19.57$\pm$0.08 | 19.92$\pm$0.08 | 19.86$\pm$0.09 | 19.17$\pm$0.25 | 20.41$\pm$0.08 | 21.48$\pm$0.17 | 20.97$\pm$0.34 | 21.84$\pm$0.17
65 | 12:28:12.65 +44:04:58.85 | 24.9$\pm$0.36 | 24.09$\pm$0.22 | 24.13$\pm$0.21 | 23.94$\pm$0.14 | 23.16$\pm$0.13 | 20.28$\pm$0.11 | 23.19$\pm$0.1 | 23.42$\pm$0.24 | 21.88$\pm$0.14 | 23.36$\pm$0.15
66 | 12:28:12.56 +44:05:00.26 | 23.08$\pm$0.09 | 22.87$\pm$0.09 | 22.82$\pm$0.09 | 23.28$\pm$0.1 | 22.25$\pm$0.09 | 19.6$\pm$0.09 | 23.02$\pm$0.11 | 23.42$\pm$0.23 | 22.09$\pm$0.18 | 23.25$\pm$0.22
67 | 12:28:14.47 +44:05:01.79 | 22.14$\pm$0.08 | 22.2$\pm$0.09 | 22.27$\pm$0.09 | 22.68$\pm$0.09 | 22.05$\pm$0.08 | 19.67$\pm$0.08 | 22.74$\pm$0.09 | 23.41$\pm$0.2 | 22.2$\pm$0.12 | 23.52$\pm$0.33
68 | 12:28:09.06 +44:05:10.49 | 24.51$\pm$0.26 | 24.08$\pm$0.16 | 23.27$\pm$0.12 | 23.31$\pm$0.19 | 22.65$\pm$0.11 | 20.64$\pm$0.15 | 22.12$\pm$0.1 | 21.88$\pm$0.13 | 21.22$\pm$0.12 | 21.44$\pm$0.13
69 | 12:28:09.17 +44:05:12.32 | 20.86$\pm$0.09 | 21.12$\pm$0.09 | 21.32$\pm$0.09 | 21.84$\pm$0.1 | 21.63$\pm$0.1 | 20.05$\pm$0.1 | 22.07$\pm$0.1 | 22.7$\pm$0.2 | 22.04$\pm$0.17 | 22.75$\pm$0.24
70 | 12:28:14.84 +44:05:13.00 | 20.09$\pm$0.08 | 20.36$\pm$0.08 | 20.53$\pm$0.08 | 20.88$\pm$0.08 | 20.77$\pm$0.09 | 20.77$\pm$0.43 | 21.41$\pm$0.08 | 22.48$\pm$0.16 | 21.94$\pm$0.36 | 22.71$\pm$0.16
71 | 12:28:10.53 +44:05:15.90 | 25.91$\pm$1.09 | 26.28$\pm$1.09 | 25.78$\pm$1.09 | 24.14$\pm$0.37 | 24.6$\pm$0.5 | 22.46$\pm$0.27 | 21.75$\pm$0.1 | 21.38$\pm$0.11 | 20.83$\pm$0.11 | 20.91$\pm$0.11
72 | 12:28:09.06 +44:05:15.74 | 22.4$\pm$0.09 | 22.33$\pm$0.09 | 22.23$\pm$0.09 | 22.19$\pm$0.09 | 22.03$\pm$0.09 | 19.97$\pm$0.11 | 21.93$\pm$0.08 | 22.67$\pm$0.17 | 21.58$\pm$0.13 | 22.58$\pm$0.24
73 | 12:28:09.41 +44:05:16.02 | 21.67$\pm$0.09 | 21.66$\pm$0.09 | 21.8$\pm$0.09 | 22.13$\pm$0.1 | 21.73$\pm$0.1 | 18.94$\pm$0.09 | 22.06$\pm$0.1 | 22.93$\pm$0.3 | 21.35$\pm$0.17 | 23.2$\pm$0.61
