# High Tide or Riptide on the Cosmic Shoreline? A Water-Rich Atmosphere or
Stellar Contamination for the Warm Super-Earth GJ 486b from JWST Observations

Sarah E. Moran Department of Planetary Sciences and Lunar and Planetary
Laboratory, University of Arizona, Tuscon, AZ, USA Sarah E. Moran & Kevin B.
Stevenson<EMAIL_ADDRESS><EMAIL_ADDRESS>Kevin B.
Stevenson Johns Hopkins APL, Laurel, MD, 20723, USA  David K. Sing Department
of Earth & Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD,
USA  Ryan J. MacDonald Department of Astronomy, University of Michigan, 1085
S. University Ave., Ann Arbor, MI 48109, USA  NHFP Sagan Fellow  James Kirk
Department of Physics, Imperial College London, Prince Consort Road, London,
SW7 2AZ, UK  Jacob Lustig-Yaeger Johns Hopkins APL, Laurel, MD, 20723, USA
Sarah Peacock University of Maryland, Baltimore County, MD 21250, USA  NASA
Goddard Space Flight Center, Greenbelt, MD 20771, USA  L. C. Mayorga Johns
Hopkins APL, Laurel, MD, 20723, USA  Katherine A. Bennett Department of Earth
& Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA  Mercedes
López-Morales Center for Astrophysics ${\rm\mid}$ Harvard & Smithsonian, 60
Garden St, Cambridge, MA 02138, USA  E. M. May Johns Hopkins APL, Laurel, MD,
20723, USA  Zafar Rustamkulov Department of Earth & Planetary Sciences, Johns
Hopkins University, Baltimore, MD, USA  Jeff A. Valenti Space Telescope
Science Institute, Baltimore, MD 21218, USA  Jéa I. Adams Redai Center for
Astrophysics ${\rm\mid}$ Harvard & Smithsonian, 60 Garden St, Cambridge, MA
02138, USA  Munazza K. Alam Carnegie Earth & Planets Laboratory, Washington,
DC, 20015, USA  Natasha E. Batalha NASA Ames Research Center, Moffett Field,
CA, USA  Guangwei Fu Department of Physics & Astronomy, Johns Hopkins
University, Baltimore, MD, USA  Junellie Gonzalez-Quiles Department of Earth &
Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA  Alicia N.
Highland Department of Astronomy, University of Michigan, 1085 S. University
Ave., Ann Arbor, MI 48109, USA  Ethan Kruse NASA Goddard Space Flight Center,
Greenbelt, MD 20771, USA  Department of Astronomy, University of Maryland,
College Park, MD 20742.  Center for Research and Exploration in Space Science
and Technology, NASA/GSFC, Greenbelt, MD 20771  Joshua D. Lothringer
Department of Physics, Utah Valley University, Orem, UT, 84058 USA  Kevin N.
Ortiz Ceballos Center for Astrophysics ${\rm\mid}$ Harvard & Smithsonian, 60
Garden St, Cambridge, MA 02138, USA  Kristin S. Sotzen Johns Hopkins APL,
Laurel, MD, 20723, USA  Department of Earth & Planetary Sciences, Johns
Hopkins University, Baltimore, MD, USA  Hannah R. Wakeford School of Physics,
HH Wills Physics Laboratory, University of Bristol, Bristol, UK

(Accepted April 7, 2023)

###### Abstract

Planets orbiting M-dwarf stars are prime targets in the search for rocky
exoplanet atmospheres. The small size of M dwarfs renders their planets
exceptional targets for transmission spectroscopy, facilitating atmospheric
characterization. However, it remains unknown whether their host stars’ highly
variable extreme-UV radiation environments allow atmospheres to persist. With
JWST, we have begun to determine whether or not the most favorable rocky
worlds orbiting M dwarfs have detectable atmospheres. Here, we present a
2.8–-5.2 $\micron$ JWST NIRSpec/G395H transmission spectrum of the warm (700
K, 40.3$\times$ Earth’s insolation) super-Earth GJ 486b (1.3 R⊕ and 3.0 M⊕).
The measured spectrum from our two transits of GJ 486b deviates from a flat
line at $2.2-3.3\sigma$, based on three independent reductions. Through a
combination of forward and retrieval models, we determine that GJ 486b either
has a water-rich atmosphere (with the most stringent constraint on the
retrieved water abundance of H2O $>$ 10% to 2$\sigma$) or the transmission
spectrum is contaminated by water present in cool unocculted starspots. We
also find that the measured stellar spectrum is best fit by a stellar model
with cool starspots and hot faculae. While both retrieval scenarios provide
equal quality fits ($\chi^{2}_{\nu}=1.0$) to our NIRSpec/G395H observations,
shorter wavelength observations can break this degeneracy and reveal if GJ
486b sustains a water-rich atmosphere.

JWST, Terrestrial Exoplanet Atmospheres, Transmission Spectroscopy

††journal: ApJL††facilities: JWST(NIRSpec)††software:  Astropy (Astropy
Collaboration et al., 2013, 2018), batman (Kreidberg, 2015), CHIMERA (Line &
Yung, 2013; Line et al., 2014), Dynesty (Speagle, 2020), emcee (Foreman-Mackey
et al., 2013), Eureka! (Bell et al., 2022), ExoCTK (Bourque et al., 2021),
FIREFLy (Rustamkulov et al., 2022), Forecaster (Chen & Kipping, 2017), IPython
(Pérez & Granger, 2007), jwst (Bushouse et al., 2022), Matplotlib (Hunter,
2007), NumPy (van der Walt et al., 2011; Harris et al., 2020), PHOENIX (Allard
et al., 2012) PICASO (Batalha et al., 2019), POSEIDON (MacDonald &
Madhusudhan, 2017; MacDonald, 2023), PyMC3 (Salvatier et al., 2016),
pysynphot(STScI Development Team, 2013), rfast (Robinson & Salvador, 2023),
SciPy (Virtanen et al., 2020), Tiberius (Kirk et al., 2019, 2021)

##  1 Introduction

Understanding the stability and longevity of atmospheres on rocky planets
orbiting M dwarfs is paramount for understanding which, if any, of these
planets may ultimately support life. However, given the high activity of most
M-dwarf stars (e.g., Peacock et al., 2019), their planets are subject to
extreme-UV radiation regimes that may remove any significant atmosphere
through escape processes (e.g., Airapetian et al., 2020; Kasting & Pollack,
1983; Zahnle & Catling, 2017; Airapetian et al., 2017). This high activity
also persists over much longer timescales given the long lifetimes of M dwarfs
compared to larger stars (e.g., Loyd et al., 2021). M dwarfs also have the
potential to impart spurious features into the transmission spectrum from
inhomogenities in the stellar photosphere, a phenomenon called the “Transit
Light Source effect” (TLS) (Rackham et al., 2018), also known as stellar
contamination (Apai et al., 2018; Barclay et al., 2021; Garcia et al., 2022;
Barclay et al., 2023).

Rocky worlds ($\leq$ 1.4R⊕) are not predicted to retain hydrogen/helium-
dominated atmospheres (Rogers, 2015; Rogers et al., 2021). This has been
confirmed by observations of terrestrial planets, including the TRAPPIST-1
planets (de Wit et al., 2016, 2018; Wakeford et al., 2019; Garcia et al.,
2022; Gressier et al., 2022), GJ 1132b (Diamond-Lowe et al., 2018; Mugnai et
al., 2021; Libby-Roberts et al., 2022), the L98-59 system (Damiano et al.,
2022; Zhou et al., 2023), LTT 1445Ab (Diamond-Lowe et al., 2022) and LHS 3488b
(Kreidberg et al., 2019; Diamond-Lowe et al., 2020). However, many of these
observations do not preclude higher mean molecular weight secondary
atmospheres for these small planets (Moran et al., 2018; Damiano et al.,
2022).

