# A Comprehensive Analysis of WASP-17b’s Transmission Spectrum from Space-
Based Observations

L. Alderson1, H. R. Wakeford1, R. J. MacDonald2, N. K. Lewis2, E. M. May3, D.
Grant1, D. K. Sing4,5, K. B. Stevenson3, J. Fowler6, J. Goyal7, N. E.
Batalha8, T. Kataria9
1School of Physics, University of Bristol, HH Wills Physics Laboratory,
Tyndall Avenue, Bristol BS8 1TL, UK
2 Department of Astronomy and Carl Sagan Institute, Cornell University 122
Sciences Drive, Ithaca, NY, 14853, USA
3 Johns Hopkins APL, 11100 Johns Hopkins Rd, Laurel, MD 20723, USA
4 Department of Earth & Planetary Sciences, Johns Hopkins University,
Baltimore, MD, USA
5 Department of Physics & Astronomy, Johns Hopkins University, Baltimore, MD,
USA
6 Department of Astronomy and Astrophysics, University of California, Santa
Cruz, CA 95064, USA
7 School of Earth & Planetary Sciences (SEPS), National Institute of Science
Education & Research (NISER), Bhubaneshwar - Odisha 752050, India
8 NASA Ames Research Center, MS 245-3, Mountain View, CA 94035, USA
9 NASA Jet Propulsion Laboratory, 4800 Oak Grove Dr, Pasadena, CA 91109, USA
E-mail<EMAIL_ADDRESS>(LA)

(Accepted 2022 March 3. Received 2022 March 3; in original form 2021 December
24)

###### Abstract

Due to its 1770 $K$ equilibrium temperature, WASP-17b, a 1.99
$R_{\mathrm{Jup}}$, 0.486 $M_{\mathrm{Jup}}$ exoplanet, sits at the critical
juncture between hot and ultra-hot Jupiters. We present its 0.3–5 $\micron$
transmission spectrum, with newly obtained with Hubble Space Telescope (HST)
Wide Field Camera 3 (WFC3) measurements, and, taking advantage of improved
analysis techniques, reanalysed HST Space Telescope Imaging Spectrograph
(STIS) and Spitzer Space Telescope Infrared Array Camera (IRAC) observations.
We achieve a median precision of 132 ppm with a mean of 272 ppm across the
whole spectrum. We additionally make use of Transiting Exoplanet Survey
Satellite (TESS) and ground-based transit observations to refine the orbital
period of WASP-17b. To interpret the observed atmosphere, we make use of free
and equilibrium chemistry retrievals using the `POSEIDON` and `ATMO` retrieval
codes respectively. We detect absorption due to H2O at $>7\sigma$, and find
evidence of absorption due to CO2 at $>3\sigma$. We see no evidence of
previously detected Na i and K i absorption. Across an extensive suite of
retrieval configurations, we find the data favours a bimodal solution with
high or low metallicity modes, as a result of poor constraints in the optical
and demonstrate the importance of using multiple statistics for model
selection. Future James Webb Space Telescope (JWST) GTO observations, combined
with the presented transmission spectrum, will enable precise constraints on
WASP-17b’s atmosphere.

###### keywords:

techniques:spectroscopic – planets and satellites: gaseous planets – planets
and satellites: atmospheres – planets and satellites: individual: WASP-17b

††pubyear: 2021††pagerange: A Comprehensive Analysis of WASP-17b’s
Transmission Spectrum from Space-Based Observations–A

##  1 Introduction

The atmospheres of giant exoplanets encode key information about the formation
and evolution of these worlds beyond the solar system (e.g., Öberg et al.,
2011; Madhusudhan et al., 2014; Mordasini et al., 2016; Espinoza et al., 2017;
Eistrup et al., 2018). In the past decade, the study of atmospheres of close-
in gas giant exoplanets in transmission has been dominated by observations
performed by the Hubble Space Telescope (HST, e.g., Charbonneau et al., 2002;
Gibson et al., 2012; Huitson et al., 2012; Mandell et al., 2013; Nikolov et
al., 2014; Kreidberg, 2015; Sing et al., 2015; Sing et al., 2016; Evans et
al., 2016; Wakeford et al., 2017; Spake et al., 2018; Wakeford et al., 2018;
Alam et al., 2020; Lewis et al., 2020; Sheppard et al., 2021), characterising
absorption by chemical species and scattering by aerosols. Many of these
studies include observations with HST’s Wide Field Camera 3 (WFC3), in
particular making use of the instrument’s near infra-red (NIR) capabilities
via the G141 grism to measure prominent H2O absorption features (e.g., Deming
et al., 2013; Fraine et al., 2014; Kreidberg et al., 2014; Evans et al., 2016;
Colón et al., 2020). As a result, the 1.4 $\micron$ H2O absorption feature has
formed the backbone of many exoplanet comparative studies (e.g., Iyer et al.,
2016; Sing et al., 2016; Stevenson, 2016; Fu et al., 2017; Fisher & Heng,
2018; Tsiaras et al., 2018; Pinhas et al., 2019; Welbanks et al., 2019; Gao et
al., 2020; Yu et al., 2021; Dymont et al., 2021). However, NIR observations
cannot alone precisely characterise the atmosphere of exoplanets, as the
amplitudes and shapes of chemical absorption features are degenerate with the
impacts of aerosol scattering and absorption towards the optical (e.g., Fisher
& Heng, 2018).

Accurately measuring the shape of the transmission spectrum into the optical
towards ultra-violet (UV) wavelengths is therefore a vital part of
understanding the nature of a planet’s atmosphere (e.g., Sing et al., 2011;
McCullough et al., 2014; Pinhas et al., 2019; Bruno et al., 2020; Wakeford et
al., 2020; Lewis et al., 2020). Such observations, which capture information
about absorption from dominant optical sources such as Na i, K i, TiO and VO,
as well as wavelength dependent scattering towards the blue, can be performed
by the Space Telescope Imaging Spectrograph (STIS) aboard HST (e.g., Nikolov
et al., 2015; Alam et al., 2021), although they are also frequently made with
ground-based telescopes, which provide the opportunity for continuous
observations not afforded by HST (e.g., Nikolov et al., 2016; Chen et al.,
2018; Kirk et al., 2018; May et al., 2018; Alderson et al., 2020; Sotzen et
al., 2020; Wilson et al., 2020; Weaver et al., 2021).

It is only with this combination of both optical and infra-red (IR) data that
the complexities hidden within exoplanetary atmospheres can begin to be
explored, as multiwavelength observations allow for multiple pressure depths
and opacity sources to be probed. Such panchromatic studies, often
complimented by photometry performed by the Spitzer Space Telescope Infrared
Array Camera (IRAC) at 3.6 and 4.5 $\micron$ (e.g., Sing et al., 2013;
Wakeford et al., 2018; Alam et al., 2020; Carter et al., 2020; Spake et al.,
2021), have placed constraints on the abundances of key spectroscopically
active species such as H2O, Na and K (Spake et al., 2021), determined the role
of uniform clouds and wavelength dependent scattering (Sing et al., 2013; Alam
et al., 2020), and obtained the metallicities of atmospheres relative to solar
values (Wakeford et al., 2018; Carter et al., 2020). The future of
comprehensive space-based exoplanet atmosphere interpretation lies with the
James Webb Space Telescope (JWST), GTO observations of which seek to obtain
the 0.6–14$\micron$ spectrum of WASP-17b (Anderson et al., 2010).