74 | 12:28:09.22 +44:05:17.63 | 21.58$\pm$0.09 | 21.54$\pm$0.09 | 21.77$\pm$0.08 | 22.2$\pm$0.09 | 21.13$\pm$0.08 | 18.47$\pm$0.09 | 21.8$\pm$0.09 | 22.15$\pm$0.17 | 20.71$\pm$0.14 | 22.54$\pm$0.3
75 | 12:28:09.34 +44:05:17.99 | 24.08$\pm$0.15 | 24.01$\pm$0.12 | 23.8$\pm$0.11 | 23.77$\pm$0.16 | 23.46$\pm$0.12 | 20.69$\pm$0.09 | 23.35$\pm$0.14 | 25.1$\pm$2.27 | 22.36$\pm$0.2 | 23.68$\pm$1.09
76 | 12:28:12.92 +44:05:20.08 | 22.53$\pm$0.08 | 22.48$\pm$0.09 | 22.4$\pm$0.08 | 22.2$\pm$0.08 | 22.58$\pm$0.11 | 22.89$\pm$1.09 | 22.79$\pm$0.09 | 23.79$\pm$0.22 | 24.06$\pm$0.85 | 24.0$\pm$0.29
77 | 12:28:08.95 +44:05:22.19 | 25.3$\pm$0.3 | 24.68$\pm$0.19 | 24.13$\pm$0.17 | 24.18$\pm$0.18 | 23.82$\pm$0.14 | 20.96$\pm$0.1 | 23.94$\pm$0.14 | 24.86$\pm$0.91 | 22.51$\pm$0.24 | 24.85$\pm$2.0
78 | 12:28:13.61 +44:05:24.95 | 22.37$\pm$0.08 | 22.3$\pm$0.08 | 22.22$\pm$0.08 | 22.29$\pm$0.08 | 22.25$\pm$0.08 | 21.74$\pm$0.19 | 22.5$\pm$0.08 | 23.48$\pm$0.15 | 23.07$\pm$0.19 | 24.15$\pm$0.33
79 | 12:28:13.65 +44:05:28.32 | 22.76$\pm$0.1 | 22.43$\pm$0.1 | 22.5$\pm$0.1 | 22.73$\pm$0.1 | 21.73$\pm$0.11 | 19.34$\pm$0.11 | 22.59$\pm$0.12 | 22.95$\pm$0.34 | 21.42$\pm$0.24 | 23.76$\pm$1.0
80 | 12:28:13.72 +44:05:27.72 | 25.18$\pm$0.2 | 24.42$\pm$0.12 | 24.26$\pm$0.11 | 24.08$\pm$0.12 | 23.52$\pm$0.1 | 20.72$\pm$0.1 | 23.38$\pm$0.1 | 23.48$\pm$0.26 | 22.08$\pm$0.17 | 23.2$\pm$0.39
81 | 12:28:10.90 +44:05:32.62 | 19.83$\pm$0.08 | 19.78$\pm$0.08 | 19.55$\pm$0.09 | 19.56$\pm$0.09 | 19.55$\pm$0.09 | 19.49$\pm$0.1 | 19.05$\pm$0.08 | 19.1$\pm$0.18 | 18.67$\pm$0.19 | 18.72$\pm$0.2
82 | 12:28:11.11 +44:05:38.21 | 23.0$\pm$0.68 | 22.58$\pm$0.47 | 22.1$\pm$0.4 | 21.23$\pm$0.19 | 21.32$\pm$0.24 | 19.34$\pm$0.21 | 19.73$\pm$0.11 | 19.82$\pm$0.27 | 19.07$\pm$0.19 | 19.4$\pm$0.26