As part of the Cycle 1 JWST General Observer (GO) Program 1981 (PIs: K.
Stevenson & J. Lustig-Yaeger), we are searching for atmospheric signatures on
rocky planets around M dwarfs. Our program focuses reconnaissance on carbon
dioxide (CO2) and methane (CH4), believed to produce the strongest signals in
terrestrial atmospheres (Kaltenegger & Traub, 2009; Lustig-Yaeger et al.,
2019). Both have strong bands between 3 and 5 $\mu$m, which can be probed by
JWST. Secondary atmospheric CO2 is also potentially common across a range of
terrestrial planetary conditions via outgassing (Lincowski et al., 2018), as
seen on Venus, Earth, and Mars. Using JWST, Program 1981 has already enabled a
strong constraint on Earth-sized exoplanet LHS 475b, ruling out Earth-like,
hydrogen/helium, water, or methane-dominated clear atmospheres Lustig-
YaegerFu2023(Lustig-Yaeger & Fu et al. 2023).

Our ultimate aim is to trace the proposed cosmic shoreline, defined by Zahnle
& Catling (2017). The cosmic shoreline describes the relationship between a
planet’s escape velocity (${\rm v_{esc}}$) and insolation (${\rm I}$). This
“shoreline” divides rocky bodies with atmospheres from those without and is
shaped by various processes that cause atmospheric loss. In the solar system,
this relationship follows ${\rm I\propto v{{}_{esc}}^{4}}$, suggesting that
atmospheric escape mechanisms are dominated by thermal processes (Zahnle &
Catling, 2017). Both thermal processes, such as Jeans escape and hydrodynamic
escape, and non-thermal processes, encompassing photochemical escape and ion
escape, cause composition-dependent atmospheric loss. These escape processes
can be enhanced in planets around active stars through UV flaring or stellar
winds. Thus, to understand any putative cosmic shoreline in the solar system
or beyond, it is important to determine not only how planet size, mass, and
atmospheric composition affect a planet’s ability to retain an atmosphere, but
also the effect of the host star’s activity. These varying factors can reveal
the mechanisms dominating atmospheric escape on a given world (e.g.,
Wordsworth & Kreidberg, 2022; McIntyre et al., 2023).

Here we present the results of our JWST-GO-1981 program observations for GJ
486b, a 1.3 R⊕ and 3.0 M⊕ planet (Caballero et al., 2022), with a zero Bond
albedo equilibrium temperature of 700 K. GJ 486b has one of the highest
transmission spectroscopy metrics (Kempton et al., 2018) of any known
terrestrial exoplanet (Trifonov et al., 2021), making it a favorable target
for study. The measured mass and radius indicate that GJ 486b is likely
composed of a small metallic core, a deep silicate mantle, and a thin volatile
upper layer (Caballero et al., 2022), which could be resistant to escape given
the quiescent M3.5 V host star (0.339 R⊙, Teff = 3291 K; Caballero et al.,
2022). Recent high-resolution observations of GJ 486b show that the planet
does not possess a clear 1$\times$ solar atmosphere dominated by
hydrogen/helium to high confidence ($\geq 5\sigma$). These observations also
suggest that a clear, pure water atmosphere could be ruled out to low
significance ($\leq 3\sigma$) (Ridden-Harper et al., 2022). We contextualize
these observations in light of our own findings in Section 5.

##  2 JWST Observations of GJ 486b

We observed two transits of GJ 486b using the Near InfraRed Spectrograph
(NIRSpec; Jakobsen et al., 2022; Birkmann et al., 2022) G395H instrument mode,
covering wavelengths $2.87-5.14$ m at an average native spectral resolution
$\mathcal{R}$ $\sim$ 2700.The G395H grating is split over two detectors, NRS1
and NRS2, with a gap from 3.72 to 3.82 $\mu$m. The first transit observation
commenced on 25 December 2022 at 11:38 UTC and the second on 29 December 2022
at 21:15 UTC. Each observation lasted 3.53 hours, which covered the 1.01 hour
transit duration and the required baseline. Both observations used the NIRSpec
Bright Object Time Series (BOTS) mode with the NRSRAPID readout pattern,
S1600A1 slit, and the SUB2048 subarray. For this bright target (Kmag = 6.4),
we used 3 groups per integration and obtained 3507 integrations per exposure.

##  3 NIRSpec G395H Data Reduction

We reduced the data using three separate pipelines: Eureka! (Bell et al.,
2022), FIREFLy (Rustamkulov et al., 2022, 2023), and Tiberius (Kirk et al.,
2018, 2019, 2021). Each pipeline analysis is described below. Appendix A
contains the updated system parameters obtained from each reduction. The three
reductions showed a consistent offset in the measured transit depth for the
Transit 1, NRS2 detector relative to the other three white light curve depths.
We rule out astrophysical effects for this discrepancy and corrected it in
each reduction as described in Appendix A.1.

###  3.1 Eureka!

We use a modified version of the jwst Stage 1 pipeline, starting from the
_uncal.fits files. We perform group-level background subtraction before
determining the flux per integration. For each group, we exclude the region
within 9 pixels of the trace before computing and subtracting a median
background value per pixel column. We process the _rateints.fits files through
the regular jwst Stage 2 pipeline, skipping the flat fielding and absolute
photometric calibration steps when our goal is to derive the planet’s spectrum
at later stages. Conversely, we include these steps when our goal is to
compute the flux-calibrated stellar spectrum (see Section 4.4). Stage 3 of
Eureka! converts the time-series of 2D integrations into 1D spectra using
optimal spectral extraction (Horne, 1986) and an aperture within 5 pixels of
the trace. We flag bad pixels at numerous points within this stage using
thresholds optimized to minimize scatter in the white light curves.

For the NRS1 detector, we extract the flux from 2.777 – 3.717 m and split the
light into 47 spectroscopic light curves, each 20 nm (0.02 m) in width. For
the NRS2 detector, we adopt the same resolution in extracting 67 spectroscopic
light curves spanning 3.825 – 5.165 m. For each detector, we manually mask 9
pixel columns that exhibit significant scatter in their individual light
curves. Doing so improves the quality of the spectroscopic light curves and
yields more consistent transit depths.

With two NIRSpec detectors and two transit observations, we fit four white
light curves and their systematics (see Figure 1). We determine the system
parameters using batman (Kreidberg, 2015) and fix the quadratic limb-darkening
coefficients to those provided by ExoTiC-LD (Grant & Wakeford, 2022), assuming
the stellar parameters given by Trifonov et al. (2021) and the MPS-ATLAS set 1
models (Kostogryz et al., 2023). For the NRS1 detector, we find that a
quadratic trend in time provides the best fit. For the NRS2 detector, a linear
trend suffices to remove systematics. Table 1 lists our best-fit system
parameters.

Figure 1: Eureka! spectroscopic and white light curves from two transits of GJ
486b. The top two rows contain the spectroscopic light curves (left), our
best-fit models (center), and subsequent residuals (right) for each transit.
Most evident in the data are wavelength-dependent ramps near 3.2 m that we
readily remove. The bottom row depicts the white light curves from each
detector (NRS1 and NRS2) before removing their systematic trends. Correlated
noise is evident in the residuals and is likely due to thermal cycling (Rigby
et al., 2022). The standard deviation of the normalized residuals is 140 ppm
for NRS1 and 165 ppm for NRS2. The complete figure set (3 images, one for each
reduction) is available in the online journal.

When fitting the spectroscopic light curves (see Figure 1), we fix the
planet’s transit midpoint, inclination, and semi-major axis to the weighted
mean values in Table 1. We fix the quadratic limb-darkening parameters to the
values provided by ExoTiC-LD for each spectroscopic channel. For the NRS1
detector, we also fix the quadratic term in our time-dependent systematic
model to that of the best-fit white light curve value (Transit 1:
$c_{2}=0.0335$, Transit 2: $c_{2}=0.0248$). For all spectroscopic light
curves, we fit for the zeroth and first-order terms ($c_{0}$ and $c_{1}$) of
our polynomial. Light curves from the NRS2 detector only require a linear
model in time. Including the term that rescales the uncertainties, each
spectroscopic light curve has four free parameters, of which only the planet-
to-star radius ratio is a physical parameter.