WASP-17b is a 1.991 $R_{\mathrm{Jup}}$, 0.486 $M_{\mathrm{Jup}}$, hot Jupiter,
with a density of just 0.08 $\rho_{\mathrm{Jup}}$ (Anderson et al., 2011),
orbiting an 11.59 MV, F6 type star in retrograde. With a period of 3.735 d
(Triaud et al., 2010) and a 4.4-hour transit duration, WASP-17b is excellent
target for transmission spectroscopy, aided by its large 1609 km scale height
(assuming a mean molecular weight for a H/He dominated atmosphere of 2.3).
With an equilibrium temperature of 1770 $K$ (Anderson et al., 2011), and a
day- to night-side temperature range spanning 1000-2500 $K$ (Kataria et al.,
2016), WASP-17b is likely to play host to a broad range of spectroscopically
active chemical species such as H2O, CO2 and CO from the optical to the near-
IR (e.g., Madhusudhan et al., 2016). At these temperatures, magnesium
silicates are expected to be the dominant cloud species by both mass and
opacity (e.g., Visscher et al., 2010; Gao et al., 2020). WASP-17b is an
excellent probe of the parameter space between hot and ultra-hot Jupiters,
such that the atmosphere of WASP-17b may be warm enough for TiO and VO to have
a significant opacities (Fortney et al., 2008).

As a result of the range of factors that make WASP-17b an excellent target, it
was among the early vanguard of exoplanets to be probed via spectroscopic
transmission observations. Ground-based high resolution atmospheric studies
have found evidence of excess absorption caused by Na i (Wood et al., 2011;
Zhou & Bayliss, 2012; Khalafinejad et al., 2018), with transmission photometry
detecting wavelength dependencies in $R_{p}$ (planetary radius) consistent
with the potential presence of Na i absorption (Bento et al., 2014). Using the
FORS2 instrument at the VLT, Sedaghati et al. (2016) were unable to confirm
the presence of Na i, however, detected the wings of the K i absorption
feature (but not the line core, see Section 5.2), and ruled out a flat
“cloudy" spectrum. Analysis of WFC3/G141 observations by Mandell et al. (2013)
detected strong H2O absorption at 1.4 µm, and found that atmospheric models
including a haze prescription produced a better fit to the spectrum than those
without. However, it is important to note that the G141 data did not contain
post transit information, and were conducted in stare mode as opposed to scan
mode (see Sections 2.1 and 5.2 for further discussion).

WASP-17b was also analysed as part of the large comparative study by Sing et
al. (2016) with new HST STIS measurements (GO-12473, PI Sing), Spitzer IRAC
data (90092, PI Désert), and a re-analysis of the WFC3/G141 measurements first
analysed by Mandell et al. (2013) (GO-12181, PI Deming). Sing et al. (2016)
concluded that of the 10 planets included in their study, WASP-17b had the
clearest atmosphere, with prominent H2O and Na i absorption features (although
no evidence of K i), and IR transit depths higher than that of the optical,
indicative of a lack of a strong optical slope. Through the strength of the Ca
ii H&K emission lines as measured by Keck/HIRES, Sing et al. (2016) also found
that the host star WASP-17 has a log$R^{\prime}_{\mathrm{HK}}$ value of
-5.531, indicative of a quiet star, a conclusion also found by Khalafinejad et
al. (2018).

WASP-17b has also been analysed extensively through theoretical and retrieval
studies (Barstow et al. 2017; Pinhas et al. 2019; Welbanks et al. 2019 using
the transmission spectrum of Sing et al. 2016, and Fisher & Heng 2018 using
the transmission spectrum of Mandell et al. 2013), with mixed evidence as to
whether its transmission spectrum is best fit by cloudy or cloud-free models.
Barstow et al. (2017) found that the atmosphere is best fit by a Rayleigh
scattering aerosol, while Fisher & Heng (2018) and Pinhas et al. (2019) found
that both cloudy and cloud-free models are statistically comparable in fit.
Reported H2O abundances for WASP-17b have also spanned a wide range, with
studies reporting sub-solar, solar and super-solar values (Barstow et al.,
2017; Fisher & Heng, 2018; Pinhas et al., 2019; Welbanks et al., 2019).

However, the WFC3/G141 observations on which many of these studies critically
relied upon were undertaken before the spatial scanning technique became an
available mode on HST. Performed in stare mode, the WFC3/G141 measurements,
like all stare mode observations, were less efficient, more susceptible to
systematics and achieved lower photometric precisions than can now be obtained
with spatial scan observations. Furthermore, the WFC3/G141 observations
additionally suffered due to a lack of complete transit coverage, failing to
capture any egress or post-transit information. These measurements therefore
do not provide as robust of constraints on the planet’s spectrum as could be
achieved with new observations. To that end, we present analysis of newly
obtained WFC3/G102 and G141 data of WASP-17b taken in spatial scanning mode
(GO-14918, PI Wakeford). We also present a comprehensive and consistent
reanalysis of the STIS and Spitzer observations, taking advantage of the many
advances in analysis techniques which have occurred since the observations
were featured in Sing et al. (2016). These analyses allow us to present an
extensive retrieval analysis of the panchromatic transmission spectrum of
WASP-17b from 0.3–5 $\micron$.

This paper is organised as follows. In Section 2, we present our observations
and our data reduction process. In Section 3, we analyse TESS data and prior
orbital ephemerides of WASP-17b, and present a new, refined orbital period.
Section 4 details the light curve fitting for each dataset. In Section 5, we
present the results of the fitting, and our combined transmission spectrum is
compared to previous works. In Section 6, we interpret the atmosphere of
WASP-17b using results from both free and equilibrium chemistry atmospheric
retrievals. Finally we present our conclusions in Section 7.

##  2 Observations & Data Reduction

###  2.1 HST/WFC3

We observed two transits of WASP-17b with HST WFC3/IR as part of GO-14918 (PI
Wakeford). HST observed one transit with the G102 grating (0.8 – 1.1 µm) on UT
2017 June 16 (program visit 2), however as this visit suffered from guide star
failure which rendered the data unusable, this observation was repeated on UT
2017 September 25 (program visit 32). A second transit was observed with the
G141 grating (1.1 – 1.6 µm) on UT 2017 July 23 (program visit 1). Both
successful visits were conducted in forward spatial scan mode, with exposure
times of 134.35 s over five HST orbits, for a total of 70 exposures. Visits 1
and 32 achieved maximum pixel counts of $\sim 39,000$ and $\sim 32,000$
respectively. Both visits were read out using the SPARS25 sampling sequence
with NSAMP=8.

To reduce our WFC3/IR observations, we used a custom pipeline built for this
analysis, designed to work with the Exoplanet Timeseries Characterisation -
Instrument Systematics Marginalisation light curve fitting package (`ExoTiC-
ISM`, Laginja & Wakeford 2020). Our pipeline takes the dark and flat-field
corrected `ima` FITS products of the `CALWF3`
pipeline111http://www.stsci.edu/hst/wfc3/pipeline/ for each transit and
performs cosmic ray removal, aperture selection, background removal and
stellar spectra extraction.

We begin by flagging and removing any cosmic rays incident on the detector in
the 2D images, replacing $>5\sigma$ outliers with the median value of that
pixel across the time axis of the observations. This step is repeated
iteratively a total of four times to ensure all cosmic rays are found and
removed. We then extract the stellar spectra by summing the flux in each scan
within an optimised aperture, found by minimising the standard deviations of
post-transit white light curve data measured from a variety of aperture widths
(see Wakeford et al. 2016). The determined aperture widths were 37 pixels wide
for visit 1, and 30 pixels wide for visit 32. A background region is then
selected with the same spatial width as the aperture, spanning the full image
width in the dispersion direction, and the median count in this region is
calculated and subtracted from each pixel. After extracting the spectra (shown
in Figure 1), we calibrate them in wavelength using target positioning offsets
and detector calibration profiles (Kuntschner et al., 2009a, b), before
calculating the $x$ sub-pixel shifts of the spectra over time by cross-
correlation, for use in instrument systematic detrending (e.g., Deming et al.,
2013; Fraine et al., 2014; Sing et al., 2015).