83 | 12:28:12.76 +44:05:40.71 | 21.45$\pm$0.08 | 21.6$\pm$0.08 | 21.56$\pm$0.08 | 21.92$\pm$0.09 | 21.67$\pm$0.08 | 19.66$\pm$0.09 | 22.22$\pm$0.11 | 23.13$\pm$0.38 | 22.13$\pm$0.26 | 23.4$\pm$0.73
84 | 12:28:11.84 +44:05:41.03 | 22.8$\pm$0.12 | 22.41$\pm$0.11 | 22.35$\pm$0.11 | 22.64$\pm$0.12 | 21.15$\pm$0.09 | 18.28$\pm$0.09 | 21.75$\pm$0.09 | 21.9$\pm$0.19 | 19.94$\pm$0.14 | 22.24$\pm$0.3
85 | 12:28:18.20 +44:05:46.70 | 21.84$\pm$0.08 | 21.97$\pm$0.08 | 22.09$\pm$0.08 | 22.28$\pm$0.08 | 22.01$\pm$0.09 | 20.42$\pm$0.1 | 22.6$\pm$0.08 | 23.44$\pm$0.15 | 22.27$\pm$0.14 | 23.77$\pm$0.2
86 | 12:28:11.96 +44:05:47.30 | 19.57$\pm$0.09 | 19.44$\pm$0.09 | 18.9$\pm$0.08 | 18.83$\pm$0.08 | 18.83$\pm$0.08 | 18.66$\pm$0.09 | 18.57$\pm$0.08 | 18.8$\pm$0.12 | 18.45$\pm$0.12 | 18.5$\pm$0.14
87 | 12:28:19.20 +44:06:00.64 | 22.81$\pm$0.09 | 22.46$\pm$0.08 | 22.69$\pm$0.09 | 22.91$\pm$0.09 | 22.3$\pm$0.09 | 20.08$\pm$0.1 | 23.02$\pm$0.09 | 23.59$\pm$0.18 | 22.45$\pm$0.13 | 23.75$\pm$0.36
88 | 12:28:13.57 +44:06:12.32 | 21.24$\pm$0.08 | 21.41$\pm$0.08 | 21.46$\pm$0.08 | 21.92$\pm$0.09 | 21.72$\pm$0.08 | 20.06$\pm$0.1 | 22.25$\pm$0.1 | 23.1$\pm$0.29 | 22.31$\pm$0.27 | 23.44$\pm$0.54
89 | 12:28:16.48 +44:06:22.64 | 23.92$\pm$0.1 | 23.45$\pm$0.09 | 23.4$\pm$0.09 | 23.59$\pm$0.1 | 22.52$\pm$0.08 | 19.73$\pm$0.08 | 23.02$\pm$0.09 | 23.58$\pm$0.27 | 21.95$\pm$0.14 | 24.07$\pm$0.43
90 | 12:28:16.02 +44:06:27.82 | 22.93$\pm$0.09 | 22.88$\pm$0.09 | 23.04$\pm$0.08 | 23.45$\pm$0.1 | 22.66$\pm$0.08 | 20.16$\pm$0.08 | 23.3$\pm$0.1 | 23.71$\pm$0.3 | 22.67$\pm$0.22 | 24.08$\pm$0.64
91 | 12:28:11.62 +44:06:31.15 | 22.04$\pm$0.08 | 22.08$\pm$0.08 | 22.25$\pm$0.08 | 22.67$\pm$0.09 | 22.15$\pm$0.09 | 19.86$\pm$0.11 | 22.7$\pm$0.09 | 23.66$\pm$0.31 | 22.15$\pm$0.17 | 23.96$\pm$0.69
