# IGRINS observations of WASP-127 b: H2O, CO, and super-Solar atmospheric
metallicity in the inflated sub-Saturn

Krishna Kanumalla School of Earth and Space Exploration, Arizona State
University, 781 Terrace Mall, Tempe, AZ, 85287, USA  Michael R. Line School of
Earth and Space Exploration, Arizona State University, 781 Terrace Mall,
Tempe, AZ, 85287, USA  Megan Weiner Mansfield NHFP Sagan Fellow  Department of
Astronomy and Steward Observatory, University of Arizona, Tucson, AZ 85719,
USA  Luis Welbanks NHFP Sagan Fellow  School of Earth and Space Exploration,
Arizona State University, 781 Terrace Mall, Tempe, AZ, 85287, USA  Peter C.B.
Smith School of Earth and Space Exploration, Arizona State University, 781
Terrace Mall, Tempe, AZ, 85287, USA  Jacob L. Bean Department of Astronomy &
Astrophysics, University of Chicago, Chicago, IL, USA  Lorenzo Pino INAF,
Astrophysical Observatory of Arcetri, Largo Enrico Fermi 5, I - 50125,
Firenze, Italy  Matteo Brogi Department of Physics, University of Turin, Via
Pietro Giuria 1, I-10125, Turin, Italy  INAF-Osservatorio Astrofisico di
Torino, Via Osservatorio 20, I-10025 Pino Torinese, Italy  Vatsal Panwar
Department of Physics, University of Warwick, Coventry CV4 7AL, UK.  Centre
for Exoplanets and Habitability, University of Warwick, Coventry CV4 7AL, UK.

###### Abstract

High resolution spectroscopy of exoplanet atmospheres provides insights into
their composition and dynamics from the resolved line shape and depth of
thousands of spectral lines. WASP-127 b is an extremely inflated sub-Saturn
(Rp= 1.311 RJup, Mp= 0.16 MJup) with previously reported detections of H2O and
CO2. However, the seeming absence of the primary carbon reservoir expected at
WASP-127 b temperatures (Teq $\sim$ 1400 K) from chemical equilibrium, CO,
posed a mystery. In this manuscript, we present the analysis of high
resolution observations of WASP-127 b with the Immersion GRating INfrared
Spectrometer (IGRINS) on Gemini South. We confirm the presence of H2O (8.67
$\sigma$) and report the detection of CO (4.34 $\sigma$). Additionally, we
conduct a suite of Bayesian retrieval analyses covering a hierarchy of model
complexity and self-consistency. When freely fitting for the molecular gas
volume mixing ratios, we obtain super-solar metal enrichment for H2O abundance
of log10X${}_{\mathrm{H_{2}O}}$ = –1.23${}^{+0.29}_{-0.49}$ and a lower limit
on the CO abundance of log10XCO $\geq$ –2.20 at 2$\sigma$ confidence. We also
report a tentative evidence of photochemistry in WASP-127 b based upon the
indicative depletion of H2S. This is also supported by the data preferring
models with photochemistry over free-chemistry and thermochemistry. The
overall analysis implies a super-solar ($\sim$ 39$\times$ Solar; [M/H] =
$1.59^{+0.30}_{-0.30}$) metallicity for the atmosphere of WASP-127 b and an
upper limit on its atmospheric C/O ratio as $<$ 0.68.

: Exoplanet atmospheres (487) – Exoplanet atmospheric composition (2021) –
Exoplanet atmospheric structure (2310) – High resolution spectroscopy (2096) –
Infrared spectroscopy (2285)

††facilities: Gemini South (IGRINS)††software: pymultinest (Buchner et al.,
2014), astropy (Astropy Collaboration et al., 2022), Numpy (Harris et al.,
2020), Scipy (Virtanen et al., 2020), Matplotlib (Hunter, 2007)

##  1 Introduction

Over the past two decades, space-based low-to-moderate resolution spectroscopy
(R $\sim$ 50 - 3000) has been the leading technique in characterizing
exoplanet atmospheres. The Hubble (HST), Spitzer, and recently, James Webb
(JWST) space telescopes have been successful in detecting several key carbon-
and oxygen-bearing molecules in the atmospheres of exoplanets (e.g.,
Madhusudhan, 2019; JWST Transiting Exoplanet Community Early Release Science
Team et al., 2023; Alderson et al., 2023; Welbanks et al., 2024). Abundance
estimation of these molecules is critical in (i) obtaining the atmospheric
metallicity estimate and (ii) constraining the formation location of
exoplanets in the protoplanetary disk via the C/O ratio (Öberg et al., 2011;
Mordasini et al., 2016).

In the era of JWST, ground-based high spectral resolution (R $>$ 15,000)
observations complement the space based observations by enabling velocity
resolved information. This is critical to unambiguously identify trace
absorbers (e.g., Line et al., 2021; Pelletier et al., 2023), disentangle
atmospheric dynamics (e.g., Ehrenreich et al., 2020; Gandhi et al., 2023;
Nortmann et al., 2024), and probe a wide range of atmospheric
pressures/altitudes (e.g., Miller-Ricci Kempton & Rauscher, 2012; Brogi et
al., 2016). Specifically, high-resolution cross-correlation spectroscopy
(HRCCS) leverages the time-resolved planetary Doppler motion around the host
star to separate the planetary signal from the dominant (stellar and telluric)
contaminants (e.g., Snellen et al, 2010; de Kok et al., 2013; Giacobbe et al.,
2021). Recent works (e.g., Smith et al., 2024; Brogi et al., 2023) have shown
that ground-base HRCCS observations of ultra-hot Jupiters can provide
abundance constraints comparable to or even exceeding those achievable with
JWST with a similar observing time.

We applied these HRCCS methods to analyze a single transit observation of an
extremely inflated (Rp = 1.31 RJup) warm sub-Saturn (M${}_{\mathrm{p}}=$ 0.16
MJup, $\rho\sim$ 0.09 $\mathrm{g/cm^{3}}$), WASP-127 b (Lam et al., 2017). It
orbits a bright ($V$ $\sim$ 10.2, $K$ $\sim$ 8.64) and photometrically quiet
G5 type star in a close orbit of 4.17 days. With its calculated equilibrium
temperature (Teq $\sim$ 1400K) and low gravity ($\log$$g$ $\sim$ 2.14 m/s2),
it has an atmosphere with one of the largest estimated scale heights ($\sim$
2350 km) making it highly amenable for transit spectroscopy.