We produce white light and spectroscopic light curves for each visit,
discarding the zeroth orbit and first exposure in each orbit due to their
significantly different systematics (e.g., Deming et al., 2013; Wakeford et
al., 2016; Zhou et al., 2017). A band-integrated white light curve for each
visit is produced by summing the flux of the stellar spectra across the full
spectrum, as shown in Figure 2. Spectroscopic light curves are produced by
dividing the stellar spectra into wavelength bins with a minimum width of four
pixels, equal to two resolution elements, and summing the fluxes of each bin.
This reduces the effect of correlated noise between wavelength
bins222https://hst-docs.stsci.edu/wfc3ihb/chapter-7-ir-imaging-with-
wfc3/7-6-ir-optical-performance. In total, 15 and 25 spectroscopic light
curves were produced for the G102 and G141 observations respectively,
resulting in spectral resolutions of $R\sim 90$ and $R\sim 140$ (see Figures
15 & 16).

Figure 1: 1D stellar spectrum and pixel maps for observations of WASP-17b from
WFC3/G102 visit 32 (left, orange) and WFC3/G141 visit 1 (right, red). Top:
Example stellar spectrum of WASP-17. Vertical bands show the wavelength bins
corresponding to each spectroscopic light curve. Upper Middle: Stellar spectra
from each visit following cosmic ray removal and extraction, but before
systematic corrections, with units of total number of photons collected. Lower
Middle: Same as above, but with stellar spectra normalised by the final
spectrum to enhance the contrast of the transit. Bottom: Residuals of each
spectroscopic light curve after systematic correction. Figure 2: Transit white
light curves of WASP-17b for the two HST WFC3 visits, G102 visit 32 (left) and
G141 visit 1 (right). Top: The raw light curves. Data points from the zeroth
orbit and first exposure in each orbit are plotted to demonstrate their
differing systematic properties. Data points joined by lines are included in
the light curve fitting. Middle: Corrected light curves and best fit model
obtained with ExoTiC-ISM. Bottom: Corrected light curve residuals. The dashed
lines indicate one and three times the standard deviation.

###  2.2 HST/STIS

To present a consistent transmission spectrum with wide wavelength coverage,
and to take advantage of advances in transit light curve analysis, we re-
reduced and analysed three archival transit observations of WASP-17b from HST
STIS as part of GO-12473 (PI D.K. Sing). Two transits were observed with the
G430L grating (2892 – 5700 Å) on UT 2012 June 08 (program visit 6) and 2013
March 18 (program visit 5), each with exposure times of 279 s over five HST
orbits and a total of 48 exposures and a maximum pixel count of $\sim$24,000.
A single transit was observed with the G750L grating (5240 – 10270 Å) on UT
2013 March 19 (program visit 19) with an exposure time of 259 s over five HST
orbits for a total of 48 exposures, with a maximum pixel count of
$\sim$27,000. To minimise slit losses the 52$\times$2” slit was used for both
gratings.

We reduced the STIS observations following the procedures outlined in Alam et
al. (2018). The raw 2D images for all STIS observations are corrected using
the
`CALSTIS`333http://www.stsci.edu/hst/stis/software/analyzing/calibration/pipe_soft_
hist/intro.html pipeline and relevant calibration files to account for bias,
dark, and flat field corrections. We used a custom routine modelled on IRAF’s
APALL to extract the stellar spectrum from each exposure using the `flt`
science files. The G750L dataset was defringed using contemporaneous fringe
flats (see Nikolov et al., 2014, for details). To identify and remove cosmic
rays we followed the procedure outlined Section 2.1 with an additional spatial
search in each frame. Due to the long exposure times and short wavelengths of
the STIS observations, a large number of cosmic ray hits are expected to be
measured. To determine the location of these, we took difference images for
each frame, comparing these to the surrounding frames in time (Nikolov et al.,
2014). We then took a median image of five frames and identified cosmic rays
using a window of 20 pixels centred on each pixel, comparing the value of the
central pixel to the median of all pixels in the window. Where the pixel value
was $>5\sigma$ above the median level we flagged this as a cosmic ray and
replaced the pixel value with a median of the column using the adjacent $\pm$
3 pixels.

We tested a range of extraction aperture widths for each observation and found
the lowest scatter with an aperture width of $\pm$ 6.5 pixels around the
central trace on all three STIS observations. We defined the best aperture by
minimising the standard deviation of the measured flux in orbit one of each
observation. The wavelength solutions were computed by using the `x1d` files
from `CALSTIS` to resample all of the extracted spectra (shown in Figure 3)
and cross correlate them to the common rest frame, taken as the final
spectrum. From this we also obtain information on sub-pixel shifts in the
dispersion direction due to the pointing motion of the telescope during the
full transit observation. We use the measured shifts in this cross correlation
in our systematic model to account for changes induced in the spectra over
time.

Band-integrated white light curves were produced for each visit by summing the
flux over the whole wavelength range of the gratings, as shown in Figure 4
(2892 – 5700 Å for G430L grating, 5240 – 10270 Å for the G750L grating). Due
to previously poor constraints on the planet’s period and transit times, each
of the STIS observations did not capture any post transit egress information
on the stellar baseline (see Figure 4 and Section 3 for details on the orbital
period).

As with the WFC3 observations, the first exposure in each orbit is discarded,
as they are subject to different systematic effects to the subsequent
exposures in that orbit. We choose to not discard the zeroth orbit of the two
G430L observations as they do not display significant differences in their
systematics to subsequent orbits. The inclusion of the zeroth orbits were
found to improve the transit model fit to the data, likely due to the
increases in the pre-transit baselines, as the observations are lacking in
post-egress data. However, the zeroth orbit of the G750L observation did show
significant differences in the flux compared to subsequent orbits and was
therefore discarded for this analysis (see Figure 4).

We create spectroscopic light curves using the wavelength bins published by
Sing et al. (2016) (see Figures 17 & 18). These bins are chosen as they sample
the Na i and K i lines covered by the G750L grating, while avoiding strong
stellar lines and ensuring that the flux of the star is evenly sampled.

Figure 3: 1D stellar spectrum and pixel maps for observations of WASP-17b from
STIS/G430L visit 5 (left, purple) visit 6 (centre, pink) and STIS/G750L visit
19 (right, green). For details see Figure 1. Figure 4: Transit white light
curves of WASP-17b for the three HST STIS visits, G430L visit 5 (left), G430L
visit 6 (middle) and G750L visit 19 (right). Top: The raw light curves. Data
points corresponding to the first exposures in each orbit for all three
visits, and from the zeroth orbit for G750L, are plotted to demonstrate their
differing systematic properties. Data points joined by lines are included in
the light curve fitting. Middle: Corrected light curves and best fit model
obtained with a custom ExoTiC-ISM IDL routine. Bottom: Corrected light curve
residuals. The dashed lines indicate one and three times the standard
deviation.