92 | 12:28:14.06 +44:06:30.21 | 23.05$\pm$0.09 | 23.03$\pm$0.09 | 23.3$\pm$0.1 | 23.53$\pm$0.11 | 23.28$\pm$0.1 | 21.1$\pm$0.12 | 23.78$\pm$0.11 | 24.72$\pm$0.3 | 23.14$\pm$0.22 | 25.58$\pm$0.38
93 | 12:28:11.38 +44:06:31.43 | 21.31$\pm$0.08 | 21.11$\pm$0.08 | 21.13$\pm$0.08 | 20.9$\pm$0.08 | 20.98$\pm$0.08 | 19.47$\pm$0.08 | 20.6$\pm$0.08 | 20.99$\pm$0.11 | 20.39$\pm$0.1 | 20.73$\pm$0.12
94 | 12:28:11.63 +44:06:32.33 | 22.51$\pm$0.09 | 22.54$\pm$0.09 | 22.55$\pm$0.09 | 22.84$\pm$0.1 | 22.57$\pm$0.1 | 20.45$\pm$0.1 | 22.85$\pm$0.09 | 23.65$\pm$0.25 | 22.58$\pm$0.18 | 23.69$\pm$0.27
95 | 12:28:16.04 +44:06:38.03 | 22.69$\pm$0.09 | 22.61$\pm$0.08 | 22.61$\pm$0.08 | 22.65$\pm$0.08 | 22.5$\pm$0.08 | 20.35$\pm$0.1 | 22.71$\pm$0.08 | 23.5$\pm$0.14 | 22.58$\pm$0.3 | 23.94$\pm$0.4
96 | 12:28:15.92 +44:06:39.20 | 23.39$\pm$0.09 | 23.28$\pm$0.09 | 23.27$\pm$0.08 | 23.34$\pm$0.09 | 23.22$\pm$0.09 | 21.01$\pm$0.1 | 23.48$\pm$0.1 | 24.26$\pm$0.27 | 22.77$\pm$0.36 | 25.09$\pm$0.56
97 | 12:28:14.97 +44:06:46.12 | 25.93$\pm$0.33 | 25.08$\pm$0.15 | 24.69$\pm$0.13 | 24.01$\pm$0.11 | 24.12$\pm$0.12 | 21.91$\pm$0.21 | 23.26$\pm$0.1 | 23.29$\pm$0.15 | 22.26$\pm$0.18 | 23.12$\pm$0.13
98 | 12:28:11.52 +44:06:47.72 | 21.84$\pm$0.08 | 21.94$\pm$0.08 | 22.04$\pm$0.08 | 22.43$\pm$0.08 | 22.07$\pm$0.08 | 20.18$\pm$0.1 | 22.74$\pm$0.09 | 23.84$\pm$0.46 | 22.79$\pm$0.22 | 24.58$\pm$0.64
99 | 12:28:13.96 +44:06:49.28 | 20.95$\pm$0.09 | 20.99$\pm$0.08 | 21.16$\pm$0.09 | 21.62$\pm$0.09 | 20.57$\pm$0.08 | 18.42$\pm$0.09 | 21.62$\pm$0.09 | 22.3$\pm$0.13 | 20.95$\pm$0.15 | 22.62$\pm$0.14

††footnotetext: (1) The identification number of the source.

††footnotetext: (2) Right Ascension and Declination in J2000 coordinates.

††footnotetext: (3-12) AB magnitude of each source in the corresponding filter
as determined by performing aperture photometry.