Previous transmission spectroscopy observations of WASP-127 b have revealed a
slew of absorption features. Using GTC/OSIRIS, Chen et al. (2018) detected
spectral features from Na, Li, K with hints of H2O absorption in the
atmosphere of WASP-127 b. The presence of a gray absorbing cloud deck between
a pressure levels of $\sim$ 0.3 and 0.5 mbar was also measured by Allart et
al. (2020), whose value is consistent with the results of Skaf et al. (2020).
Combining the HST (WFC3, STIS) and Spitzer data, Spake et al. (2021) did not
detect the Li and K features but reported strong absorption from H2O and CO2.
From the single photometric data point at 4.5 $\mu$m, Spake et al. (2021) also
reported that they were not able to disentangle the role of CO2 and CO in the
carbon enrichment of WASP-127 b. As a consequence, the retrieved values of C/O
ratio range from sub- to super-solar. The reported absence of CO presents a
mystery in the atmosphere of WASP-127 b as there is no thermochemical
mechanism that would enrich CO2 content while depleting CO (e.g., Moses et
al., 2011).

Using the high-resolution SPIRou spectrograph ($\sim$ 0.95 - 2.50 $\mu$m; R =
70,000) on 3.6 m CFHT, Boucher et al. (2023) confirmed the presence of H2O and
a tentative signal of OH absorption. However, they did not detect CO resulting
in a sub-solar C/O ratio from their model fits. Recently, Nortmann et al.
(2024) reported the detection of CO in WASP-127 b using the CRIRES+ ($\sim$
1.97 - 2.45 $\mu$m; R $\sim$ 140,000) on 8.2 m VLT. Currently, these two high-
resolution studies present a contrasting picture on the presence of CO in
WASP-127 b. Additionally, they reported a strong $\sim$ 7.7 km/sec equatorial
jet in WASP-127 b’s atmosphere based on two signals from the morning and
evening sides of WASP-127 b’s terminator. Based on their H2O detection, the
results of Boucher et al. (2023) support the signal detection from the blue-
shifted evening terminator. However, Boucher et al. (2023) do not find a red-
shifted signal originating from the morning terminator.

In this manuscript, we present our analysis of the time-resolved IGRINS
transit observations of WASP-127 b confirming the presence of H2O and CO
molecules. In §2, we describe our observations and the data reduction. In §3,
we explain the atmospheric model description followed by the cross-correlation
analysis used to detect molecular signals in WASP-127 b. We detail the
retrieval framework in §4 and we report our constraints on the atmospheric
structure. We discuss the implication of our results in §5 and we provide
conclusions of this work in §6.

##  2 Observations and Data Reduction

Observations of WASP-127 b were taken using the IGRINS spectrograph (R $\sim$
45,000, Park et al., 2014) on the Gemini South 8-m telescope as a part of
GS-2021A-LP-107 program (PI: Megan Mansfield). The planet was observed in
transmission for a continuous sequence of 4.78 hrs covering a full transit,
including 12 min of out-of-transit baseline on each side. The observations
were taken in ABBA nodding pattern which resulted in 51 AB pairs for the
duration of the observation. The integration time for a single AB pair was 120
sec which resulted in a median SNR of 214 and 196 in H- and K-bands
respectively.

Figure 1: An example of the removal of telluric/stellar contamination on one
order. The top panels show the “raw” flux (counts) with phase (/frame) as a
function of wavelength. The second panel is the data matrix after the removal
of one eigen component (zeroing out the first singular value) . The out-of-
transit frames have been cropped and it can also be seen that the broad-band
flux variations due to airmass and throughput variations have been removed.
The horizontal black dashed lines represent the beginning and end of transit.
The thrid panel shows the matrix after zeroing out the first six components,
which largely removes the stellar and telluric features. However, to ensure
effective telluric removal, the fourth panel shows strong telluric wavelengths
masked from the residual data matrix. The planet’s signal is buried inside
this noisy matrix.

The IGRINS Pipeline Package (PLP, Lee & Gullikson, 2016; Mace et al., 2018)
was used to reduce, optimally extract the spectra, and perform initial
wavelength calibrations. As done in Line et al. (2021) $\&$ Brogi et al.
(2023), we performed an additional wavelength adjustment by applying a linear
stretch and shift to each spectrum to match the last spectrum in the sequence
(closest in time to the PLP-based wavelength calibration source)–precise
wavelength alingment with time is critical for the telluric de-trending. We
discarded 16 spectral orders due to heavy telluric contamination (median
atmospheric transmittance $<$0.7, low SNR) as well as trim 100 pixels from
either side of each order to due to the low throughput. As a result of these
steps, the final un-calibrated flux/counts data cube is of shape: 38 (orders)
× 101 (phases [$\phi$]) × 1,848 (pixels/wavelengths [$\lambda$] per order).

The telluric and stellar signals need to be removed from the data as the
planetary signal is orders of magnitude smaller. We used the singular value
decomposition (SVD) technique (de Kok et al., 2013; Birkby et al, 2013) to
remove quasi-stationary telluric and stellar spectral features in each each
spectral order. The numpy.linalg.svd function was used to determine the Eigen
vectors (principal components) and the Eigen values (relative contribution of
each vector) of the $N_{\phi}$ $\times$ $N_{\lambda}$ matrix (applied to the
full sequence including out of transit frames). For all the matrices, we saved
the SVD output as two reconstructed matrices: one matrix constructed using the
first $N_{c}$ lower order principal components (i.e., the scaling matrix) and
another matrix constructed from the higher order ($>$ $N_{c}$) principal
components (i.e., the residual matrix) to generate the scaling and residual
data cubes. The planet signal is contained in this residual data cube buried
within the post-SVD residual noise. The resulting products of SVD on one of
the orders of WASP-127 b’s data is shown in Figure 1. For our analysis, we
used six components for all orders as it resulted in the maximum detection SNR
for H2O and CO molecules (see §3.2). However, as discussed in Brogi et al.
(2023), we found that our abundance constraints are weakly dependent on the
number of components used.

To reduce noise in the cross-correlation analysis from the out-of-transit
frames where we do not expect any planetary signal, we cropped 15 frames
before ingress and 9 frames after egress from the residual data cube post-SVD.
Additionally, we masked the wavelengths of the residual data cube where the
telluric features are dominant (atmospheric transmittance $<$ 0.90) using the
Earth’s telluric template obtained from ESO’s
SkyCalc111https://www.eso.org/observing/etc/skycalc/.This helps mitigate any
additional contamination in the residual data due to imperfect telluric
removal with SVD. The combined effect of these two steps provided a
significant increment to our CO signal-to-noise ratio (SNR) in the CC maps
(Figure 10)

##  3 Molecular Signal Detection & Analysis

An important step in any HRCCS analysis is to identify the presence of
absorbers within the exoplanet atmosphere. This provides a zeroth order
estimate of the nature of the planetary atmosphere. However, because the
individual absorption lines are weak compared to the noise level, to identify
the atmospheric features, a model template must be cross-correlated as a
function of velocity with the residual data (bottom panel in Figure 1). We
first discuss the model template setup and then the cross-correlation
analysis.