###  2.3 Spitzer/IRAC

Continuing in our updated analysis, we re-reduced two archival transits of
WASP-17b obtained by Spitzer’s Infrared Array Camera (IRAC, Fazio et al.,
2004) as a part of Program 90092 (PI: Désert), incorporating recent advances
in exoplanet data reduction and light curve fitting techniques for Spitzer
observations. One transit was observed with the 3.6 $\micron$ channel on UT
2013 May 10, and one transit was observed with the 4.5 $\micron$ channel on UT
2013 May 14. Both observations had a frame time of 2 seconds, for a total of
14,720 frames in both channels.

We used the Photometry for Orbits, Eclipses, and Transits (POET, Campo et al.,
2011; Stevenson et al., 2012; Cubillos et al., 2013) pipeline, including
updates from May & Stevenson (2020) which apply a fixed sensitivity map in
order to remove the intrapixel sensitivity variations at 4.5 $\micron$. The
data is extracted using 2D Gaussian centroiding, with a fixed aperture size of
2.25 pixels for both transits. This aperture was determined by varying the
aperture in 0.25 pixel increments between 2.00 and 4.00 pixels and selecting
the size which results in the best signal difference to noise ratio (SDNR). We
further use a fixed annulus between 7 and 15 pixels for background
subtraction.

##  3 Updated Orbital Period

Figure 5: Top: Observed transit timings against calculated transit number, fitted with a linear relation. Middle: The residuals of this fit, giving the O-C diagram for previous mid-transit timings of WASP-17b. For details of the observations included, see Table 1. Bottom: Distributions of the periods of Triaud et al. (2010), Anderson et al. (2011) and Southworth (2012), along with the periods obtained when fitting different combinations of the values listed in Table 1. All uncertainties are assumed to be Gaussian. Table 1: Mid-transit times used in Figure 5 to calculate the period of WASP-17b. All times have been converted to BJDTDB. Observation | Epoch (BJDTDB) (Days)
---|---
Anderson et al. (2010) | 2454559.18175 $\pm$ 0.000694
Anderson et al. (2011) | 2454577.85879 $\pm$ 0.000694
Southworth (2012) | 2454592.802271 $\pm$ 0.000694
Sedaghati et al. (2016) | 2457192.698749 $\pm$ 0.000694
Spitzer IRAC Ch1 | 2456423.18973 $\pm$ 0.00023
Spitzer IRAC Ch2 | 2456426.9246 $\pm$ 0.0003
HST WFC3 G102 Visit 32 | 2457921.11772783 $\pm$ 0.000775
HST WFC3 G141 Visit 1 | 2457958.473652 $\pm$ 0.000775
HST STIS G430L Visit 5 | 2456367.15615529 $\pm$ 0.001615
HST STIS G430L Visit 6 | 2456086.99426107 $\pm$ 0.001615
HST STIS G750L Visit 19 | 2456370.8921914 $\pm$ 0.001499
TESS Sector 12 Transit 1 | 2458630.86200815 $\pm$ 0.000575
TESS Sector 12 Transit 2 | 2458634.59639647 $\pm$ 0.000596
TESS Sector 12 Transit 3 | 2458638.33169591 $\pm$ 0.000569
TESS Sector 12 Transit 4 | 2458645.80353243 $\pm$ 0.000597
TESS Sector 12 Transit 5 | 2458649.53850306 $\pm$ 0.000596
TESS Sector 38 Transit 1 | 2459336.86789830 $\pm$ 0.000514
TESS Sector 38 Transit 2 | 2459340.60377057 $\pm$ 0.000530
TESS Sector 38 Transit 3 | 2459344.33875063 $\pm$ 0.000571
TESS Sector 38 Transit 4 | 2459348.07425237 $\pm$ 0.000592
TESS Sector 38 Transit 5 | 2459351.81013867 $\pm$ 0.000548
TESS Sector 38 Transit 6 | 2459355.54543725 $\pm$ 0.000561
TESS Sector 38 Transit 7 | 2459359.28138246 $\pm$ 0.000563
Fitted Period (Days): | 3.73548546 $\pm$ 0.00000027

In preparation for light curve fitting, here we revisit and update WASP-17b’s
orbital period. After assessing the existing literature values for the system
parameters, we found that there is a discrepancy in published periods of
WASP-17b, with reported periods separated into two distinct groups $\sim$4 s
different from one another. As illustrated in Figure 5, several studies report
periods within $1\sigma$ of 3.735435 d (e.g., Anderson et al., 2010, 2011;
Sedaghati et al., 2016), while other studies (e.g., Southworth, 2012) and
initial fitting of the HST data obtain periods $\sim$4 s longer, within
$1\sigma$ of 3.735485 d. While this difference is only 0.001% of the period of
WASP-17b, accurate transit timings are a key element in observation planning
and light curve fitting, particularly in the case of HST where there are
significant gaps in observations during single transit events. Poor
constraints on a planet’s period and transit timings can lead to observations
which miss pre- or post-transit information (e.g., impacting the STIS
observations as outlined in Section 2.2), an effect which will continue to
worsen if the orbital ephemerides are not updated.

###  3.1 TESS

To obtain a longer temporal baseline for our refinement of the orbital period
of WASP-17b, we made use of transits observed by the Transiting Exoplanet
Survey Satellite (TESS Ricker et al. 2014).

During its primary mission, TESS observed 5 transits of WASP-17b (TIC
66818296) in sector 12, with a further 7 transits observed during the extended
mission in sector 38. The 12 total transits observed by TESS substantially
increase the baseline available for transit timing measurements (see Section
3.2). We downloaded the WASP-17b data from the MAST data
archive444https://archive.stsci.edu/ using the python package `lightkurve`
(Lightkurve Collaboration et al., 2018), before stitching the two sectors
together, normalising the light curve by its median value, and selecting the
PDCSAP flux (Smith et al., 2012) as the starting point of our analysis. All
the data has a cadence of 2 minutes, and we convert the times from BJTD
(Barycentric TESS Julian Date) to $\rm{BJD}_{\rm{TDB}}$.

We initially fit the entire light curve to generate a period estimate, found
to be 3.73548578 d, before fixing this value and individually fitting 1-day
windows of data centred on each transit, with the aim of measuring the mid-
transit times. For all of our TESS lightcurve fits, we use the python package
`batman` (Kreidberg, 2015) to model the transits, and the fast 1D Gaussian
Process (GP) code, `celerite` (Foreman-Mackey et al., 2017; Foreman-Mackey,
2018), to model any systematics. In the transit model, we allow the mid-
transit time ($T_{0}$), ratio of the planet to stellar radii ($R_{p}/R_{s}$),
ratio of the semi-major axis to stellar radius ($a/R_{s}$), and inclination
($i$) to vary, and set the eccentricity to zero (Southworth, 2012). We employ
a four-parameter non-linear limb-darkening law, with the coefficients fixed to
values computed using the Exoplanet Characterisation Toolkit (ExoCTK, Bourque
et al. 2021). For the systematics model, we use an approximate Matern-3/2
kernel, parameterised by a length scale and an amplitude, both of which are
marginalised over during the fitting. We include one additional free
parameter, a constant variance term, which is added to the diagonal of the
covariance matrix to account for underestimated uncertainties, however we find
this parameter is constrained to negligible values. Further tests were
performed to assess the sensitivity of the results to the transit model
parameterisation, the size of the data window around each transit, testing 1
and 2 day windows, and GP kernel type, testing a stochastically driven,
damped, harmonic oscillator kernel. We also test various limb-darkening laws,
with both fixed and free parameters. In all these tests we find the results
are insensitive to these choices.