Table 4: Source Properties

ID | Age | E(B-V) | Log(Mass) | $\chi^{2}_{red}$
---|---|---|---|---
| Myr | mag | M⊙ |
1 | $5^{+0}_{-0}$ | $0.0^{+0.04}_{-0.0}$ | $2.54^{+0.057}_{-0.0}$ | 3.74
2 | $8^{+6}_{-1}$ | $0.46^{+0.16}_{-0.14}$ | $2.99^{+0.514}_{-0.277}$ | 8.26
3 | $3^{+1}_{-0}$ | $0.46^{+0.1}_{-0.08}$ | $3.12^{+0.223}_{-0.098}$ | 5.13
4 | $5^{+0}_{-0}$ | $0.0^{+0.04}_{-0.0}$ | $2.8^{+0.055}_{-0.0}$ | 15.41
5 | $9000^{+0}_{-8994}$ | $0.04^{+0.92}_{-0.04}$ | $4.53^{+0.085}_{-1.909}$ | 2.44
6 | $4^{+1}_{-0}$ | $0.8^{+0.24}_{-0.26}$ | $2.92^{+0.238}_{-0.192}$ | 8.92
7 | $5^{+0}_{-1}$ | $0.0^{+0.08}_{-0.0}$ | $2.86^{+0.119}_{-0.082}$ | 7.64
8 | $9000^{+0}_{-8994}$ | $0.0^{+0.96}_{-0.0}$ | $4.81^{+0.097}_{-1.949}$ | 5.9
9 | $7^{+5}_{-2}$ | $0.2^{+0.36}_{-0.14}$ | $2.5^{+0.638}_{-0.199}$ | 10.16
10 | $2000^{+1000}_{-1940}$ | $0.0^{+0.68}_{-0.0}$ | $4.16^{+0.192}_{-0.444}$ | 2.78
11 | $4^{+0}_{-0}$ | $0.0^{+0.08}_{-0.0}$ | $2.7^{+0.116}_{-0.0}$ | 4.45
12 | $9^{+31}_{-2}$ | $0.56^{+0.16}_{-0.14}$ | $3.56^{+0.554}_{-0.368}$ | 7.76
13 | $6^{+8994}_{-1}$ | $1.32^{+0.22}_{-0.92}$ | $3.41^{+1.58}_{-0.388}$ | 8.9
14 | $4^{+0}_{-1}$ | $0.3^{+0.08}_{-0.12}$ | $2.66^{+0.106}_{-0.264}$ | 5.56
15 | $5^{+9}_{-0}$ | $0.0^{+0.08}_{-0.0}$ | $2.48^{+0.434}_{-0.035}$ | 16.64
16 | $5^{+0}_{-0}$ | $0.0^{+0.08}_{-0.0}$ | $3.33^{+0.103}_{-0.0}$ | 10.59
17 | $6^{+8994}_{-1}$ | $0.78^{+0.22}_{-0.78}$ | $2.85^{+1.598}_{-0.395}$ | 1.86
18 | $4^{+0}_{-2}$ | $0.3^{+0.08}_{-0.12}$ | $3.52^{+0.107}_{-0.227}$ | 4.55
19 | $4^{+0}_{-0}$ | $0.72^{+0.14}_{-0.16}$ | $2.55^{+0.164}_{-0.187}$ | 1.23
20 | $9^{+11}_{-2}$ | $0.7^{+0.16}_{-0.14}$ | $3.18^{+0.368}_{-0.273}$ | 4.17
21 | $9^{+5}_{-2}$ | $0.32^{+0.1}_{-0.14}$ | $4.09^{+0.373}_{-0.384}$ | 6.8
22 | $7^{+3}_{-1}$ | $0.76^{+0.24}_{-0.14}$ | $3.29^{+0.514}_{-0.139}$ | 13.31
23 | $9^{+5}_{-2}$ | $0.26^{+0.12}_{-0.12}$ | $3.16^{+0.369}_{-0.35}$ | 15.6
24 | $9000^{+0}_{-7000}$ | $0.42^{+0.2}_{-0.12}$ | $5.39^{+0.082}_{-0.505}$ | 14.0