For each light curve, we first perform a least-squares minimization using the
Powell method (Powell, 1964) and then initialize our MCMC routine using our
best-fit values. We estimate the parameter uncertainties using emcee (Foreman-
Mackey et al., 2013) and, at each iteration, we increase the uncertainties by
an average factor of $\sim$1.5 to achieve a reduced $\chi^{2}=1$. All of our
posteriors are Gaussian distributed and there are no parameter degeneracies.

###  3.2 FIREFLy

We run the jwst pipeline through Stages 1 and 2 using the uncal.fits files. We
utilize group-level 1/f subtraction and apply a scaled superbias to account
for the vertical offset seen in NRS2 Transit 1. (See Section A.1.) We correct
for cosmic rays and bad and hot pixels in the Stage 2 output rateints.fits
files and apply a second 1/f correction at the integration level by masking
the spectral trace and then calculating the median of the background pixels in
each column. This value is then subtracted from the cleaned 2D image.

We next cross-correlate each 2D image with the median aligned image to
determine the x- and y-shifts of the spectral trace, which are used to align
all 2D images. A Gaussian profile is then cross-correlated to each column in
the y-direction and a fourth-order polynomial is fit in the x-direction to
determine the spectral trace, which is used to extract the spectra.

The white light curves for Transits 1 and 2 are fit from the extracted spectra
by summing the spectra in the wavelength direction over a detector. We fit
$a/R_{\star}$, limb darkening parameters, and the impact parameter b using the
weighted mean from both transits and both detectors. We then fix
$a/R_{\star}$, b, and the period, and fit for $R_{P}/R_{\star}$, $T_{0}$, and
limb darkening in the white light curve. A low-order polynomial in time
(third-order in NRS1 and up to fourth-order for NRS2) was used to model the
baseline, with additional detrending parameters of the x- and y-shifts and
superbias scale factor. We then fix the system parameters (presented in Table
2) and limb-darkening coefficients in each wavelength column to fit the
spectroscopic light curves.

###  3.3 Tiberius

With Tiberius we started by running STScI’s jwst stage 0 pipeline on the
uncal.fits files from the group_scale step through gain_scale step. We set
\--odd_even_columns $=$ True at the ref_pix step and ran our own 1/f
correction step at the group level prior to running ramp_fit, which removes
the median background flux for every column of every group’s spectral image.
We define the background as a 14-pixel-wide region that avoided 18 pixels
centered on the curved trace, and mask bad pixels using our own custom bad
pixel map. We subsequently ran assign_wcs and extract_2d to obtain the
wavelength solution and proceeded to run Tiberius’s spectral extraction on the
gainscalestep.fits files.

First we oversampled each pixel by a factor of 10 using a linear
interpolation. This allows us to measure the stellar flux at the sub-pixel
level, which reduces noise in the light curves (The JWST Transiting Exoplanet
Community Early Release Science Team et al., 2022). We used a fourth order
polynomial to trace the NRS1 detector stellar spectrum and a sixth order
polynomial for NRS2. We performed standard aperture photometry at every pixel
column, with a 4-pixel-wide aperture. We performed an additional background
subtraction step at this stage by calculating the background in 14 pixels on
either side of the trace, excluding 7 pixels on each side. For NRS1 we fit
these background pixels with a linear polynomial while for NRS2 we used a
median since our defined background regions were mostly above the stellar
trace.

We remove cosmic rays and residual bad pixels manually and then correct for
small shifts in the stellar spectra along the dispersion direction by cross-
correlating all spectra in the time-series with the first, resampling each
spectrum onto a common pixel grid. Finally, we created a white light curve
between 2.75–3.72 $\mu$m for NRS1 and 3.83–5.15 $\mu$m for NRS2. Our
spectroscopic light curves were created at 1 pixel resolution over the same
wavelength range.

We fit the four white light curves (2 transits $\times$ 2 detectors) with
batman (Kreidberg et al., 2015), leaving $a/R_{*}$, $R_{P}/R_{*}$, the orbital
inclination ($i$), and the time of mid-transit ($T_{0}$) as free parameters,
and fixing the period to the value from Trifonov et al. (2021). For our white
and spectroscopic light curves, we assumed quadratic limb darkening with
coefficients fixed to values from 3D stellar atmosphere models (Magic et al.,
2015) using ExoTiC-LD (Grant & Wakeford, 2022). We adopted
$T_{\mathrm{eff}}=3340$ K, [Fe/H] $=0.070$ and $\log g_{*}=4.9155$ (Trifonov
et al., 2021). For our systematics model we used a combination of polynomials:
quadratic-in-time, linear-in-x-position, and linear-in-y-position, resulting
in 9 free parameters: 4 transit model parameters and 5 systematics model
parameters.

To determine the best fitting values and uncertainties, we used emcee
(Foreman-Mackey et al., 2013) with 90 walkers for two runs of 20,000 steps.
After the first run we inflated our photometric uncertainties to give a
reduced $\chi^{2}=1$ for our best-fitting model before the second run. Table 3
summarizes the results of our white light curve fits. For our spectroscopic
light curve fits, we fixed $a/R_{*}$, $i$ and $T_{0}$ to the weighted mean
values from our 4 white light curve fits and only fitted for $R_{P}/R_{*}$ and
the 5 parameters defining our systematics model. Here we used a Levenberg-
Marquadt sampler for computational speed as we had to fit 6876 spectroscopic
light curves.

##  4 Interpretation of GJ 486b’s Transmission Spectrum

The three data reductions produce consistent spectra with a slight slope on
the blue end ($\leq$ 3.7 $\mu$m) but are otherwise featureless. Here, we first
quantify the significance of this slope in GJ 486b’s spectrum. We then proceed
to offer physical explanations of the spectrum through forward modeling and
retrieval analyses.

###  4.1 A Non-Flat Spectrum

We performed a flat line hypothesis rejection test to determine the
statistical significance of the slope in the transmission spectrum. We fitted
the spectrum from each pipeline using two models: a flat featureless model
that uses one free parameter for the transit depth, and a Gaussian spectral
feature model with four free parameters: the flat transit depth and the
central wavelength, amplitude, and width of a Gaussian feature added to the
baseline featureless spectrum. We fitted both models to each dataset using the
dynesty nested sampling code for Bayesian inference (Speagle, 2020) and then
used the Bayesian evidence to calculate the Bayes factor of each model (e.g.,
Trotta, 2008, 2017). We then converted the Bayes factors to more classical
“sigma” detection significances using the relationship detailed by Benneke &
Seager (2013).

Figure 2 demonstrates that each spectrum separately favors the Gaussian model
and rejects a featureless spectrum. The strength of the signal detection is
$3.20\sigma$ for Eureka!, $2.24\sigma$ for FIREFLy, and $3.29\sigma$ for
Tiberius. The FIREFLy detection significance is lower due to slightly larger
uncertainties associated with that reduction, which stem from FIREFLy’s choice
of spectroscopic binning to produce similar transit depth errors across the
full wavelength range and wavelength-dependent baseline functions.
Nevertheless, the same shape is seen in the spectra from the three pipelines.
Thus, the flat line hypothesis is rejected by all three analyses with varying
confidence. Each individual reduction hypothesis rejection test is available
in the online journal.