The fitting is performed using the affine-invariant Markov Chain Monte Carlo
(MCMC) algorithm, `emcee` (Foreman-Mackey et al., 2013). The resulting mid-
transit times from all 12 TESS transits are shown in Table 1, where the
uncertainties are the $68$% credible intervals of the period posterior
distributions. These mid-transit times are later used to refine the orbital
period of WASP-17b, as described in Section 3.2. The final fit to all the TESS
data using our updated orbital period is shown in Figure 14.

###  3.2 Refining WASP-17b’s Orbital Period

We used the mid-transit times of WASP-17b from previous observations and those
presented in this work (see Table 1) to produce an observed - calculated mid-
transit timing (O-C) diagram in order to better refine the planet’s orbital
period. We converted all available transit times to BJDTDB using the tools and
methods outlined by Eastman et al. (2010), and fitted a linear relation to the
plot of observed transit times against the transit number (Agol & Fabrycky,
2018), to obtain an updated period, as shown in Figure 5. The resulting period
is 3.73548546 $\pm$ 0.00000027 d, and we henceforth adopt this value for this
work.

To visualise the improvement in the orbital period, Figure 5 shows the
probability densities of a selection of periods from previous works (Triaud et
al., 2010; Anderson et al., 2011; Southworth, 2012), along with the periods
obtained by fitting different combinations of the transit timings of the data
presented in Table 1. Assuming Gaussian uncertainties, we plot normal
distributions centred on the periods with standard deviations of the quoted
uncertainties. Due to the significant increase in transit timings now
available, we are able to improve upon previous constraints, and thanks to the
extended baseline provided by TESS, see the best refinement of the period
uncertainty when considering all available data.

##  4 Light Curve Fitting

To extract the 0.3 – 5.0 µm transmission spectrum of WASP-17b, we fit our
light curves to obtain their respective transit depths and relative
uncertainties. Our fitting procedure for the HST STIS and WFC3 white and
spectroscopic light curves is described in Section 4.1. Our fitting procedure,
along with Bilinearly Interpolated Subpixel Sensitivity (BLISS) mapping and
PRF detrending for the Spitzer IRAC photometric light curves is described in
Section 4.2.

Table 2: Star and Planet parameters held fixed in the light curve fitting. Parameter | Value | Reference
---|---|---
[Fe/H] (dex) | -0.25 | Southworth (2012)
$T_{\mathrm{eff}}$ (K) | 6550 | Southworth (2012)
log(g) | 4.2 | Southworth (2012)
$a/R_{*}$ | 7.025 | Sedaghati et al. (2016)
Eccentricity | 0 | Sedaghati et al. (2016)
|  | Weighted Mean of
Inclination (∘) | 86.9 | Anderson et al. (2010, 2011)
|  | & Sedaghati et al. (2016)
Period (Days) | 3.73548546 | This Work

###  4.1 Hubble

To fit the HST light curves and correct them for systematic effects from the
telescope and instruments, we use the systematic instrument marginalisation
method outlined in Wakeford et al. (2016). For our WFC3/G102 and G141 light
curves, we use the `ExoTiC-ISM` python package developed by Laginja & Wakeford
(2020). `ExoTiC-ISM` uses a Levenberg-Marquardt least-squares minimisation
over a grid of 50 systematic models555https://github.com/Exo-TiC/ExoTiC-
ISM#the-systematic-model-grid to obtain a set of fitted transit parameters for
each model, making use of the resulting Akaike Information Criterion (AIC,
Akaike 1974) to calculate each model’s evidence and weight. The weights can
then be used to calculate marginalised fit parameters, resulting in robust
transit depths that do not heavily depend on the choice of an individual
systematic model, and, as shown in previous work, obtains spectra consistent
with other approaches (e.g., Wakeford et al., 2016; Wakeford et al., 2020).

As `ExoTiC-ISM` is currently only set up for WFC3 systematics, to analyse the
STIS light curves we used a custom IDL (Interactive Data Language, Landsman
1995) routine to implement the systematic models used in Wakeford et al.
(2017). These models account for a linear slope in time ($\theta$), HST
thermal breathing ($\phi$), shifts in wavelength position
($\delta_{\lambda}$), and the x and y positional shifts on the detector
throughout the observation, required due to the use of the slit in STIS
spectra. The STIS systematic models take the form

$S(t,\lambda)=T_{1}\theta+\sum_{i=1}^{4}p_{i}\phi^{i}+l_{1}\delta_{\lambda}+a_{1}x+a_{2}y\mathrm{,}$
(1)

where $T_{1}$, $p_{i}$, $l_{1}$, $a_{1}$, and $a_{2}$ are coefficients fixed
to zero or free parameters in the systematic model being fit. In total we fit
80 systematic models for each STIS light curve, computing the evidence for
each model based on the AIC. The evidence is then used to compute a weighting
for each model which is used to marginalise the results to obtain the
marginalised transit depth and uncertainty following the steps outlined in
Wakeford et al. (2016) and processed using the `ExoTiC-ISM` framework.

In both the WFC3 and STIS light curves we account for stellar limb darkening
using a 4-parameter non-linear limb-darkening law (Claret, 2000; Sing, 2010)
and, due to the lack of phase coverage, fix the values to those derived from
the 3D stellar models presented in Magic et al. (2015), using the stellar
parameters shown in Table 2. The stellar models can be found in the `ExoTiC-
ISM` python package where they can be implemented for both WFC3 and STIS
spectroscopic instruments.

####  4.1.1 White Light Curves

For both WFC3 and STIS, we first fit the white light curves, fitting for the
mid-transit time, transit depth and baseline stellar flux, and holding
$a/R_{*}$, eccentricity and inclination fixed to literature values, with the
period fixed to the new value (see Section 3, as outlined in Table 2). For a
more accurate fit, we computed the weighted mean of the inclinations presented
in Anderson et al. (2010, 2011) and Sedaghati et al. (2016), and hold the
inclination fixed to this value. The raw and resulting best fit WFC3 and STIS
white light curves are shown in Figures 2 and 4, while the resulting transit
depths are given in Table 3.

Table 3: Results of the white light curve fits for all visits and relative uncertainties. Instrument | Visit | Transit | Error
---|---|---|---
| Number | Depth (%) | (%)
HST WFC3 G102 | 32 | 1.4778 | 0.0028
HST WFC3 G141 | 1 | 1.4899 | 0.0024
HST STIS G430L | 5 | 1.5081 | 0.0150
HST STIS G430L | 6 | 1.5708 | 0.0161
HST STIS G750L | 19 | 1.5023 | 0.0276
Spitzer IRAC | Ch1 | 1.5177 | 0.0123
Spitzer IRAC | Ch2 | 1.5679 | 0.0149
TESS Combined | - | 1.5103 | 0.0121

####  4.1.2 Spectroscopic Light Curves

We next fit the spectroscopic light curves, following the same procedures as
for the white light curve, marginalising over the systematic grids for each
spectroscopic light curve individually. The residuals of these fits are shown
in the lower panels of Figures 1 and 3 for our WFC3 and STIS observations
respectively. The middle panels of Figures 1 and 3 also show the pixel maps of
the stellar spectra once the extraction process is complete. These plots
demonstrate our high quality low noise data and extraction and fitting
routines, with no obvious bad pixels in the stellar spectra, and no wavelength
dependent trends in the spectroscopic light curves.

The measured transit depth values and errors for each spectroscopic channel
are shown in Tables 4, 5 and 6. Our final transmission spectrum combining the
information from all five visits can be seen in Figure 8, with the combined
transit depth and error of the two G430L visits given by their weighted mean,
as shown in Table 6.