###  3.1 Model Template Description

We first generated a 1D-radiative convective thermochemical equilibrium
(1D-RCTE) atmosphere using ScCHIMERA tool (Piskorz et al., 2018; Mansfield et
al., 2021). ScCHIMERA produces a converged atmospheric structure i.e., gas
volume mixing ratios (VMRs) and temperature with pressure (TP profile) given
the following inputs: the incident stellar flux (a Phoenix stellar model,
Husser et al., 2013), internal/effective temperature (taken to be 520K based
upon Thorngren et al., 2019), planetary/stellar system properties (Lam et al.,
2017), and the elemental abundances (scaled from Lodders et al. (2009) with an
atmospheric metallicity [M/H] and a C/O ratio). We assumed a solar composition
atmosphere with no clouds for the initial analysis.

We included the line opacities from H2O (Polyansky et al., 2018), CO (Li et
al., 2015), CO2 (Rothman et al., 2010), CH4 (Hargreaves et al., 2020), C2H2
(Polyansky et al., 2018), HCN (Barber et al., 2014), NH3 (Coles et al., 2019),
OH (Rothman et al., 2010), H2S (Azzam et al., 2016), and FeH (Polyansky et
al., 2018) as well as H2-H2/-He collision-induced continuum absorption (CIA,
Karman et al., 2019). Cross-sections were pre-computed using the HELIOS-K tool
(Grimm et al., 2021). We chose these species as they are the most plausible
species to exist under WASP-127 b conditions (Burrows & Sharp, 1999).

The 1D-RCTE profiles were then used to generate a high-resolution (R=250,000)
transmission spectrum with the CHIMERA transmission forward model (Line et
al., 2013; Kreidberg et al., 2015; Bell et al., 2023)–upgraded to run on
graphics processing units (GPUs). The spectra were then broadened (assuming a
rotation kernel 222Although this does not account for additional deviations
from solid body rotation, we expect that the low equatorial vsini of WASP-127
b ($\sim$ 1.63 km/sec) will not heavily alter the line shape within IGRINS
resolution element ($\sim$ 6.67 km/sec) with a planetary rotation velocity,
$v$sin$i$ = 1.63 km/sec) and then convolved with the IGRINS instrumental
profile (a Gaussian kernel with a FWHM of 5.5 model pixels).

###  3.2 Cross-Correlation Analysis

Before cross-correlating the transmission model template with the data, it is
necessary to ensure that the linear transformation effects from the SVD
operation on the data cube are also applied on the model template. As detailed
in Brogi & Line (2019) and Line et al. (2021), we injected the model template
into the in-transit frames of scaling matrix. This model-injected data cube
can be assumed to contain the “true” planet’s signal with our model
assumptions. We then reapply the SVD on the model-injected data cube to
recover the appropriately modified model template that can then be directly
cross-correlated with the residual data cube.

Figure 2:  The CC trail of WASP-127 b generated by cross-correlating the
residual data with a solar RCTE model with opacity from all the molecular
absorbers (i.e., full model). The atmospheric signal can be seen blue shifted
($\sim$ 6 km/sec) from the planet’s RV trace (indicated by the green dashed
line). The phases during which the planet is in-transit are in between the two
dashed horizontal black lines. For the t-test, the in-trail CC values were
collected from the region in between the purple lines. The bottom panel shows
the normalised/probability histograms of the in-trail and out-of-trail
populations. These two histograms show a significant difference, implying the
independance between these two samples.

For each spectral order, we cross-correlated the residual data matrix with the
recovered model template to produce a Pearson cross-correlation value (CC) at
each observed phase ($\phi$). Using spline interpolation (Brogi et al, 2014),
the forward model was Doppler-shifted based on the planet’s velocity ($V_{p}$)
given by,

$V_{p}(\phi)=V_{sys}+V_{bary}(t)+K_{p}\text{sin}(2\pi\phi)$ (1)

where, $K_{p}$ denotes the semi-amplitude of the planet radial velocity,
$V_{sys}$ is the star-planet systemic velocity, and $V_{bary}$ denotes the
barycentric velocity of the observer. For the WASP-127 system, the literature
reported value of systemic velocity is $V_{sys_{0}}$ = –8.64 $\pm$ 0.89 km/sec
(Gaia Collaboration et al., 2018) and the calculated $K_{p_{\mathrm{0}}}$ (=
K⋆ $\frac{\mathrm{M_{\star}}}{\mathrm{M_{pl}}}$) is 132.93 $\pm$ 25 km/sec.
For a given template model, the cross-correlation function (CC value as a
function of velocity) reaches it maximum value at a particular $V_{p}$ at each
phase. The CCF values are summed for all orders and mean-subtracted to
generate a trail of positive CC values in $V_{p}$ as a function of $\phi$.

Figure 3:  The detections of H2O, CO, and total atmospheric signal in the
atmosphere of WASP-127 b. The CC maps shown here are generated by cross-
correlating the residual data with solar composition RCTE models with opacity
from H2O alone, CO alone, and all absorbers respectively. The tellurics have
been masked and the out-of-transit frames have been cropped in the residual
data. The white dashed lines in these maps denote the literature reported
values of $V_{sys_{0}}$ and $K_{p_{0}}$ for the WASP-127 b system. The red
cross indicates the peak value of the SNR within each map. Similar to the
trail, the peak signal in these maps can be seen blue shifted. The bottom red
curves under each panel shows the row/cross-section of the CC SNRs at the peak
$K_{p}$.  Figure 4: The CC map generated by cross-correlating the CO only
model with the IGRINS orders where CO has prominent features. By doing so, the
CC noise from rest other orders was suppressed and the SNR of CO detection has
been increased to 4.34$\sigma$. Similar to Figure 3, the white dashed lines
indicate the literature $K_{p_{\mathrm{0}}}$ and $V_{{sys}_{\mathrm{0}}}$
values. The bottom panel shows the CC cross-section at the peak Kp value.

We detected a clear trail of WASP-127 b’s atmosphere when the residual data
was cross-correlated with a solar composition 1D-RCTE model template
containing opacities from all the above mentioned absorbers (termed as the
full model). The trail, shown in Figure 2, is significantly blue shifted from
the planet’s RV curve by $\sim$ 6 km/sec. To quantify the detection of
planetary signal from this trail, we performed the Welch’s t-test between the
in-trail and out-of-trail CC values data with the null hypothesis that both of
these data are sampled from the same population and therefore are not
disparate. From the observed blue shifted atmospheric signal, we generated the
in-trail sample using the CC values within $\pm$ 5 km/sec from the observed
planetary trail. In Figure 2, this is shown as the region in between the
purple lines. For the out-of-trail dataset, we selected the CC values that
were 35 km/sec away from the trail. From this, the t-test on the in-trail vs
out-of-trail CC values confirmed the trail’s presence with a 9.24$\sigma$
significance.