25 | $4^{+0}_{-0}$ | $0.18^{+0.08}_{-0.08}$ | $3.37^{+0.114}_{-0.114}$ | 2.55
26 | $5^{+0}_{-1}$ | $0.8^{+0.1}_{-0.12}$ | $3.69^{+0.1}_{-0.165}$ | 13.0
27 | $5^{+0}_{-1}$ | $0.1^{+0.08}_{-0.08}$ | $2.6^{+0.114}_{-0.114}$ | 2.56
28 | $6^{+0}_{-0}$ | $0.16^{+0.08}_{-0.08}$ | $3.28^{+0.105}_{-0.105}$ | 9.82
29 | $6^{+8994}_{-0}$ | $1.2^{+0.14}_{-0.98}$ | $4.09^{+1.615}_{-0.444}$ | 11.01
30 | $5^{+0}_{-0}$ | $0.5^{+0.06}_{-0.08}$ | $4.41^{+0.072}_{-0.096}$ | 19.61
31 | $6^{+0}_{-1}$ | $0.0^{+0.08}_{-0.0}$ | $2.46^{+0.103}_{-0.05}$ | 5.12
32 | $1^{+0}_{-0}$ | $0.3^{+0.08}_{-0.08}$ | $3.45^{+0.101}_{-0.101}$ | 12.27
33 | $7^{+8993}_{-0}$ | $0.98^{+0.1}_{-0.72}$ | $3.54^{+1.9}_{-0.067}$ | 3.64
34 | $20^{+20}_{-12}$ | $0.46^{+0.16}_{-0.1}$ | $4.38^{+0.216}_{-0.544}$ | 5.57
35 | $5^{+0}_{-1}$ | $0.12^{+0.08}_{-0.1}$ | $3.0^{+0.112}_{-0.2}$ | 3.88
36 | $5^{+1}_{-0}$ | $0.04^{+0.1}_{-0.04}$ | $3.16^{+0.151}_{-0.085}$ | 6.34
37 | $5^{+0}_{-0}$ | $0.0^{+0.0}_{-0.0}$ | $3.21^{+0.0}_{-0.0}$ | 40.32
38 | $9^{+11}_{-2}$ | $0.36^{+0.12}_{-0.12}$ | $3.52^{+0.372}_{-0.355}$ | 24.66
39 | $6^{+9}_{-0}$ | $1.16^{+0.2}_{-0.48}$ | $3.34^{+0.247}_{-0.418}$ | 6.16
40 | $4^{+1}_{-0}$ | $0.16^{+0.06}_{-0.08}$ | $3.75^{+0.159}_{-0.099}$ | 14.67
41 | $6^{+3}_{-0}$ | $1.14^{+0.2}_{-0.44}$ | $4.02^{+0.099}_{-0.402}$ | 4.58
42 | $4^{+0}_{-1}$ | $0.06^{+0.08}_{-0.06}$ | $2.47^{+0.128}_{-0.184}$ | 2.18
43 | $9^{+21}_{-2}$ | $0.82^{+0.14}_{-0.14}$ | $3.27^{+0.458}_{-0.348}$ | 8.61
44 | $6^{+8994}_{-0}$ | $1.16^{+0.14}_{-0.98}$ | $4.08^{+1.589}_{-0.451}$ | 8.83
45 | $6^{+8994}_{-0}$ | $1.44^{+0.12}_{-0.94}$ | $3.64^{+1.598}_{-0.368}$ | 9.11
46 | $4^{+0}_{-0}$ | $0.08^{+0.08}_{-0.08}$ | $2.33^{+0.114}_{-0.114}$ | 2.85
47 | $5^{+0}_{-0}$ | $0.28^{+0.08}_{-0.08}$ | $3.06^{+0.111}_{-0.111}$ | 6.62
48 | $4^{+0}_{-0}$ | $0.06^{+0.08}_{-0.06}$ | $2.45^{+0.115}_{-0.087}$ | 5.0
49 | $7^{+8993}_{-1}$ | $0.88^{+0.4}_{-0.68}$ | $3.37^{+1.927}_{-0.098}$ | 8.59