Figure 2: Relative transmission spectra of the three data reductions (Eureka!:
blue circles, FIREFLy: orange squares, Tiberius: green triangles). The median
fit to the Eureka! dataset using an agnostic Gaussian model is shown in purple
bounded by $1\sigma$ and $3\sigma$ Bayesian credibility envelopes. The legend
displays the statistical significance with which each reduction rules out a
flat line in favor of the Gaussian model. Analyses of all three reductions
reveal an uptick at the blue end of the wavelength range. Instrument
throughput deteriorates in the grey shaded region and the measured transit
depths become unreliable; thus we exclude points within this region from our
hypothesis rejection tests.

###  4.2 Forward Modeling Tentatively Supports an Atmosphere with Water Vapor

We ran a suite of forward models using the stellar and planet parameters from
Caballero et al. (2022) to compare to each transmission spectrum. We also
generated forward models using an updated stellar log(g) = $4.91\pm 0.02$,
obtained from our updated $a/R_{s}$ constraints (See Appendix A) (Seager &
Mallén-Ornelas, 2003; Sandford & Kipping, 2017), finding consistent results.

We focus on higher mean molecular weight scenarios to explain the transmission
spectrum. For completeness, however, we simulate a 1000$\times$ solar
metallicity atmosphere with a parameterized pressure-temperature profile in
thermochemical equilibrium with CHIMERA (Line & Yung, 2013; Line et al., 2014)
as in our previous work (Lustig-Yaeger et al., 2023). We include the species
H2O, CH4, CO, CO2, NH3, HCN, H2S, H2, and He. The CHIMERA thermochemical
equilibrium abundances result in a model spectrum that is primarily shaped by
methane, carbon dioxide, and water. After generating the temperature-pressure
profile and atmospheric abundances with CHIMERA, we use the radiative transfer
suite of PICASO (Batalha et al., 2019), with opacities resampled to $R=10,000$
from Batalha et al. (2020), to generate model spectra.

In each case, we bin the resulting model transmission spectrum to the
resolution of the data before performing a reduced-$\chi^{2}$ comparison. The
full datasets of all three reductions to which we fit our forward models and
retrievals can be found with the Supplemental Materials. As with the Gaussian
hypothesis tests, we exclude the data points in the grey shaded region of
Figure 2 from our model-fitting due to steeply-falling instrument throughput
at these wavelengths ($<$ 2.87).

As shown in Figure 3, the slight slope and flatness of the spectra from each
reduction allow us to confidently disregard low mean molecular weight
atmospheres dominated by hydrogen/helium – up to metallicities of 1000$\times$
solar – to greater than 3$\sigma$. This improves upon the previous high
resolution data obtained by Ridden-Harper et al. (2022) that could only
strongly rule out atmospheres up to a few times solar. Our 1000$\times$ solar
metallicity atmosphere has an average mean molecular weight of 13.86 g/mol
compared to the high resolution’s 5 g/mol limit, though our constraint is less
stringent for non-chemically consistent atmospheres (see Section 4.3).

Figure 3: Our final Eureka! spectra of GJ 486b binned to R$\sim$200 (black
points) compared to a set of PICASO forward models (colored lines:
1000$\times$ solar, pink; H2O, blue; CO2, orange; CH4, purple; Earth-
composition, green). A 1 bar, pure water atmosphere on GJ 486b fits the data
with the lowest reduced-$\chi^{2}$ (1.01), and a flat-line model (dashed grey
line) is nearly as well fit by the data (reduced-$\chi^{2}$ = 1.11), though is
weakly rejected by Gaussian vs flat line tests. Alternatively, stellar
contamination with water in the atmosphere of the star, rather than the
planet, can explain the observed transit depths (see Fig. 4).

We also compare the data from each reduction to a set of end-member forward
models from PICASO with single-gas 1 bar, isothermal atmospheres. For ease of
interpretation, we focus here on the results from the Eureka! reduction, as we
determined that it was the most representative dataset, with the smallest
weighted average deviation from the median of all three reductions. However,
the trend in best-fit agrees among all three reductions (for a complete
description of each reduction’s fit, see Table 4 in Appendix B). The slight
slope on the blue end of NRS1 results in best-fitting (reduced-$\chi^{2}$ =
1.01) forward models that contain pure water vapor, as this molecule has a
strong absorption feature from 2.2 to 3.7 $\mu$m, consistent with the slope we
observe in NRS1.

Our data across all reductions also moderately to weakly rule out carbon-rich
atmospheres of either CH4 or CO2 to 6.5$\sigma$ and 2.3$\sigma$, respectively.
A flat-line model, representative of an airless body or a high-altitude (0.1
$\mu$bar) cloud deck, fit the data with reduced-$\chi^{2}$ = 1.11, which is
statistically equivalent to the clear water atmosphere model within the
forward modeling framework. However, between its equilibrium temperature and
size, GJ 486b is not expected to support clouds to such low pressures, as
there are few condensible species in this temperature range. Photochemical
hazes could dampen the presence of any spectral features with a haze layer at
this altitude and create a flat line spectrum (Gao et al., 2020; Pidhorodetska
et al., 2021; Caballero et al., 2022); however, given the Bayesian evidence of
the Gaussian absorption tests discussed above, the water atmosphere is the
preferred explanation from the PICASO analysis for all reductions. We note
that the FIREFLy reduction only weakly rejects the flat line hypothesis and,
therefore, an airless planet or very hazy planet is still a possibility. In
Figure 3, we show the results of our PICASO forward modeling compared to the
Eureka! data. The full set of results for each reduction is available in the
online journal.

###  4.3 Retrievals Suggest a Water-rich Atmosphere or Unocculted Starspot
Contamination

In addition to our forward model comparisons, we performed an atmospheric
retrieval analysis to assess the robustness of our tentative evidence for a
water-rich atmosphere and consider alternative astrophysical explanations. We
apply two independent retrieval codes — POSEIDON (MacDonald & Madhusudhan,
2017; MacDonald, 2023) and rfast (Robinson & Salvador, 2023) — to all three
data reductions to ensure reliable inferences.

####  4.3.1 Water-rich Atmosphere Scenario

Our POSEIDON atmospheric retrieval considers six potential gases that can
range in abundance from being trace volatiles to the dominant background gas:
N2, H2, H2O, CH4, CO2, and CO. The opacity contributions from these gases
include line opacity (Polyansky et al., 2018; Yurchenko et al., 2017; Tashkun
& Perevalov, 2011; Li et al., 2015) and collision-induced absorption (CIA)
from H2-H2, H2-N2, H2-CH4, H2-CO2, CO2-CO2, CO2-CH4, and N2-N2 (Karman et al.,
2019). Since the mixing ratios must sum to unity, we have five free parameters
describing their mixing ratios that each follow centered log-ratio (CLR)
priors, ranging from $10^{-12}$ to 1, as described by Lustig-
YaegerFu2023Lustig-Yaeger & Fu et al. (2023). The other free parameters are
the isothermal temperature ($\mathcal{U}$ [200 K, 900 K]), the atmosphere
radius at the 1 bar reference pressure ($\mathcal{U}$ [0.9 $R_{\rm{p,\,obs}}$,
1.1 $R_{\rm{p,\,obs}}$]), and the log-pressure of an opaque surface
($\mathcal{U}$ [-7, 2], in bar). We calculate transmission spectra via opacity
sampling at a resolving power of $R=$ 20,000 from 0.5–5.4 $\micron$, with the
lower wavelength limit set far below our shortest wavelength (2.8 $\micron$)
to later demonstrate how retrieval solutions diverge at optical wavelengths.
These 8-parameter POSEIDON retrievals used the PyMultiNest (Feroz et al.,
2009; Buchner et al., 2014) package to explore the parameter space with 2,000
live points.