Unlike the full model trail, the trails generated using templates of
individual molecules are often hard to spot unless the model parameters match
well to those of the planet’s true signal. Therefore, we produced CC maps over
a grid of $K_{p}$ and $V_{sys}$ values which allow for a clear signal
detection. For this, we again cross-correlated the residual data cube with the
recovered model template and summed the CCF values for all phases and orders
to produce a CC map as seen in Figure 3. The SNR in these maps is calculated
by dividing the peak CC value by the standard deviation of the whole map.

Parameter |  Description |  Prior Range
---|---|---
Free Chemistry Retrieval
log10$X_{\text{gas}}$ |  Logarithmic volume mixing ratios of gases |  $\mathcal{U}(-12,0.3)$
|  (H2O, CO, CO2, CH4, C2H2, HCN, NH3, OH, H2S, FeH) |
$T_{0}$ |  Temperature at the top (10-9 bars) of atmosphere |  $\mathcal{U}(500,3000)$ K
log10$P_{1,2}$ |  Pressure nodes at the top and middle of the inversion layer |  $\mathcal{U}(-9,2)$ bar
log${}_{10}P_{3}$ |  Pressure at the bottom of inversion layer |  $\mathcal{U}(-2,2)$ bar
$\alpha_{1,2}$ |  Slopes of the TP profile |  $\mathcal{U}(0.02,2)$ K-1/2
Grid Retrievals
$f$ |  Heat redistribution factor |  $0.543\rightarrow 1.08$ (11 grid points)
[M/H] |  Logarithmic atmospheric metallicity |  $-0.25\rightarrow 2.5$ (21 grid points)
C/O |  Carbon-to-oxygen ratio |  $0.1\rightarrow 0.95$ (15 grid points)
log${}_{10}P_{cl}$ |  Logarithmic cloud top pressure |  $\mathcal{U}(-9,2)$ bar
$K_{p}$ |  Radial velocity semi-amplitude |  $\mathcal{U}(102,182)$ km/sec
$V_{sys}$ |  Systemic velocity |  $\mathcal{U}(-20,20)$ km/sec
log${}_{10}P_{\text{ref}}$ |  Logarithmic reference pressure at $R_{\text{pl}}$ |  $\mathcal{U}(-9,2)$ bar
log $a$ |  HRCCS specific scaling factor |  $\mathcal{U}(-1,1)$
$\times{Rp}$ |  Scaling factor for the radius of planet |  $\mathcal{U}(0.5,1.5)$
Table 1: The description and priors of the parameters in the retrievals.
$\mathcal{U}(a,b)$ denotes a uniform prior from a to b. In the grid
retrievals, priors on $f$, [M/H], and C/O are uniform from their lowest to the
highest grid point value.

We obtained a strong detections of H2O (SNR = 8.67$\sigma$) and CO (SNR =
4.01$\sigma$) from the CC maps generated using the model templates containing
H2O and CO opacities respectively. These CC maps, along with the one generated
using the full model, are shown in the panels of Figure 3. We noticed a
similar blue shifted $V_{sys}$ offset seen in the CC trails in all the three
maps. We also generated the CC maps using the individual model templates of
other absorbers (i.e., CH4, C2H2, HCN, NH3, OH, H2S, FeH, CO2); however, we
did not detect any strong signal peaks (Figure 11). As a result, the CC map
generated using a model with H2O+CO opacity produced an SNR peak that was as
significant as the full model signal.

To validate our CO detection, we only summed the CCFs from the spectral orders
where the opacity from CO was dominant. These correspond to eight spectral
orders in the H and K bands with the ro-vibrational transitions of CO (Figure
14). By doing so, we boosted the significance of CO detection from a 4.01
$\sigma$ to 4.34 $\sigma$ (Figure 4).

##  4 Atmospheric Retrieval Analysis

In order to obtain quantitative information about the composition, clouds, and
the temperature structure, Bayesian inference (i.e., atmospheric retrievals)
must be performed. We used the two common retrieval methods based on the free-
chemistry (e.g., Madhusudhan & Seager, 2009; Kreidberg et al., 2015) and the
grid-based (e.g., Brogi et al., 2023; Bell et al., 2023) chemically consistent
atmosphere paradigms. The full description and priors for all the parameters
in these retrievals are given in Table 1. We used the log-likelihood framework
from Brogi & Line (2019) and sampled the prior space with the pymultinest tool
(Feroz et al., 2009; Buchner et al., 2014).

###  4.1 Free-chemistry retrieval

In the free-chemistry retrieval, we fitted for the constant-with-altitude VMRs
for H2O, CO, CO2, CH4, C2H2, HCN, NH3, OH, H2S, and FeH. We used the six-
parameter temperature-pressure (TP) profile description of Madhusudhan &
Seager (2009) . We also fitted for an opaque grey cloud-top pressure
(log10$P_{cl}$) as well as the planetary radius (as a scaling factor to the
reported radius, $\times$$R_{p}$) and the reference pressure (log10$P_{ref}$)
for that radius. The latter two account for the uncertainty in where to
vertically position the hydrostatic grid which impacts both the planetary
gravity with altitude and the stretching/shifting to the overall spectrum
(Welbanks & Madhusudhan, 2019). We also included a scale factor (log10$a$) to
account for uncertainties in the stretching of the planetary signal during
SVD. Finally, we also retrieved for both the $K_{p}$ and $V_{sys}$ parameters.

Figure 6 summarizes the constraints from the free retrieval. The full
posterior is summarized with a corner plot in the Appendix (Figure 12). The
free retrieval finds log10$X_{\mathrm{H_{2}O}}$ = –1.23${}^{+0.29}_{-0.49}$
and a lower limit on the CO abundance, log10$X_{\mathrm{CO}}$ $>$ –2.20 at
2$\sigma$. Only upper limits on the remaining 8 gases were obtained,
consistent with their non-detections. Additionally, we obtained a bounded
constraint on cloud-top-pressure, log10$P_{cl}$ = –4.21${}^{+0.48}_{-0.36}$.
The temperature profile is largely unconstrained below the cloud deck but is
generally monotonically decreasing with altitude. The obtained $K_{p}$ and
$V_{sys}$ constraints i.e., 132.05${}^{+3.04}_{-2.95}$ $\&$
–14.10${}^{+0.16}_{-0.18}$ km/sec respectively, are consistent with the peaks
from the CC maps. Based on the obtained abundance constraints, we calculated
the estimates on [M/H] and C/O (eqs (1) and (2), Brogi et al., 2023) as $>$
0.07 (2$\sigma$) and $>0.60$ (2$\sigma$) respectively.