Table 4: Results of the wavelength-binned fits for WFC3/G102 and G141 visits (Figures 15 & 16) and relative uncertainties. $\lambda$ | $\Delta\lambda$ | Transit | Error
---|---|---|---
(µm) | (µm) | Depth (%) | (ppm)
– G102 –
0.81979 | 0.01902 | 1.50766 | 0.0162
0.83881 | 0.01902 | 1.48501 | 0.0139
0.85783 | 0.01902 | 1.49152 | 0.0132
0.87803 | 0.02140 | 1.46812 | 0.0115
0.89943 | 0.02140 | 1.47618 | 0.0109
0.92796 | 0.03566 | 1.46501 | 0.0080
0.95768 | 0.02377 | 1.48400 | 0.0093
0.97670 | 0.01426 | 1.48733 | 0.0115
0.99215 | 0.01664 | 1.46927 | 0.0107
1.01236 | 0.02377 | 1.47548 | 0.0090
1.03376 | 0.01902 | 1.46933 | 0.0099
1.05278 | 0.01902 | 1.44943 | 0.0101
1.07180 | 0.01902 | 1.47691 | 0.0104
1.09087 | 0.01902 | 1.50017 | 0.0105
1.11103 | 0.02140 | 1.50330 | 0.0098
– G141 –
1.13801 | 0.01816 | 1.51271 | 0.0118
1.15845 | 0.02270 | 1.49809 | 0.0111
1.17888 | 0.01816 | 1.47967 | 0.0113
1.19704 | 0.01816 | 1.47529 | 0.0117
1.21747 | 0.02270 | 1.48210 | 0.0104
1.23790 | 0.01816 | 1.47744 | 0.0109
1.25606 | 0.01816 | 1.45743 | 0.0116
1.28557 | 0.04086 | 1.47289 | 0.0081
1.31509 | 0.01816 | 1.49151 | 0.0114
1.33325 | 0.01816 | 1.49072 | 0.0114
1.35141 | 0.01816 | 1.51744 | 0.0123
1.37184 | 0.02270 | 1.50988 | 0.0112
1.39227 | 0.01816 | 1.52036 | 0.0119
1.41043 | 0.01816 | 1.52419 | 0.0118
1.43086 | 0.02270 | 1.51715 | 0.0109
1.45130 | 0.01816 | 1.51481 | 0.0121
1.46946 | 0.01816 | 1.53627 | 0.0129
1.48762 | 0.01816 | 1.52800 | 0.0131
1.50805 | 0.02270 | 1.51300 | 0.0117
1.52848 | 0.01816 | 1.50233 | 0.0137
1.54664 | 0.01816 | 1.49508 | 0.0132
1.56480 | 0.01816 | 1.45936 | 0.0133
1.58524 | 0.02270 | 1.46988 | 0.0135
1.60567 | 0.01816 | 1.47278 | 0.0145
1.62383 | 0.01816 | 1.44110 | 0.0148
Table 5: Results of the wavelength-binned fits for STIS/G750L visit 19 (Figure 18) and relative uncertainties. $\lambda$ | $\Delta\lambda$ | Transit | Error
---|---|---|---
(µm) | (µm) | Depth (%) | (ppm)
0.55000 | 0.04000 | 1.44694 | 0.0629
0.57940 | 0.01880 | 1.48929 | 0.0621
0.58930 | 0.00100 | 1.55754 | 0.1999
0.61240 | 0.04520 | 1.52691 | 0.0422
0.65250 | 0.03500 | 1.46307 | 0.0448
0.69000 | 0.04000 | 1.51113 | 0.0429
0.73800 | 0.05600 | 1.50697 | 0.0401
0.76840 | 0.00480 | 1.50241 | 0.1365
0.79540 | 0.04920 | 1.54974 | 0.0789
0.85000 | 0.06000 | 1.47629 | 0.0660
0.91000 | 0.06000 | 1.43743 | 0.0692
0.98500 | 0.09000 | 1.47111 | 0.0853
Table 6: Results of the wavelength-binned fits for STIS/G430L visits 5 and 6 (Figure 17) and the weighted average transit depths, along with their relative uncertainties. $\lambda$ | $\Delta\lambda$ | Visit 5 | Error | Visit 6 | Error | Combined | Error
---|---|---|---|---|---|---|---
(µm) | (µm) | Transit Depth (%) | (ppm) | Transit Depth (%) | (ppm) | Transit Depth (%) | (ppm)
0.34000 | 0.10000 | 1.55130 | 386 | 1.61964 | 419 | 1.58547 | 285
0.40500 | 0.03000 | 1.46790 | 385 | 1.63700 | 363 | 1.55245 | 265
0.42750 | 0.01500 | 1.55149 | 400 | 1.62705 | 412 | 1.58927 | 288
0.44250 | 0.01500 | 1.49618 | 348 | 1.58308 | 391 | 1.53963 | 262
0.46250 | 0.02500 | 1.48968 | 272 | 1.54781 | 253 | 1.51874 | 186
0.47960 | 0.00920 | 1.50913 | 471 | 1.63180 | 498 | 1.57046 | 343
0.48610 | 0.00400 | 1.55433 | 716 | 1.57314 | 691 | 1.56374 | 498
0.49410 | 0.01200 | 1.49842 | 372 | 1.51725 | 400 | 1.50784 | 273
0.50505 | 0.00990 | 1.54108 | 425 | 1.55753 | 482 | 1.54931 | 321
0.51750 | 0.01500 | 1.49238 | 345 | 1.49490 | 347 | 1.49364 | 244
0.53250 | 0.01500 | 1.52485 | 370 | 1.52233 | 349 | 1.52359 | 255
0.54500 | 0.01000 | 1.49947 | 430 | 1.53033 | 434 | 1.51490 | 305
0.55500 | 0.01000 | 1.53247 | 465 | 1.52748 | 484 | 1.52998 | 335
0.56500 | 0.01000 | 1.58632 | 665 | 1.47848 | 623 | 1.53240 | 456

###  4.2 Spitzer Photometry

Figure 6: Top: Standard BLISS map best fit for the 3.6 $\micron$ transit.
Bottom: Fixed sensitivity map for the 4.5 $\micron$ transit. The centroids for
this transit overlap entirely with the fixed sensitivity map. Figure 7:
Photometric transit light curves of WASP-17b for two Spitzer IRAC
observations, Channel 1 (left) and Channel 2 (right). Top: The raw light
curves along with the flux binned to phase increments of 0.002 (645 $s$).
Middle: Corrected, normalised light curves and best fit model. Bottom:
Corrected light curve residuals. The dashed lines indicate one and three times
the standard deviation.

####  4.2.1 The Intrapixel Sensitivity Effect

Intrapixel sensitivity variations as the centroid drifts within a single pixel
dominate the sources of Spitzer IRAC systematics at 3.6 and 4.5 $\micron$. To
address this, we use the standard Bilinearly Interpolated Subpixel Sensitivity
(BLISS) mapping technique introduced by Stevenson et al. (2012) at 3.6
$\micron$, and the fixed sensitivity map introduced by May & Stevenson (2020)
at 4.5 $\micron$. We use the standard BLISS mapping technique at 3.6 $\micron$
instead of a fixed sensitivity map due to the time variability of the
intrapixel effect in this channel, as discussed in May & Stevenson (2020).