Figure 4: POSEIDON retrieval results for GJ 486b’s transmission spectrum.
Left: retrieved transmission spectra for two models compared to the JWST
NIRSpec G395H data from the Eureka! reduction (black points with error bars).
Two scenarios can equivalently explain GJ 486b’s transmission spectrum
($\chi^{2}_{\nu}=1.0$): unocculted starspots with no planetary atmosphere
(orange contours) or a water-rich atmosphere with no starspots (blue
contours). The median retrieved spectrum (solid lines) and 1$\sigma$ and
2$\sigma$ confidence intervals (dark and light contours) for each scenario are
overlaid. Top right: posterior histograms for the unocculted starspot model,
defined by the fractional coverage area of cold stellar heterogeneities/spots
(fhet), the temperature of the heterogeneities/spots (Thet), and the stellar
photospheric temperature (Tphot). Bottom right: posterior histogram for the
water-rich atmosphere scenario, highlighting hydrogen and water’s retrieved
mixing ratios alongside the atmospheric surface pressure. Water is necessary
to explain GJ 486b’s spectrum, but the retrievals cannot differentiate between
a water-rich planetary atmosphere or water contained in cool starspots that
contaminate the transmission spectrum. The complete figure set (3 images, one
for each reduction) is available in the online journal.

Figure 4 shows our POSEIDON retrieval results for this atmospheric model
scenario (blue retrieved spectrum and histograms) for the Eureka! data
reduction – see the online figure set for the other two reductions. For
Eureka! and FIREFLy, the preferred explanation for the observed rise in the
blue wavelengths of the transmission spectrum is H2O opacity from the wing of
the band centered on 2.8 m. Bayesian model comparisons favor the presence of
H2O with Bayes factors of 133 and 8 (3.6$\sigma$ and 2.6$\sigma$) for Eureka!
and FIREFLy, respectively. The retrieved H2O abundance posterior indicates
that water is the most likely background gas (e.g., Eureka! requires a H2O
mixing ratio $>$ 10% to 2$\sigma$ confidence), with an upper limit ruling out
a H2-dominated atmosphere. The Eureka! and Tiberius reductions also yield
upper limits on the CH4 and CO2 abundances (see the Appendix, Figure 6). The
Tiberius reduction, however, does not uniquely infer a water-rich atmosphere.
Though a water-rich atmosphere remains the preferred solution for Tiberius, a
secondary mode permits a clear, H2-dominated atmosphere with no other gases
contributing to the spectrum. This secondary mode reflects a solution where
the wavelength dependence of H2-H2 CIA is used to fit the spectrum. This
solution is unphysical since an H2-dominated atmosphere will always contain
other trace molecules with more prominent absorption features at these
wavelengths. Upon further investigation, we found that the unphysical solution
is driven by the upwards rise at the longest wavelengths that are only present
in the Tiberius reduction (see Figure 2). We, therefore, conclude that a
consistent explanation for GJ 486b’s transmission spectrum, assuming the
observed non-flatness is caused by atmospheric absorption, can be readily
explained ($\chi^{2}_{\nu}\approx 1.0$) by a water-rich atmosphere — in
agreement with the forward models in Section 4.2.

We also conducted single-composition atmospheric retrievals with rfast for all
three reductions. These retrievals consider atmospheres with a single
absorbing gas alongside a spectrally inactive background gas with an agnostic
mean molecular weight. Our rfast retrieval model has 6 free parameters: the
log-gas mixing ratio, $\log_{10}f_{\mathrm{gas}}$ ($\mathcal{U}$ [-12, 0]),
the log-surface pressure, $\log_{10}P_{0}$ ($\mathcal{U}$ [-1, 6], in Pa), the
surface temperature, $T_{0}$ ($\mathcal{U}$ [300, 1100] K), the mean molecular
weight of the background gas, $m_{b}$ ($\mathcal{U}$ [2, 50] amu), the planet
radius, $R_{p}$ ($\mathcal{U}$ [1.1, 1.4] $R_{\oplus}$), and the planet mass,
$M_{p}$ ($\mathcal{N}$ [2.28, 0.12] $M_{\oplus}$). For the single gases, we
consider, in separate retrievals, H2O, CO2, CO, and CH4. The rfast retrievals
use emcee (Foreman-Mackey et al., 2013) with 100 walkers for 15,000 steps,
where the first 5,000 are discarded for burn-in.

We show our rfast 1D posteriors in the Appendix (Figure 7). Our rfast
retrievals also identify a H2O-rich atmosphere as a consistent explanation for
the Eureka! and FIREFLy reductions (though the lower limits on H2O are weaker
compared with POSEIDON due to the combination of a free mean molecular weight,
planet mass, and log-uniform vs. CLR priors). rfast also finds that the
Tiberius reduction permits lower mean-molecular weight atmospheres for similar
reasons to POSEIDON.

####  4.3.2 Unocculted Starspot Scenario

We now consider the potential for GJ 486b’s host star alone to explain our
observed transmission spectrum. Stellar heterogeneities (starspots and/or
faculae) that are not occulted during transit can induce wavelength-dependent
features in transmission spectra if the stellar intensity illuminating the
planetary atmosphere differs from the overall average stellar intensity — also
known as the transit light source effect (TLS) (e.g., Rackham et al., 2018).
This confounding stellar influence is a crucial consideration for transmission
spectra of planets orbiting cool M dwarfs, such as GJ 486, since H2O existing
in cold starspots could mimic atmospheric signatures.

We implement stellar contamination retrievals with POSEIDON following a
similar approach to Rathcke et al. (2021), based on the parameterization from
Pinhas et al. (2018). The contamination model is defined by four parameters:
the stellar heterogeneity temperature, $T_{\rm{het}}$ ($\mathcal{U}$ [2300 K,
1.2 $T_{*,\rm{eff}}$]), the heterogeneity coverage fraction, $f_{\rm{het}}$
($\mathcal{U}$ [0, 0.5]), the stellar photosphere temperature, $T_{\rm{phot}}$
($\mathcal{N}$ [$T_{*,\rm{eff}}$, $\sigma_{T_{*,\rm{eff}}}$]), and the
planetary radius, $R_{p}$ ($\mathcal{U}$ [0.9 $R_{\rm{p,\,obs}}$, 1.1
$R_{\rm{p,\,obs}}$]). For the priors, we adopt literature values of
$T_{*,\rm{eff}}=3340$ K and $\sigma_{T_{*,\rm{eff}}}=54$ K (Trifonov et al.,
2021). We calculate the stellar contamination factor by interpolating the
Allard et al. (2012) grid of stellar PHOENIX models using the pysynphot
package (STScI Development Team, 2013).

Figure 4 demonstrates that contamination from unocculted starspots, with no
planetary atmosphere, provides an equally plausible ($\chi^{2}_{\nu}\approx
1.0$) alternative explanation to GJ 486b’s transmission spectrum. In this
scenario, the observed slope in the spectrum is still caused by the wing of an
H2O band, but the water resides in the host star. The POSEIDON retrievals for
all three data reductions yield a spot coverage fraction of $\sim 10\%$, but
with relatively weak and inconsistent constraints on the spot temperature.
Compared to a flat spectrum, the unocculted starspot model is preferred with
Bayes factors of 255, 16, and 114 (3.8$\sigma$, 2.9$\sigma$, and 3.5$\sigma$)
for Eureka!, FIREFLy, and Tiberius, respectively. We stress that, while our
present observations cannot distinguish between the water-rich atmosphere
scenario and unocculted starspots, these two scenarios deviate substantially
at shorter wavelengths (see Figure 4). Consequently, even in the case of
aerosol-laden atmospheres (Rackham et al., 2022), future observations at
shorter wavelengths can readily distinguish which scenario is correct.

###  4.4 A Spotty Star Best Explains the Stellar Spectrum

To further investigate the possibility of stellar contamination, we return to
the JWST/NIRSpec G395H data to probe the Stage 3 stellar spectra and examine
whether the star is consistent with a particular stellar model. Upon
completing Stage 2 of the jwst pipeline with the flat fielding and absolute
photometric calibration steps enabled, we noticed that only the region within
8 pixels of the trace is converted to units of MJy. The remaining pixel
regions are in DN/s, so we manually mask them before running Stage 3 of
Eureka!. Due to the lack of unmasked background pixels, we disable Stage 3
background subtraction for this flux-calibrated reduction. This change does
not skew the final calibrated spectrum since we already performed group-level
background subtraction in Stage 1.