###  4.2 Grid-based retrievals

In the grid-based retrievals, we directly retrieved the [M/H] and a C/O, as
well as the heat redistribution factor $f$ based upon a pre-computed grid of
1D-raditive-convective thermochemical/photochemical equilibrium (1D-RCTE/RCPE)
model atmospheres (e.g., Brogi et al., 2023; Bell et al., 2023). To assess the
role of disequilibrium chemistry in influencing the inferred abundances, we
performed grid retrievals with both the thermochemical and the photochemical
grids to the data.

To fit the IGRINS observations, we generated on-the-fly transmission spectrum
given an interpolated model atmosphere, dictated from the pymultinest
parameter draws for [M/H], C/O, and $f$. The model atmosphere (gas VMRs and TP
profile) for each parameter draw is generated via regular grid interpolation
(using scipy.interpolate.RegularGridInterpolator) on the 3,465 grid points. To
remain consistent with the free retrieval, log${}_{10}P_{cl}$,
log${}_{10}P_{ref}$, $\times R_{p}$, log${}_{10}a$, $K_{p}$, and $V_{sys}$
parameters were also included.

Figure 5: VMR profiles of several dominant absorbers in IGRINS coverage. These
profiles are calculated for an atmosphere with 10$\times$ solar metallicity
with f and C/O fixed at 1 and 0.5 respectively. The solid lines indicate the
VMR profiles from a RCPE model and the dashed lines indicate the same from a
RCTE model. Both H2O and H2S are highly impacted by photochemistry, however
H2S is heavily depleted deeper in the atmosphere unlike H2O.

Figure 7 shows the results from the grid-retrieval scenarios. Full corner
plots are shown in the Appendix (Figure 13). From these fiducial grid
retrieval fits (left corner plot of Figure 7), we achieved disparate
constraints on the [M/H] and C/O with a RCTE (blue) and the RCPE (green) model
assumptions. Under the RCTE model assumption, we obtained upper limit on the
[M/H] ($<$ 1.64; 2$\sigma$) and a sub-solar C/O (0.34${}^{+0.08}_{-0.09}$).
However, with a RCPE model assumption, we obtained a bounded constraint on the
[M/H] = 1.59${}^{+0.30}_{-0.30}$ and an upper limit on the C/O as $<$0.68. The
retrieved pressure of the cloud deck also varies between the two retrievals,
corresponding to $\sim$ 3.4 mbar (log10$P_{cl}$ = –2.46${}^{+0.41}_{-0.69}$)
and $\sim$ 0.18 mbar (log10$P_{cl}$ = –3.73${}^{+0.27}_{-0.27}$) from the RCTE
and RCPE retrievals respectively. The $K_{p}$ and $V_{sys}$ velocities remain
consistent between the two grid retrievals (and with the free retrieval)
indicating that neither one of these has converged to an erroneous parameter
space. The heat redistribution parameter, although unconstrained, also remains
consistent between the two retrievals yielding a similar atmospheric
temperature structure for both (Figure 8).

Figure 6: The summary of the free-chemistry retrieval showing a subset of five
posterior distributions (left) and retrieved TP profile (right) using the
Madhusudhan & Seager (2009) parameterisation. In the posterior distributions,
the square marker and the horizontal line represent the median value and
1$\sigma$ error bar on each parameter. For comparison, the VMRs of H2O and CO
from the RCTE (dashed) and RCPE (solid) models with 1$\times$, 25$\times$,
100$\times$ solar metallicity are also shown. These correspond to red, coral,
and blue lines respectively. The retrieved TP profile is shown on the right
with lighter shades of purple representing 1$\sigma$ and $2\sigma$ regions.
The RCPE TP profiles from 1$\times$ (red), 25$\times$ (coral), 100$\times$
(blue) solar metallicity are also plotted for reference.  Figure 7: The
summary of our grid retrievals showing the retrieved posteriors on [M/H], C/O,
and log10$P_{cl}$ from the fiducial (left) retrieval and the test (right)
retrieval. In the right inset, the additional log10$\alpha_{H2S}$ parameter
corresponds to the scaling factor on the H2S abundance (VMR) profile from the
test retrievals. In both the left and right insets, the blue contours and
marginal distributions are from the thermochemical grid retrieval and the
green contours and marginal distributions correspond to the photochemical grid
retrievals. The dotted blue and green lines indicate the median (middle dotted
line) and 1$\sigma$ confidence regions for a given posterior.  Figure 8: The
retrieved TP profiles from the RCPE and RCPE grid retrievals. The TP profile
from the free-chemistry retrieval is also shown for reference. In these
profiles, the lighter shades of the colors represent the 1$\sigma$ confidence
region. The constraint on the pressure level of the gray absorbing cloud from
all the retrievals (square markers) and previous studies (triangle markers)
are shown along with their 1$\sigma$ errors (black bars). Among these, the
pressure level of the cloud-deck from the fiducial RCPE retrieval is
consistent with the two previous studies, whereas the results from RCTE
retrieval are consistent with constraints from Nortmann et al. (2024).

We further investigated the source of the discrepancy between the RCTE and
RCPE retrieval constraints. To do this, we first inspected the model
atmosphere structures under the RCPE/RCTE assumptions for a representative
composition to understand which species are most affected by disequilibrium.
Figure 5 shows an example atmospheric configuration for f = 1, [M/H]= 1 and a
C/O = 0.5. From this, we noted that H2S is the third most dominant molecular
absorber in our models and is strongly influenced by photo-dissociation.

To test the impact of H2S on the inferred composition, we ran two grid
retrievals where we scaled the VMR profile of H2S with a multiplicative factor
(log10$\alpha_{H_{2}S}$) in both grids. We set a uniform prior
($\mathcal{U}(-6,0)$) for this factor. When applied to the RCTE grid (Figure
7, right panel), $\alpha_{H2S}$ converged to a value well below unity (
log10$\alpha_{H_{2}S}$ = –1.38${}^{+0.46}_{-0.58}$). In contrast, when applied
to the RCPE grid, the scale factor constraint runs up against the upper prior
bound (i.e., no scaling, $>$ –1.07 at 2$\sigma$).

This signified that the inclusion of the H2S scale factor had very little
overall impact on the derived constraints from the RCPE grid, but a
significant impact on the ones derived from the RCTE grid. The inclusion of
$\alpha_{H_{2}S}$ decreased the metallicity upper limit from $<$ 1.64
(2$\sigma$) to $<$ 1.47 (2$\sigma$) and altered the sub-solar C/O
(0.35${}^{+0.10}_{-0.10}$) constraint to a super-solar value
(0.59${}^{+0.07}_{-0.11}$). However, the pressure level of the cloud remained
consistent within 1$\sigma$. Most notably, while such modification
substantially transformed our RCTE retrieval results, we noticed that all of
our constraints remained consistent (within 1 $\sigma$) between the RCPE
retrievals (green contours/distributions in Figure 7).