The standard BLISS map is defined by (1) an intrapixel spatial binning size,
optimised by comparing the Bayesian Information Criterion (BIC, Liddle, 2007)
of map fits to those done with a nearest neighbour approach to ensure the data
is not being overfit; and (2) the minimum number of exposure centroids
required in a given spatial bin, optimised by comparing the standard deviation
of the normalised residuals (SDNR) of fits varying this parameter. At 3.6
$\micron$, which uses this standard BLISS technique, we used a spatial binning
size of 0.006 pixels with a minimum number of 6 exposures in a given spatial
bin. Spatial bins with less than 6 exposures are ignored in the fit and
extrapolated over later.

The fixed sensitivity map needs only the spatial binning size optimised (See
Figure 6). Here we selected the spatial binning size which results in the best
BIC using the fixed map. At 4.5 $\micron$, which uses the fixed sensitivity
map, we used a bin size of 0.018 pixels. Because of the way this map is
generated, there is no requirement on the number of exposures in a given
spatial bin. The larger bin size at 4.5 $\micron$ as compared to 3.6 $\micron$
is reflective of the weaker intrapixel effect in this channel.

Figure 6 shows the BLISS maps from each transit on the spatial bin size used.

####  4.2.2 PRF Detrending

At 3.6 $\micron$ we also removed a functional dependence on the shape of the
IRAC point-response function (PRF). The PRF stretches away from a circle
towards an oval as the centroid approaches the edge of a pixel, resulting in
lost flux when circular photometry is performed. There are demonstrated
significant improvements in the quality of fits by removing this additional
source of noise (Lanotte et al., 2014; Demory et al., 2016a; Demory et al.,
2016b; Gillon et al., 2017; Mendonça et al., 2018). We find that a first order
function of the PRFs performs best for this 3.6 $\micron$ data. At 4.5
$\micron$, this effect is encapsulated in the fixed sensitivity map.

####  4.2.3 Astrophysical Source Models

We used the python package batman (Kreidberg, 2015) to model the Spitzer
transit events. The orbital parameters are held fixed to those given in Table
2, fitting only for the centre of transit time and transit depth. Limb
darkening is accounted for using a quadratic law based on the parameters given
in Table 2, using ExoCTK’s limb darkening tool (Bourque et al., 2021). We
adopted fixed values of {0.069,0.127} at 3.6 $\micron$ and {0.074, 0.091} at
4.5 $\micron$. We considered five possible temporal ramps: no ramp, an
exponential ramp, a linear ramp, a combined exponential + linear ramp, and a
quadratic ramp. The choice of ramp for each transit is identified by comparing
the $\Delta$BIC value, with best fits calculated using a Levenberg-Marquardt
minimiser. Parameter uncertainties are estimated using POET’s custom
Differential Evolution Markov Chain algorithm (ter Braak, 2006).

As discussed in Fu et al. (2021), Spitzer transit and eclipse depths can vary
strongly with the amount of data trimmed from the start due to strong initial
ramps, and due to temporal ramp modelling choices being degenerate with the
standard BLISS map. To mitigate this effect, we progressively trim each
dataset in 10 minute increments from 0 to 120 minutes, trying all 5 ramp
options for each case. We find that the 3.6 $\micron$ transit is not affected
by this dependence, with the linear ramp always preferred, and consistent best
fit transit depths across all trim levels for a linear ramp. We therefore
select a 40 minute trim based on visually inspecting the data for our final
fit. Due to separate degeneracies between BLISS mapping and PRF detrending
that worsen if data are temporally binned (May & Stevenson, 2020), we do not
perform temporal binning.

Our best model combination at 3.6 $\micron$ includes a linear temporal ramp, a
first order PRF detrending function, a standard BLISS map, and a transit
model. At 4.5 $\micron$ we find that no ramp is needed, and our best model
combination includes only the transit model and removing the fixed sensitivity
map. The raw and resulting best fit Spitzer photometric light curves are shown
in Figure 7, while the resulting transit depths are given in Table 3.

##  5 The Panchromatic Transmission Spectrum of WASP-17b

Our 0.3–5.0 µm transmission spectrum of WASP-17b is shown in Figure 8, wherein
no offsets have been applied between the datasets. We take the weighted mean
of the two G430L transmission spectra to produce the final quoted transmission
spectrum between 0.340-0.565 µm. The spectrum is characterised by distinct H2O
absorption features at 1.15 and 1.4 µm, a weak slope in the blue-optical and a
lack of evidence for absorption from Na i and K i (see Section 5.1). In
addition, our Spitzer observations show WASP-17b to have a deeper transit at
4.5 µm than at 3.6 µm, indicative of absorption by carbon bearing species such
as CO or CO2 /citepFortney2010. To ensure that our transmission spectrum is
robust regardless of the choice of binning scheme, we also test a variety of
bin sizes and positions across the STIS and WFC3 wavelength ranges, and found
that in all cases, the resulting transmission spectra are consistent within 1
sigma, showing the same shape and structure.

Figure 8: Transmission spectrum of WASP-17b measured over five visits with HST
WFC3/G102 and G141, and STIS/G430L and G750L modes, and two visits with
Spitzer IRAC. The two individually analysed STIS/G430L visits are shown in
pink and purple, and the transmission spectrum resulting from their weighted
average is shown by the blue circles, spanning 3400–5650 Å. The transmission
spectrum obtained by STIS/G750L from 5500–9850 Å is shown in green. The
WFC3/G102 spectrum is shown in orange, and covers 8199–11110 Å, while the WFC3
G141 spectrum is shown in red, and covers 11380–16238 Å. The photometric
observations by Spitzer IRAC Channels 1 and 2 are shown in brown. Also plotted
is the transit depth obtained from the joint fit of the transits observed by
TESS, shown in turquoise. The scale height of WASP-17b was calculated using
parameters obtained from Anderson et al. (2011), assuming an H/He atmosphere
with a mean molecular mass of $\mu=2.3$.

###  5.1 Search for Sodium and Potassium

We investigate the presence of Na i and K i absorption features in the G750L
spectrum, as the original analysis of the G750L spectrum by Sing et al. (2016)
showed evidence of Na i absorption, but not of K i. We make use of
spectroscopic channels with a range of widths, increasing incrementally in
steps of 10 Å from 10 to 30 Å , and then in steps of 20 Å from 30 to 250 Å
with each bin centred on 5893 Å for Na i and 7665 Å for K i. If an atmospheric
signal is present in the planetary spectrum, this binning scheme should show a
gradual decay in the transit depth with increasing bin width (e.g., Alam et
al., 2018, 2020; Alderson et al., 2020). The results of this analysis are
shown in Figure 9. Both the Na i and K i tests show a very small increased
depth in the narrowest bin, but are well within the 1$\sigma$ uncertainty of
the white light depth, indicating non-detections of the respective features.

Figure 9: Left: Na i absorption feature at 5893 Å shown in bins of
incrementally increasing width from 10–250 Å. Right: K i absorption feature at
7655 Å, shown in bins of incrementally increasing width from 10–250 Å. In both
plots, dashed lines indicate the transit depth and one sigma uncertainties of
the white light curve from the G750L observations (see Figure 4). Both plots
show a lack of increased absorption in narrower bins, indicating a non-
detection of the respective features.

###  5.2 Comparison to Previous Work

Figure 10: The transmission spectra of WASP-17b as presented by this work
(black), along with that of Mandell et al. (2013) (blue), Sing et al. (2016)
(lime green), and Sedaghati et al. (2016) (turquoise).