To compute the stellar baseline spectrum, we exclude 1040 integrations during
transit (1560 - 2599) and then compute median values along the time axis. We
manually mask a few obvious outliers before estimating the baseline spectrum
uncertainties by computing the standard deviation in flux along the time axis.
Typical uncertainties are 3 – 5 mJy, but can be as large as 55 mJy for some
spectral channels. The typical uncertainty values are consistent with the
uncertainties derived from our standard spectral extraction routine. We do not
use the standard error calculation for our uncertainties. That is, we do not
divide our uncertainties by the square root of the number of integrations
because, as demonstrated below, the standard deviation in flux better
represents the true uncertainty in our flux-calibrated spectrum. We note that
the derived baseline spectrum is remarkably consistent between both transits
(see Figure 5).

We used PHOENIX stellar models produced by Allard et al. (2012) to analyze
whether the observed stellar baseline spectrum is best explained by a spotless
or spotted star. We utilized the Allard et al. (2012) models, as in Section
4.3.2, because they account for the formation of molecular bands including
H2O, CH4, and TiO2 and have higher ($\Delta\lambda$=2 Å) resolution than the
observations. This grid of models also has sufficient temperature and gravity
coverage to model the photospheres of M-dwarf stars and their spots and
faculae ($T_{\rm eff}$ $\geq$ 2000 K, log($g$) = 0 – 6 cm s-2).

We employed single PHOENIX models to represent spotless (or one-component)
stars. We used weighted linear combinations of PHOENIX models to create
inhomogeneous models. Two-component models include one model with $T_{\rm
eff}$ $\geq$ 3000 K to represent the background photosphere and a second,
cooler model with $T_{\rm eff}$ $\leq$ $T_{\rm eff,photosphere}$ \- 100 K to
represent spots. Three-component models include an additional $T_{\rm eff}$
$\geq$ $T_{\rm eff,photosphere}$ \+ 100 K model to represent faculae. In the
two- and three-component models, all spots have the same $T_{\rm eff}$ and
log($g$), as do the faculae. Linear combinations were computed by
interpolating the spot and faculae models onto the photosphere wavelength grid
before summing the fluxes in a weighted fraction where the photosphere was
required to be $\geq$ 50% of the total.

To compare the models to the observed baseline spectra, we converted the
native wavelengths from Å to $\mu$m and the flux densities from ergs s-1 cm-2
cm-1 to fluxes in units of mJy. We then scaled the models by
R${}_{*}^{2}$/dist2 using literature values for GJ 486: R∗=0.33 R⊙ (Trifonov
et al., 2021) and dist = 8.07 pc (Gaia Collaboration et al., 2021). We
smoothed and interpolated the models to be the same resolution as the
observations before calculating a reduced-$\chi^{2}$. In our
reduced-$\chi^{2}$ calculations, we considered 3187 wavelength points for
Transit 1 (3180 for Transit 2) and three fitted parameters ($T_{\rm eff}$,
log($g$), and a scaling factor). The multi-component models included
additional fit parameters for determining the percent coverage for the spots
and faculae. The scaling factor was multiplied by the R${}_{*}^{2}$/dist2 term
to account for uncertainty in either measured quantity and varied from 0.9 to
1.1. To get the final reduced-$\chi^{2}$ value for each model, we computed
reduced-$\chi^{2}$ individually for Transits 1 and 2 and then took the
average.

Figure 5: Best matching one-, two-, and three-component PHOENIX models to the
Baseline GJ 486 spectra from Transits 1 (green) and 2 (black). The bottom two
panels zoom-in on the grey highlighted regions of the top panel spectrum. When
considering a one-component photosphere, a $T_{\rm{eff}}$ = 3300 K,
log($g$)=4.5 cgs model is preferred (purple, $\chi^{2}_{\nu}$ = 72.0). When
allowing for spots in a two-component model, a warmer $T_{\rm{eff}}$ = 3400 K,
log($g$)=5 cgs photosphere with 25% coverage of $T_{\rm{eff}}$ = 3000 K,
log($g$)=5 cgs spots is the preferred model (blue, $\chi{{}^{2}}_{\nu}$ =
53.4). The best overall match to the observations is produced with a three-
component photosphere+spots+faculae model that has a background photosphere
with $T_{\rm{eff}}$ = 3200 K, log($g$)=5 cgs, 20% spot coverage
($T_{\rm{eff}}$ = 3000 K, log($g$)=5 cgs), and 25% faculae coverage
($T_{\rm{eff}}$ = 3400 K, log($g$)=5 cgs) (orange, $\chi^{2}_{\nu}$ = 49.0).

Considering each type of one-, two-, and three-component model individually,
we find that the models with the smallest reduced-$\chi^{2}$ values are fairly
consistent with the existing literature values, though no model is a
particularly good fit with a reduced-$\chi^{2}$ near 1 (for numerical details,
see Appendix C). A 100% $T_{\rm eff}$ = 3300 K, log($g$)=4.5 cgs model with
$\chi_{\nu}^{2}$=72.0 is the preferred one-component photosphere model (scale
factor = 1.05), yielding a lower surface gravity than expected for a field age
mid-M-dwarf like GJ 486. In agreement with our updated log($g$)=4.91$\pm$0.02
cgs, we disfavor the low stellar surface gravity of the best-matched
photosphere-only model when taking into account inhomogeneities on the stellar
surface. A 75% $T_{\rm eff}$ = 3400 K, log($g$)=5 cgs background photosphere
with 25% spot coverage at $T_{\rm eff}$ = 3000 K, log($g$)=5 cgs is the
preferred two-component model ($\chi_{\nu}^{2}$=53.4; scale factor = 1.05).
The model most preferred overall is a three-component model with
$\chi_{\nu}^{2}$=49.0 that has a background photosphere with $T_{\rm eff}$ =
3200 K, log($g$)=5 cgs, 20% spot coverage at $T_{\rm eff}$ = 3000 K,
log($g$)=5 cgs, and 25% faculae coverage at $T_{\rm eff}$ = 3400 K, log($g$)=5
cgs (scale factor = 1.1). These three models are shown in Figure 5 compared to
the Baseline GJ 486 spectra from Transits 1 and 2. There is decent general
agreement for each model throughout the full $\sim$2.9-5 $\mu$m range, with
slightly better agreement for the three-component photosphere+spot+faculae
model, indicating that we cannot rule out star spots as a source for the
presumed water detection.

##  5 Discussion and Conclusions

There is remarkable agreement in the stellar heterogeneity parameters obtained
from a) retrieving for unocculted star spots in the planetary transmission
spectrum and b) fitting the baseline stellar spectrum with PHOENIX multi-
component stellar models. Both lines of inquiry find best fits with
overlapping values for faculae/spot coverage and temperature as well as the
photospheric temperature. The stellar spectrum is best fit by a 3200 K
photosphere with 20% cool spots at 3000 K and 25% hot faculae at 3400 K. These
values match well compared to the TLS retrievals with a 3280 K photospheric
temperature lower limit and cool spots up to $\sim$3100K at 7 - 18% coverage
(see Figure 6). This consistency lends strong support to this physical
interpretation of our JWST NIRSpec/G395H data. Moreover, even quiescent M
dwarfs are known to be highly hetereogenous with strong impacts on the
transmission spectrum (Rackham et al., 2018; Zhang et al., 2018; Somers et
al., 2020).