To reinforce our inferences drawn from all of our retrievals, we computed the
Bayesian evidences ($\mathcal{Z}$) from each retrieval paradigm to determine
the model assumption that is most suitable to our data. We obtained the
highest evidence from the RCPE grid retrieval, followed by the free-chemistry
($\Delta ln\mathcal{Z}=4.10$) and RCTE grid retrieval ($\Delta
ln\mathcal{Z}=33.89$) – suggesting that the model that includes photochemistry
is significantly favored compared to the models from the RCTE and
free=chemistry retrievals by 8.54$\sigma$ and 3.33$\sigma$ confidence
respectively.

##  5 Discussion

###  5.1 The search for CO

Comparing our analysis with Boucher et al. (2023), the most contrasting
difference between our studies is the higher SNR of our observations and the
higher number of components we needed to remove to achieve a tentative 3.05
$\sigma$ confidence signal in our CCF map of CO template model (see Figure
10). To boost the SNR, we had to (i) mask the strong tellurics, (ii) crop the
out-of-transit frames (post-SVD) and (iii) select the spectral orders with
high CO opacity in CC map generation. The third strategy solidified the signal
detection from applying (i) and (ii) and best works for CO and other molecules
with banded opacity structure. However, as the SNR of the planet’s signal in
HRCCS methods relies on the number of lines, applying (iii) to generate CC map
with H2O only model produced a signal with a significantly reduced SNR due to
less number of H2O lines captured.

Between the first two strategies, cropping the out-of-transit frames provided
a higher boost to the SNR of the CO signal. Recently, studies have shown that
higher number of out-of-transit frames assists the SVD technique in
efficiently identifying the dominant trends (e.g., Cabot et al., 2024; Dash et
al., 2024) in the data. For the same reason, we also performed the SVD on the
full dataset including out-of-transit frames. However, with our analysis, we
show that cropping the out-of-transit frames post-SVD was highly influential
in augmenting the significance of our signal detections. This also bodes well
considering that frames without any planet signal cannot contribute to the SNR
of the CC map, thus removing them should reduce the noise they pose. However,
this statement best applies in the case of SVD detrending. Because, in Dash et
al. (2024), it was shown that principal component analysis (PCA) based
detrending smears the true exoplanet signal to the out-of-transit portion of
the sequence when eigenvalues are re-fitted on the data via multi-linear
regression.

Our CO detection was made possible due to combined effect of the mentioned
three strategies. Therefore, we echo the prospect of removing the out-of-
transit frames and telluric masking in minimising the contaminants of higher
order data components, where we find the planetary signal. In Figure 10, we
have detailed our steps implemented and their corresponding boost in the CO
detection significance.

The consistent position of our signal peaks in our CC maps and in all the
retrievals serves a robust indicator that we are finding a consistent
atmospheric signal defined by a specific RV curve. While the deviations from
the literature reported $K_{p}$ value can be attributed to errors on the
adapted planetary parameters, the deviations in $V_{sys}$ are best explained
only from winds, ephemerides error (e.g., transit mid-point errors), and other
3-dimensional atmospheric effects (e.g., Brogi et al., 2016). We observed a
strong blue shift of $\sim$ 6 km/sec, consistent with the blue shift reported
by Boucher et al. (2023) based on their H2O detection. However, we do not find
any red shifted signals of H2O and CO as seen in Nortmann et al. (2024). This
is likely due the comparatively lower spectral resolution offered by IGRINS.
Regardless, as discussed in Line et al. (2021), we expect that the Vsys offset
had a negligible effect on altering our retrieved abundance inferences as we
did not observe any correlation with other atmospheric parameters of the
retrieval.

###  5.2 Free-chemistry vs Thermochemistry vs Photochemistry

Altering the H2S abundance profile by scaling it produced minimal difference
of the results of the RCPE grid retrieval while substantially changing the
results of its thermochemical counterpart. This is a direct consequence of the
in-built H2S depletion at low pressure levels above the cloud deck as a result
of photochemistry. Additionally, this also indicated the the inferences drawn
from our thermochemical retrieval were greatly influence by H2S, a molecule we
could not detect both from the CC analysis and the free-chemistry retrieval.

To further support this reasoning, we assessed the level to which H2S would
have been detected, if it was indeed present based on thermochemical
equilibrium. This would also verify that our detrending technique and analysis
were unbiased in detecting H2S. For this, we removed the best fit fiducial
RCPE model from the data and injected the best fit fidicual RCTE model. This
simulated dataset containing the best-fit RCTE signal was then cross-
correlated with the best fit RCTE model and best fit H2S RCTE models. From
Figure 15, we were able to recover a signal of H2S with a confidence of 3.08
$\sigma$.

It is important to acknowledge the biases on estimating [M/H] and C/O ratio
from a free-chemistry model assumption. Brogi et al. (2023) showed that C/O
estimates evaluated from the molecular abundance posteriors of a free-
chemistry retrieval are overestimated. In their case, it was showed that the
abundance estimates of carbon-containing species can exceed the value
permissible based on a chemical equilibrium. From our results, we see a
similar effect where the obtained constraint on the C/O ratio is $>$0.60
(2$\sigma$), which is primarily derived from our posteriors on XH2O $\&$ XCO.
This estimate would be contradictory given the reported CO2 detection by Spake
et al. (2021). We note that, we were only able to place upper limit on the
abundance of CO2 from our free-chemistry retrieval.

RCPE models account for a fundamental process of planetary atmospheres i.e.,
photochemistry, which is not captured by the models with free-chemistry and
thermochemistry. The limitations of free-chemistry retrieval and RCTE grid
retrievals made us question the reliability of [M/H] and C/O estimates
obtained from them. Based on these reasons and from Bayesian evidence, we
quote our final results from our RCPE grid retrieval.

###  5.3 Atmospheric metallicity of WASP-127 b

With a chemically consistent retrieval, the most recent compositional
assessment on WASP-127 b by Nortmann et al. (2024) reports solar values for
both C/O (0.56${}^{+0.05}_{-0.07}$) and atmospheric metallicity ([M/H] =
–0.01${}^{+0.36}_{-0.39}$). Our RCTE grid retrieval constraints on [M/H]
(i.e., 0.21${}^{+0.70}_{-0.33}$) and C/O (i.e., 0.34${}^{+0.08}_{-0.09}$) are
consistent with these values within 1$\sigma$ and 2$\sigma$ confidence regions
respectively.