50 | $6^{+74}_{-1}$ | $0.76^{+0.26}_{-0.32}$ | $2.97^{+0.793}_{-0.377}$ | 5.91
51 | $5^{+0}_{-0}$ | $0.32^{+0.08}_{-0.08}$ | $3.1^{+0.104}_{-0.104}$ | 5.99
52 | $5^{+0}_{-1}$ | $0.18^{+0.06}_{-0.08}$ | $3.26^{+0.079}_{-0.192}$ | 11.66
53 | $1^{+0}_{-0}$ | $0.28^{+0.08}_{-0.06}$ | $3.86^{+0.102}_{-0.076}$ | 16.44
54 | $3^{+1}_{-2}$ | $0.5^{+0.14}_{-0.14}$ | $2.23^{+0.288}_{-0.163}$ | 1.62
55 | $5^{+0}_{-0}$ | $0.06^{+0.08}_{-0.06}$ | $2.74^{+0.118}_{-0.089}$ | 7.95
56 | $4^{+0}_{-0}$ | $0.02^{+0.08}_{-0.02}$ | $1.96^{+0.128}_{-0.032}$ | 1.71
57 | $5^{+0}_{-0}$ | $0.14^{+0.08}_{-0.06}$ | $3.43^{+0.097}_{-0.073}$ | 17.82
58 | $4^{+0}_{-1}$ | $0.04^{+0.08}_{-0.04}$ | $2.45^{+0.104}_{-0.15}$ | 5.18
59 | $1^{+1}_{-0}$ | $0.38^{+0.1}_{-0.08}$ | $2.47^{+0.121}_{-0.097}$ | 7.3
60 | $5^{+0}_{-0}$ | $0.0^{+0.04}_{-0.0}$ | $2.17^{+0.062}_{-0.0}$ | 5.73
61 | $5^{+0}_{-0}$ | $0.0^{+0.04}_{-0.0}$ | $2.83^{+0.059}_{-0.0}$ | 7.04
62 | $5^{+0}_{-0}$ | $0.22^{+0.08}_{-0.06}$ | $2.89^{+0.105}_{-0.079}$ | 4.12
63 | $3^{+1}_{-2}$ | $0.46^{+0.24}_{-0.22}$ | $2.04^{+0.406}_{-0.218}$ | 0.35
64 | $5^{+0}_{-0}$ | $0.0^{+0.02}_{-0.0}$ | $3.35^{+0.03}_{-0.0}$ | 10.28
65 | $3^{+1}_{-2}$ | $0.56^{+0.18}_{-0.2}$ | $2.5^{+0.272}_{-0.164}$ | 2.32
66 | $1^{+2}_{-0}$ | $0.18^{+0.14}_{-0.1}$ | $2.4^{+0.146}_{-0.146}$ | 2.37
67 | $4^{+0}_{-1}$ | $0.12^{+0.08}_{-0.12}$ | $2.47^{+0.113}_{-0.272}$ | 3.57
68 | $5^{+0}_{-0}$ | $0.88^{+0.14}_{-0.12}$ | $3.32^{+0.134}_{-0.115}$ | 4.34
69 | $5^{+0}_{-0}$ | $0.0^{+0.06}_{-0.0}$ | $2.66^{+0.089}_{-0.0}$ | 2.71
70 | $5^{+0}_{-1}$ | $0.0^{+0.02}_{-0.0}$ | $2.96^{+0.03}_{-0.038}$ | 8.63
71 | $13^{+2987}_{-2}$ | $0.88^{+0.08}_{-0.54}$ | $3.66^{+1.009}_{-0.108}$ | 4.68
72 | $5^{+0}_{-1}$ | $0.32^{+0.1}_{-0.08}$ | $2.89^{+0.138}_{-0.184}$ | 4.79
73 | $3^{+0}_{-0}$ | $0.12^{+0.08}_{-0.08}$ | $2.6^{+0.12}_{-0.12}$ | 1.66
74 | $1^{+2}_{-0}$ | $0.12^{+0.1}_{-0.08}$ | $2.8^{+0.113}_{-0.124}$ | 1.78
75 | $4^{+0}_{-1}$ | $0.46^{+0.12}_{-0.2}$ | $2.46^{+0.166}_{-0.4}$ | 2.43