Our forward model water atmosphere demonstrates that water is the best-fit
absorber to explain GJ 486b’s spectrum in the absence of stellar
contamination. Such a pure steam atmosphere could theoretically be generated
by impacts from small, icy bodies (Zahnle et al., 1988) or outgassed depending
on the mantle composition (Tian & Heng, 2023), but would be quickly lost via
the runaway greenhouse effect (Goldblatt et al., 2013), as well as being
disfavored by high resolution observations (Ridden-Harper et al., 2022). We
examine the effect of adding CO2 to our H2O forward model, finding that
scaling the carbon content upwards always results in a worse fit to the data.
In the water-rich POSEIDON retrievals, we find strong water abundance lower
limits across the three reductions, with an agnostic background gas prior. Two
carbon species have stringent upper limits: carbon dioxide and methane. All
reductions have posteriors where the constrained carbon species abundances can
supersede that of water, but the best fits prefer atmospheres where water
vapor dominates over carbon species. Such atmospheres would be challenging to
maintain at GJ 486b’s 700 K equilibrium temperature, given our current
understanding of the runaway greenhouse effect (Goldblatt et al., 2013) and
expected limits on the interior sequestration and outgassing rates of carbon
species relative to water (Sossi et al., 2023; Tian & Heng, 2023). However,
given the large range of retrieved abundances compatible with GJ 486’s
spectrum, they remain consistent with atmospheric theory. Furthermore, our
retrievals cannot constrain the abundance of carbon monoxide (CO), providing
an additional potential reservoir for carbon in the atmosphere. A warm, water-
rich atmosphere with little atmospheric carbon would represent a terrestrial
exoplanet wholly unlike any solar system analogue and challenge our
understanding of atmospheric formation (Wordsworth & Kreidberg, 2022; McIntyre
et al., 2023).

GJ 486b joins the ranks of other terrestrial M-dwarf planets with tantalizing
atmospheric inferences. Such planets include the first planet of our JWST-
GO-1981 program, LHS 475b, exisiting observations of which cannot distinguish
a carbon dioxide atmosphere from an airless body (Lustig-Yaeger et al., 2023).
L 98-59c is another planet where recent HST observations have tentatively
suggested either a hydrogen-rich planetary atmosphere or stellar contamination
(Barclay et al., 2023) — though a different analysis favored a flat,
featureless transmission spectrum (Zhou et al., 2023). Both GJ 486b at 1.3 R⊕
and L 98-59c at 1.35 R⊕ track the upper edge of planets below the expected
hydrogen-dominated atmospheric cut-off (Rogers, 2015; Rogers et al., 2021).
Their difference in insolation, with GJ 486b at Teq = 700 K and L 98-59c at
Teq = 550 K, combined with their retrieved upper limit atmospheric hydrogen
fractions, offer suggestive hints at a cosmic shoreline that is confounded by
potential stellar contamination. More data are clearly necessary to
confidently mark the boundaries of any cosmic shoreline.

Secondary eclipse observations of GJ 486b with JWST’s Mid-Infrared Instrument
(MIRI) Low Resolution Spectroscopy (LRS) mode are already scheduled (GO 1743,
PI: Mansfield). These observations will measure the dayside emission spectrum
of the planet, allowing an expected $5\sigma$ constraint on surface pressures
$\geq 1$ bar, as well as providing evidence for the atmospheric composition
with a sufficiently thick atmosphere (Mansfield et al., 2019, 2021). Thus,
these MIRI/LRS observations can lend an additional line of evidence for or
against both a significant atmosphere as well as the presence of water.
However, our water-rich atmospheric retrieval scenario demonstrates that much
lower surface pressures (down to millibar levels) are consistent with the data
from NIRSpec/G395H, which is beyond the sensitivity of the planned MIRI/LRS
observations. In this case, the secondary eclipse emission spectrum is
unlikely to provide strong evidence in favor of either of our interpretations
for GJ 486b.

As seen in Figure 4, the unocculted star spot scenario and the water-rich
atmosphere scenario diverge strongly shortwards of 0.8 $\mu$m. In the case
that the upcoming MIRI observations cannot definitely detect an atmosphere,
high precision shorter wavelength observations could provide evidence for or
against an atmosphere on GJ 486b. Ultimately, our JWST NIRSpec/G395H stellar
and transmission spectra, combined with retrievals and stellar models, suggest
either an airless planet with a spotted host star or a significant planetary
atmosphere containing water vapor. Given the agreement between our stellar
modeling and atmospheric retrievals for the spot scenario, this interpretation
may have a slight edge over a water-rich atmosphere. However, a true
determination of the nature of GJ 486b remains on the horizon, with wider
wavelength observations holding the key to this world’s location along the
cosmic shoreline.

## Acknowledgments

. We thank the anonymous referee whose comments improved this manuscript. This
work is based in part on observations made with the NASA/ESA/CSA JWST. The
data were obtained from the Mikulski Archive for Space Telescopes at the Space
Telescope Science Institute, which is operated by the Association of
Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127
for JWST. These observations are associated with program #1981. Support for
program #1981 was provided by NASA through a grant from the Space Telescope
Science Institute, which is operated by the Association of Universities for
Research in Astronomy, Inc., under NASA contract NAS 5-03127. This material is
based in part upon work performed as part of the CHAMPs (Consortium on
Habitability and Atmospheres of M-dwarf Planets) team, supported by the
National Aeronautics and Space Administration (NASA) under Grant No.
80NSSC21K0905 issued through the Interdisciplinary Consortia for Astrobiology
Research (ICAR) program. The material is based upon work supported by NASA
under award number 80GSFC21M0002. We also acknowledge Jordin Sparks for her
lyrical genius.

All the JWST data used in this paper can be found in MAST:
http://dx.doi.org/10.17909/z89v-dg97 (catalog 10.17909/z89v-dg97).

##  Appendix A Data Reduction

Dataset |  $T_{0}$ (BJDTDB) |  $i$ (∘) | $a/Rs_{*}$ | $R_{P}/R_{*}$ | Residual RMS (ppm)
---|---|---|---|---|---
Transit 1, NRS1 | $2459939.071619^{+2.0e-05}_{-2.1e-05}$ | $89.10^{+0.26}_{-0.35}$ | $11.24^{+0.03}_{-0.09}$ | $0.03697\pm 0.00009$ | 143
Transit 1, NRS2 | $2459939.071570^{+2.4e-05}_{-2.4e-05}$ | $89.06^{+0.34}_{-0.38}$ | $11.22^{+0.06}_{-0.13}$ | $0.03784\pm 0.00009$ | 171
Transit 2, NRS1 | $2459943.472959^{+2.0e-05}_{-2.0e-05}$ | $89.02^{+0.35}_{-0.38}$ | $11.23^{+0.07}_{-0.13}$ | $0.03689\pm 0.00009$ | 137
Transit 2, NRS2 | $2459943.472974^{+2.3e-05}_{-2.4e-05}$ | $89.06^{+0.46}_{-0.47}$ | $11.22^{+0.10}_{-0.19}$ | $0.03670\pm 0.00009$ | 158
Weighted Mean | $2459939.071594\pm 1.6e-05$ | $89.06\pm 0.18$ | $11.229\pm 0.043$ | $0.03709\pm 0.00004$ | n/a
| $2459943.472967\pm 1.5e-05$ |  |  |  |
Table 1: Best-fit system parameters and 1$\sigma$ uncertainties from fitting the four white light curves using Eureka!. Dataset |  $T_{0}$ (BJDTDB) |  $i$ (∘) | $a/Rs_{*}$ | $R_{P}/R_{*}$ | Residual RMS (ppm)
---|---|---|---|---|---
Transit 1, NRS1 | 2459939.0716102 $\pm$ 2.1e-05 | 89.11 $\pm$ 0.35 | 11.294 $\pm$ 0.137 | 0.03759 $\pm$ 0.00013 | 132
Transit 1, NRS2 | 2459939.0715592 $\pm$ 2.2e-05 | 89.97 $\pm$ 0.27 | 11.449 $\pm$ 0.023 | 0.03791 $\pm$ 0.00010 | 159
Transit 2, NRS1 | 2459943.4729689 $\pm$ 1.9e-05 | 89.99 $\pm$ 0.22 | 11.446 $\pm$ 0.021 | 0.03784 $\pm$ 0.00013 | 130
Transit 2, NRS2 | 2459943.4730019 $\pm$ 2.3e-05 | 89.30 $\pm$ 0.40 | 11.325 $\pm$ 0.111 | 0.03742 $\pm$ 0.00017 | 158
Weighted Mean | 2459939.0715859 $\pm$1.5e-05 | 89.75 $\pm$ 0.14 | 11.443 $\pm$ 0.015 | 0.03775 $\pm$ 0.000063 | n/a
| 2459943.4729823 $\pm$ 1.5e-05 |  |  |  |
Table 2: The system parameters resulting from the FIREFLy fits to the white light curves.  Dataset |  $T_{0}$ (BJDTDB) |  $i$ (∘) | $a/Rs_{*}$ | $R_{P}/R_{*}$ | Residual RMS (ppm)
---|---|---|---|---|---
Transit 1, NRS1 | $2459939.071586^{+3.5e-05}_{-3.6e-05}$ | $89.99^{+0.65}_{-0.61}$ | $11.34^{+0.04}_{-0.13}$ | $0.03683\pm 0.00015$ | 158
Transit 1, NRS2 | $2459939.071548^{+3.6e-05}_{-3.5e-05}$ | $89.97^{+0.67}_{-0.62}$ | $11.36^{+0.05}_{-0.13}$ | $0.03756\pm 0.00017$ | 188
Transit 2, NRS1 | $2459943.472952^{+3.6e-05}_{-3.5e-05}$ | $90.02^{+0.75}_{-0.72}$ | $11.42^{+0.06}_{-0.17}$ | $0.03684\pm 0.00015$ | 158
Transit 2, NRS2 | $2459943.472955^{+4.3e-05}_{-4.3e-05}$ | $89.83^{+1.35}_{-1.32}$ | $11.23^{+0.19}_{-0.4}$ | $0.03685\pm 0.00019$ | 194
Weighted Mean | $2459939.07158\pm 1.9e-05$ | $89.96\pm 0.37$ | $11.40\pm 0.06$ | $0.03701\pm 8e-05$ | n/a
Table 3: The system parameters resulting from the Tiberius fits to the white
light curves.