However, a solar metallicity for WASP-127 b would be in tension with the
results of Chen et al. (2018); Skaf et al. (2020); Spake et al. (2021);
Boucher et al. (2023), which have all reported super-solar metal enrichment.
From a retrieval with a free-chemistry assumption, Chen et al. (2018) reported
super-solar abundance for logX${}_{\mathrm{H_{2}O}}$ as
–2.50${}^{+0.94}_{-4.56}$. This result was also supported by Skaf et al.
(2020) who reported a tighter constraint as logX${}_{\mathrm{H_{2}O}}$ $=$
–2.71${}^{+0.78}_{-1.05}$. Both the free-chemistry and chemically consistent
retrievals of Spake et al. (2021) have favoured super-solar abundance,
yielding an average of 17 $\pm$ 4$\times$ solar metallicity for H2O, Na, and
CO2 under chemical equilibrium. With a free-chemistry assumption, Boucher et
al. (2023) obtained a solar logX${}_{\mathrm{H_{2}O}}$ as
–3.0${}^{+0.5}_{-0.6}$, however, a super-solar value of
logX${}_{\mathrm{CO_{2}}}$ as –3.7${}^{+0.8}_{-0.6}$.

Regardless of the model assumptions in all these studies, there is a general
consensus that the atmosphere of WASP-127 b is metal-enriched, in accordance
with the mass-metallicity relationship of exoplanets as discussed in Spake et
al. (2021). From our RCPE and free-chemistry retrievals, we achieve a similar
conclusion. More importantly, the abundance of CO2 reported by previous
studies can only be reproduced with our RCPE grid retrieval results. In Figure
9, we have shown that our RCPE results are in agreement with several previous
studies that report super-solar metal enrichment in the atmosphere of WASP-127
b.

Figure 9: Our abundance profiles of H2O, CO, and CO2 from the RCPE retrieval
compared against the constraints obtained from previous studies (triangle
markers). For the three molecules, lighter shades of their color represent the
1$\sigma$ confidence region. The error bars on the constraints of the
abundances of H2O, CO, and CO2 are represented in blue, brown and black bars
respectively. The constraints of Spake et al. (2021) were taken from their
estimate on WASP-127 b metallicity from their chemically consistent retrieval
(i.e., 17 $\pm$ 4$\times$ solar). Our RCPE results agree well with the results
reporting super-solar metal enrichment for WASP-127 b.

Our super-solar metallicity constraint ($\sim$ 39$\times$ solar) from the
photochemical retrieval (1D-RCPE) is well below the maximum metallicity for
WASP-127 b ($\sim$ 600$\times$) based on interior structure modelling (e.g.,
Thorngren et al., 2019). While it might seem that such metal enrichment poses
a challenge in explaining the low density of WASP-127 b, the resulting
enhanced opacity could be a possible driver of inflation by delaying the
atmospheric contraction due to heat deposition (Burrows et al., 2007). The
obtained high-altitude cloud deck further supports this reasoning. However,
enhanced opacity is one of the many mechanisms considered to explain the
inflated states of planets such as WASP-127 b e.g., Ohmic heating (Thorngren &
Fortney, 2018; Pu & Valencia, 2017), semi-convective interior (Chabrier &
Baraffe, 2007), He-rain (Stevenson & Salpeter, 1977). Disentangling these
drivers of inflation is beyond the scope of our study and warrants further
investigation.

###  5.4 Validating the depletion of H2S

To confirm or rule out the preliminary signs of photochemistry we reported
here, we strongly encourage additional observations and analyses on WASP-127
b. A possible venue to support/contradict our H2S depletion would be by the
detection/absence of SO2. In the presence of strong UV irraadtion from the
host star, SO2 is an expected primary photochemical product from the
oxidisation of H2S (Zahnle et al., 2016; Tsai et al., 2021; Polman et al.,
2023). In IGRINS bandpass, SO2 possess weak opacity and will be overshadowed
by H2O, CO opacities and their abundances. This statement also applies for CO2
and could possibly be the reason why we do not detect it from our CC maps.
NIRSpec and MIRI on the JWST are an excellent platform to provide an
unambiguous SO2 and CO2 detections (Tsai et al., 2023; JWST Transiting
Exoplanet Community Early Release Science Team et al., 2023; Powell et al.,
2024; Dyrek et al., 2024) and with our analysis, we present an opportunity for
further investigation.

###  5.5 Rossiter-Maclaughlin effect and center-to-limb variations

For our analysis, we did not model the Rossiter-Maclaughlin effect (RME)
(Rossiter, 1924; McLaughlin, 1924; Brogi et al., 2016; Triaud, 2018; Genest et
al., 2022) and the center-to-limb variations (CLV) (Yan et al., 2017; Allart
et al., 2020; Genest et al., 2022; Sicilia et al., 2022). CLVs arise from the
non-uniformity of the stellar flux across the stellar disk from center to the
outer periphery. In exoplanet spectroscopy where the spectra are derived using
the flux ratio between the star and the planet, CLV is responsible for
overestimation of calculated absorption depths of planetary spectral lines
(Borsa & Zannoni, 2018; Sicilia et al., 2022). However, the effect of CLV in
the infra-red (IR) wavelengths is lesser than compared to visible region
(Borsa & Zannoni, 2018). To assess this, we obtained the quadratic limb
darkening (LD) coefficients of WASP-127 from EXOFAST
333https://astroutils.astronomy.osu.edu/exofast/limbdark.shtml and examined
the LD multiple factor as a function of transit in H, K and V-bands. The
variation in LD multiplicative factor value was less pronounced in the H and K
bands, where the value changes from 0.74 (ingress/egress) - 0.99 (mid-
transit). However, this variation in the V-band was significant where the
value changed from 0.32 (ingress/egress) - 0.99 (mid-transit). Therefore, we
expect the CLV effect on our results to be negligible. However, a detailed
exploration of CLV effects on high-resolution IR data warrants further
investigation.

On the other hand, modelling the RME is imperative because the trace of
stellar lines is left on the post-SVD data cube when there are common spectral
lines for the planet and the star. While H2O line are not expected to be
present in a WASP-127 (G5-type), CO lines would bias the abundance constraints
of WASP-127 b if not properly accounted for. However, with a slow rotation
velocity of WASP-127 (vsini $\sim$ 0.5 km/sec), Allart et al. (2020) showed
the RME on the transmission spectra on WASP-127 b was always encompassed
inside the stellar line cores, thereby having minimal effect on the planet’s
spectrum.

##  6 Conclusions

In this work, we analysed the time-resolved IGRINS high-resolution
transmission spectra of WASP-127 b. We used the cross-correlation technique
with a solar RCTE model containing 10 dominant IR absorbers to detect the
atmospheric trail of WASP-127 b. From the CC maps, we detect a strong signal
of H2O (8.67$\sigma$), supporting its previous detections from HST (Spake et
al., 2021) and SPIRou (Boucher et al., 2023). We masked the strong telluric
wavelengths and cropped the out-of-transit phases to reveal a strong CO signal
(4.01$\sigma$) from the CC maps. Among these, cropping the out-of-transit
frame reduced the CC noise and provided a significant boost to the CO signal.
We further validated this detection by selecting the dominant orders with CO
opacity to produce a CC map with an amplified SNR (4.34$\sigma$) of the CO
signal.