76 | $6^{+0}_{-1}$ | $0.0^{+0.24}_{-0.0}$ | $2.22^{+0.409}_{-0.0}$ | 7.28
77 | $3^{+1}_{-2}$ | $0.48^{+0.28}_{-0.18}$ | $2.17^{+0.481}_{-0.215}$ | 1.56
78 | $6^{+0}_{-0}$ | $0.0^{+0.06}_{-0.0}$ | $2.28^{+0.089}_{-0.0}$ | 4.85
79 | $2^{+1}_{-1}$ | $0.24^{+0.12}_{-0.12}$ | $2.62^{+0.155}_{-0.155}$ | 2.71
80 | $3^{+1}_{-2}$ | $0.58^{+0.2}_{-0.16}$ | $2.41^{+0.356}_{-0.177}$ | 2.67
81 | $20^{+20}_{-11}$ | $0.16^{+0.14}_{-0.14}$ | $4.26^{+0.164}_{-0.434}$ | 4.29
82 | $6^{+1}_{-1}$ | $1.1^{+0.34}_{-0.5}$ | $4.17^{+0.361}_{-0.47}$ | 2.99
83 | $4^{+1}_{-0}$ | $0.06^{+0.08}_{-0.06}$ | $2.6^{+0.207}_{-0.093}$ | 2.46
84 | $1^{+1}_{-0}$ | $0.44^{+0.1}_{-0.1}$ | $3.13^{+0.129}_{-0.129}$ | 4.16
85 | $5^{+0}_{-1}$ | $0.06^{+0.08}_{-0.06}$ | $2.49^{+0.112}_{-0.16}$ | 3.76
86 | $12^{+88}_{-4}$ | $0.26^{+0.08}_{-0.22}$ | $4.43^{+0.422}_{-0.506}$ | 1.37
87 | $4^{+0}_{-0}$ | $0.18^{+0.08}_{-0.08}$ | $2.42^{+0.114}_{-0.114}$ | 3.98
88 | $5^{+0}_{-0}$ | $0.0^{+0.08}_{-0.0}$ | $2.59^{+0.123}_{-0.0}$ | 1.11
89 | $1^{+1}_{-0}$ | $0.34^{+0.1}_{-0.08}$ | $2.49^{+0.113}_{-0.122}$ | 3.0
90 | $3^{+1}_{-2}$ | $0.14^{+0.08}_{-0.14}$ | $2.15^{+0.216}_{-0.151}$ | 2.63
91 | $4^{+0}_{-0}$ | $0.1^{+0.08}_{-0.08}$ | $2.45^{+0.12}_{-0.121}$ | 1.42
92 | $4^{+0}_{-0}$ | $0.06^{+0.1}_{-0.06}$ | $1.97^{+0.156}_{-0.094}$ | 2.33
93 | $6^{+0}_{-1}$ | $0.26^{+0.14}_{-0.08}$ | $3.26^{+0.202}_{-0.101}$ | 11.79
94 | $4^{+1}_{-0}$ | $0.14^{+0.08}_{-0.1}$ | $2.36^{+0.196}_{-0.146}$ | 2.54
95 | $4^{+1}_{-0}$ | $0.18^{+0.1}_{-0.08}$ | $2.43^{+0.184}_{-0.116}$ | 5.91
96 | $4^{+1}_{-0}$ | $0.2^{+0.08}_{-0.08}$ | $2.18^{+0.133}_{-0.122}$ | 3.63
97 | $5^{+0}_{-1}$ | $0.88^{+0.14}_{-0.14}$ | $2.81^{+0.141}_{-0.141}$ | 2.44
98 | $4^{+1}_{-0}$ | $0.02^{+0.08}_{-0.02}$ | $2.36^{+0.194}_{-0.03}$ | 2.75
99 | $2^{+2}_{-1}$ | $0.0^{+0.14}_{-0.0}$ | $2.74^{+0.223}_{-0.059}$ | 6.88

††footnotetext: ID number, age, color excess, mass, and reduced $\chi^{2}$
obtained from SED fitting.