###  A.1 Data Reduction Consistency: An Offset between the NRS1 and NRS2
Detectors

As stated in the main text, all initial reductions showed a consistent offset
in measured transit depth for the Transit 1, NRS2 detector relative to the
other white light curve depths. Since this shift is not seen in the NRS1
detector, we can confidently rule out all astrophysical effects (e.g., stellar
variability) as a source of the discrepancy. For the FIREFLy reduction, we
altered our application of the superbias in the bias subtraction step and
light-curve fitting stages, which we found produced more consistent transit
depths for NRS1 and NRS2.

In our FIREFLy reduction, we measured the superbias level by rescaling the
superbias image to match the level in the trace-masked groups of each
integration. We note that a full readout of the detector mitigates bias drifts
using reference pixels, but the subarray readouts used here do not have such
pixels. We find that the superbias level changes by hundreds of ppm throughout
the time series, with typical values of the scaling factor about 1.003. We use
the standard-deviation-normalized time series of the superbias scaling
coefficient as a detrending vector at the light-curve fitting stage, added
linearly to our usual systematics model. We find that the superbias
decorrelation coefficient is statistically preferred in the systematics model,
with some residual structure in the photometry well-explained by this term.
The addition of superbias detrending reduced the transit depth tension between
NRS1 and NRS2, with the white-light curve transit depths agreeing within the
uncertainties.

For the Eureka! reduction, we also investigated time-dependent variations in
the NRS2 detector bias level. We found that applying a scale factor correction
to the superbias frame for each integration in Stage 1 marginally improved the
consistency in measured transit depths (by $\sim 20$ ppm), but also led to
increased scatter. Applying a single scale factor correction for all
integrations yielded a similar improvement, but without the increased scatter.
We continue to investigate different methods of scaling the superbias frame.
In the meantime, we elect to adopt the standard bias correction in our final
Eureka! analysis and apply a manual offset of 78 ppm in transit depth to NRS2,
Transit 1.

To account for NRS2 transit visit discrepancy for the final Tiberius
reduction, we also manually offset the transmission spectrum for NRS2, Transit
1 by 63 ppm, such that the median transit depth was equal to NRS2, Transit 2.

After this superbias-detrending in FIREFLy and manual offsets in Eureka! and
Tiberius, we saw excellent agreement between the Eureka!, FIREFLy, and
Tiberius spectra across both NRS1 and NRS2 in both transits, as shown in
Figure 2. Since the superbias correction alters FIREFLy’s absolute transit
depths, we elect to compare their relative transit depths.

##  Appendix B Interpretation Supplemental Information

CHIMERA | Eureka! | FIREFLy | Tiberius | Average $\sigma$ | Significance
---|---|---|---|---|---
Forward Model | (dof = 110) | (dof = 46) | (dof = 46) | ruled out |
1000$\times$ solar | 1.64 | 1.26 | 2.44 | 3.6 | moderately ruled out
H2O, 1 bar | 1.01 | 0.76 | 1.37 | 0.9 | consistent with data
CO2, 1 bar | 1.39 | 1.17 | 1.63 | 2.3 | weakly/moderately ruled out
CH4, 1 bar | 2.10 | 1.77 | 5.96 | 6.5 | strongly ruled out
Earth-like | 1.33 | 1.04 | 2.35 | 2.8 | moderately ruled out
Flat line | 1.11 | 0.91 | 1.60 | 1.5 | weakly/moderately rejected by Gaussian fitting
Table 4: Each reduction’s reduced-$\chi^{2}$ compared to our end-member
composition PICASO forward models. Since each reduction has a different
degree-of-freedom (dof), we also report the average significance (in $\sigma$,
following Trotta (2017)) by which the model is ruled out. Note that the “flat
line” model can correspond either to an airless planet or a very hazy
atmosphere. Figure 6: Posterior probability distributions from the POSEIDON
retrievals. Top rows (blue): retrieval model where GJ 486b’s spectrum is
caused by a water-rich atmosphere. Bottom rows (orange): retrieval model
instead considering unocculted starspots. The rows in each scenario correspond
to different data reductions (Eureka!, FIREFLy, and Tiberius from top to
bottom).  Figure 7: The 1D posteriors for rfast single-gas retrievals. From
top to bottom water, carbon dioxide, carbon monoxide, and methane. Each
reduction is shown in its own color with Eureka! in blue, FIREFLy in orange
and Tiberius in green.

##  Appendix C Stellar Model Statistics

Model Configuration | $\chi_{\nu}^{2}$ | $\chi^{2}$ | $n$ | $K$
---|---|---|---|---
Photosphere | 72.0 | 228,680.81 | 3,187 | 3
Photosphere+Spot | 53.4 | 169,489.64 | 3,187 | 5
Photosphere+Spot+Faculae | 49.0 | 155,374.94 | 3,187 | 7
Table 5: Summary of values for our goodness-of-fit testing, where $n$ is the
number of wavelength points, and $K$ is the number of free parameters.

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contemporary case of trust in artificial intelligence. Explainable Artificial
Intelligence defines the task of building “trustworthy” AI systems, without a
good understanding of how the concept of trust applies to such systems.
Missing from XAI is a robust theory of trust that is calculable,
philosophically acceptable, and respecting of the minimal conditions under
which a trusting instance can be decided. In section 5.4, we explored how the
Contextual Trust account can accommodate transparency as particular kinds of
contextual factors, and argued that the goal of XAI should be to present
difference-making factors of this kind to relevant users. Contextual trust can
thus be of relevance to computer scientists, XAI researchers, philosophers,
and anyone generally interested in studying the behavior of complex,
collaborative systems.

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