We assessed the molecular abundances and temperature structure of WASP-127 b’s
atmosphere using a free-chemistry retrieval, a RCTE grid retrieval, and a RCPE
grid retrieval. From the free-chemistry retrieval, we obtained super-solar
abundance limits (within 2 $\sigma$) for both log10$X_{\mathrm{H_{2}O}}$
(–1.23${}^{+0.29}_{-0.49}$) and log10$X_{\mathrm{CO}}$ ($>$–2.20). For the
rest other atmospheric constituents, we place upper limits on their
abundances. We also retrieved the pressure level for a high-altitude cloud
deck at $\sim$ 0.06 mbar (log10$P_{cl}$ = –4.21${}^{+0.48}_{-0.36}$).

From the chemically consistent RCTE grid retrieval, we obtained an upper limit
on the [M/H] as $<$ 1.64 (2$\sigma$) and a bounded constraint on the sub-solar
C/O as 0.34${}^{+0.08}_{-0.09}$. Conversely, we obtained a bounded contraint
on the [M/H] as 1.59${}^{+0.30}_{-0.30}$ and an upper limit on the C/O as $<$
0.68 from the RCPE grid retrieval. We obtained a cloud pressure level
(log10$P_{cl}$ = –3.73${}^{+0.27}_{-0.27}$) from the RCPE grid retrieval,
which is consistent with the results of Skaf et al. (2020) $\&$ Allart et al.
(2020) within 1$\sigma$. However, a deeper cloud deck was obtained at $\sim$
3.4 mbar (log${}_{10}P_{cl}$ = –2.46${}^{+0.41}_{-0.69}$) from the
thermochemical counterpart. The heat redistribution factor ($f$) remained the
only parameter consistent among the two grid retrievals rendering a similar
atmospheric temperature structure from the two grid retrievals.

We attributed this discrepancy on [M/H] and C/O to the mandatory presence of
H2S from a thermochemical atmospheric assumption. Our test grid retrievals,
where we modified the H2S abundance profile with a scaling factor, indicated
that the RCTE retrieval heavily relied on the presence of H2S. However, we did
not find any trace of this molecule from our CC analysis and the free-
chemistry retrieval. Due to the H2S depletion in the upper atmosphere,
modifying the H2S abundance did not affect our inferences from the RCPE
retrieval. Furthermore, the Bayesian evidence strongly supports that a
photochemical model performed better in matching the data when compared to a
thermochemical model. Based on all these indicators, we draw our main
conclusions from the RCPE/photochemical grid retrieval that the atmosphere of
WASP-127 b is super-solar ($\sim$ 39$\times$) with C/O ratio $<$0.68.

A tighter constraint on the C/O ratio requires additional observations
covering the dominant wavelength regions of CO2 opacity. In the wavelength
coverage of IGRINS, opacity from CO2 is overshadowed by H2O and CO. Given
recent detections of CO2 with JWST (JWST Transiting Exoplanet Community Early
Release Science Team et al., 2023; Bean et al., 2023; Xue et al., 2024), we
anticipate that the observations with NIRSpec and MIRI should not only tighten
our C/O inference, but also validate the evidence of photochemistry presented
here.

This work used the Immersion Grating Infrared Spectrometer (IGRINS) that was
developed under a collaboration between the University of Texas at Austin and
the Korea Astronomy and Space Science Institute (KASI) with the financial
support of the Mt. Cuba Astronomical Foundation, of the US National Science
Foundation under grants AST-1229522 and AST1702267, of the McDonald
Observatory of the University of Texas at Austin, of the Korean GMT Project of
KASI, and Gemini Observatory. K.K., M.R.L., and J.L.B. acknowledge support
from NASA XRP grant 80NSSC19K0293. M.R.L. and J.L.B. acknowledge support for
this work from NSF grant AST-2307177. L.W. and M.W.M. acknowledge support from
NASA Hubble Fellowship program. We would also like to thank the NOIRLabs
support staff helping with the implementation of these observations. Support
for this work was provided by NASA through the NASA Hubble Fellowship grants
HST-HF2-51485.001-A and HST-HF2-51496.001-A awarded by the Space Telescope
Science Institute, which is operated by AURA, Inc., for NASA, under contract
NAS5-26555. Finally, we acknowledge the Research Computing at Arizona State
University for providing HPC and storage resources that have significantly
contributed to the research results reported within this manuscript.

Figure 10: The steps implemented on the data of WASP-127 b to highlight the
weak CO signal. From the leftmost panel, it is not possible to claim a
confident detection of CO. The most significant increase in the significance
of our CO detection comes by cropping the out-of-transit phases post-SVD.
Figure 11: The CC maps showing the of non-detections of CO2, H2S, and OH from
our dataset. These three maps are a subset of eight non-detection maps of the
absorbers included in our models apart from H2O and CO.  Figure 12: The full
posterior from the free-chemistry retrieval showing the constraints on all the
parameters specific to free-chemistry. In all the marginal distributions, the
median and the 1$\sigma$ bound are shown as the vertical dashed lines. The
posteriors on [M/H] and C/O are calculated based on the obtained abundance
constraints (see Brogi et al. (2023) for the description). The obtained
temperature structure based on the six parameters of the TP profiles is shown
in the top right.  Figure 13: The full posteriors of the RCTE (right, blue)
and the RCPE (left, green) grid retrievals. The main conclusions in this
manuscript have been drawn from the RCPE grid retrieval. In all the marginal
distributions, the median and the 1$\sigma$ bound are shown with a vertical
dashed line and their values are given in the top of each column.  Figure 14:
The solar RCTE model of CO (brown) used for our cross-correlation analysis.
The best fit RCPE model is also shown as the green curve. The 38 IGRINS
spectral orders used in our analysis are shown as the blue stripes. Among
these, the eight spectral orders where the CO opacity is dominant as shown in
black. The CC map where we detected maximum SNR of CO signal has been
generated with the CC values from these eight spectral orders.

Figure 15: The CC maps where were removed the best fit RCPE model from the
data and injected the best fit RCTE model at Vsys = 20 km/sec (Black dashed
vertical line in the maps). The cross-correlation was performed with best fit
RCTE full model and H2S absorption only model to assess the level at which H2S
can be detected, if present, dictated by thermochemical equilibrium. While not
as strong as the full model (10.27$\sigma$), we would have been able to detect
H2S at 3.08$\sigma$ under thermochemical equilibrium.

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