In addition to our HST+Spitzer transmission spectrum, there also exists ground
based transmission spectroscopy, presented by Sedaghati et al. (2016), the
original STIS and Spitzer analysis by Sing et al. (2016), and prior WFC3/G141
observations analysed by both Mandell et al. (2013) and Sing et al. (2016). We
show a comparison between these spectra and that of this work in Figure 10.
The shape of our transmission spectrum is broadly consistent with those of
Sedaghati et al. (2016), Sing et al. (2016) and Mandell et al. (2013).
Although there is an offset between the spectra presented in this work and by
Sing et al. (2016), this is due to the differences in the system parameters
used in the analyses (see Table 2 for updated and consistent system
parameters).

Notably, our new WFC3/G141 measurements have a greater precision compared to
previous analyses, which have an average uncertainty of 250 ppm over 19 bins
in the case of Mandell et al. (2013) and 393 ppm over 14 bins for Sing et al.
(2016), compared to the 120 ppm average uncertainty over 25 bins achieved in
this work. This marked improvement is driven by the implementation of WFC3’s
spatial scan mode and the more complete transit coverage. The addition of
WFC3/G102 data is also beneficial, bridging the gap in wavelength coverage
bluewards of WFC3/G141. This new overlap between WFC3/G102 and the red end of
STIS/G750L aides in anchoring the STIS data in the wavelength region where
fringing is known to have an impact, verifying the depth calibration of the
transmission spectrum across instruments and allowing for the detection of
multiple H2O absorption peaks.

In the optical regime, we utilise the same binning scheme as that of Sing et
al. (2016), but have the added advantage of using consistent and more robust
planetary parameters across all instruments, helping to avoid offsets between
the discontinuous datasets from each visit. Our non-detection of Na i
absorption (see Section 5.1) matches that of Sedaghati et al. (2016).
Additionally, while Wood et al. (2011) and Zhou & Bayliss (2012) were able to
detect an excess transit signal at the wavelength of Na i in narrow 1.5 Å bins
at $4\sigma$ and $4.5\sigma$ confidence respectively, Wood et al. (2011) found
no significant detection in bins wider than 4 Å, consistent with our result.
However, unlike Sedaghati et al. (2016), we also find no evidence of K i
absorption. This can likely be explained by the fact that the ground-based
spectroscopy of Sedaghati et al. (2016) was unable to probe the line core of K
i, so the quoted detection was driven only by potential evidence of the wings
of the feature which would increase the continuum level of the spectrum.
Figure 9 shows that while the K i absorption signal does appear to drop away
in larger bin sizes, suggestive of wings around the line core, the change is
fully encompassed by our observational uncertainties. Therefore, any detection
of K i remains inconclusive.

To contextualise the amplitude of the 1.4 $\micron$ H2O absorption feature in
the measured transmission spectrum of WASP-17b, we calculated the H2O – J
index (Stevenson, 2016) and the H2O absorption amplitude (Wakeford et al.,
2019). Using our transmission spectrum as presented in Figure 8, we find a H2O
– J index of $1.10\pm 0.18$. This is a higher amplitude and more precise value
than obtained in previous works (Stevenson 2016; Fu et al. 2017, using the
transmission spectrum of Mandell et al. 2013), and is thanks to the more
precise G141 data provided by our use of spatial scan mode observations and
improvements in transit light curve analysis techniques. The H2O absorption
amplitude is $35.8\pm 3.4$ % muted compared to that of a clear solar
metallicity atmosphere, which places WASP-17b firmly within the prediction
that H2O absorption features will typically be muted by aerosol opacities to
$33\pm 24$ % (Wakeford et al., 2019).

##  6 Interpretation of the Atmosphere of WASP-17b

Table 7: Uniform prior bounds applied to the POSEIDON retrieval suite. | POSEIDON Priors
---|---
$R_{\mathrm{p,ref}}$ ($R_{J}$) | 1.5895, 2.1505
$T$ (K) | 400, 2300
log($X_{\mathrm{i}}$) | -12, -1
log($a$) | -4, 8
$\gamma$ | -20, 2
log($P_{\mathrm{cloud}}$) | -6, 2
Table 8: Goodness of fit values and major species abundances from the suite of free chemistry retrievals run on the WASP-17b transmission spectrum (see Figure 11). The full transmission spectrum consists of 68 data points such that the degrees of freedom represents 68 - the number of free parameters. Each retrieval listed includes: three aerosol parameterisations ($\alpha$, $\gamma$, log($P_{\mathrm{cloud}}$)) and an isothermal temperature profile. Model 19 represents the most basic model. Bolded models are those presented in Figure 12. Model | Fit chemical species | DOF |  $\chi^{2}$ CDF (%) | BIC | ln($Z$) | log($X_{\mathrm{H_{2}O}}$) | log($X_{\mathrm{CO_{2}}}$)
---|---|---|---|---|---|---|---
19 | H2O, CH4, CO2 | 60 | 73.96 | 100.36 | 469.17 | -4.90${}^{+0.43}_{-0.27}$ | -5.80${}^{+0.72}_{-0.74}$
18 | H2O, CH4, CO2, CO | 59 | 78.47 | 105.24 | 469.02 | -4.86${}^{+0.45}_{-0.29}$ | -5.83${}^{+0.74}_{-0.80}$
17 (B) | H2O, CH4, CO2, Na, K, CO | 57 | 70.15 | 108.54 | 468.94 | -4.73${}^{+0.65}_{-0.32}$ | -5.64${}^{+0.89}_{-0.81}$
16 | H2O, CH4, CO2, TiO | 59 | 73.82 | 103.47 | 468.56 | -4.69${}^{+0.68}_{-0.35}$ | -5.55${}^{+0.90}_{-0.78}$
15 | H2O, CH4, CO2, Na, K, CO, TiO | 56 | 73.38 | 112.79 | 468.22 | -4.44${}^{+1.60}_{-0.48}$ | -5.25${}^{+1.52}_{-0.94}$
14 | H2O, CH4, CO2, Na, K, CO, VO | 56 | 73.38 | 112.79 | 467.41 | -4.42${}^{+1.97}_{-0.55}$ | -5.20${}^{+1.88}_{-1.06}$
13 | H2O, CH4, CO2, H- | 59 | 60.86 | 99.34 | 468.63 | -4.22${}^{+1.92}_{-0.91}$ | -5.22${}^{+2.00}_{-1.41}$
12 (A) | H2O, CH4, CO2, H-, TiO | 58 | 58.78 | 101.94 | 468.35 | -3.21${}^{+1.29}_{-1.62}$ | -4.19${}^{+1.43}_{-1.88}$
11 | H2O, CH4, CO2, H-, VO | 58 | 62.80 | 103.10 | 467.47 | -2.98${}^{+1.09}_{-1.85}$ | -4.06${}^{+1.30}_{-2.06}$
10 | H2O, CH4, CO2, H-, Na, K, CO | 56 | 60.71 | 108.87 | 468.80 | -2.91${}^{+1.05}_{-1.83}$ | -3.85${}^{+1.19}_{-2.13}$
9 | H2O, CH4, CO2, H-, Na, K, TiO | 56 | 62.67 | 109.43 | 468.66 | -2.32${}^{+0.59}_{-1.77}$ | -3.29${}^{+0.79}_{-1.80}$
8 | H2O, CH4, CO2, H-, Na, K, VO | 56 | 62.67 | 109.43 | 468.01 | -2.30${}^{+0.89}_{-2.82}$ | -3.25${}^{+1.63}_{-5.00}$
7 | H2O, CH4, CO2, H-, Na, K, CO, TiO | 55 | 66.35 | 113.70 | 468.60 | -2.28${}^{+0.58}_{-1.69}$ | -3.25${}^{+0.76}_{-1.79}$
6 | H2O, CH4, CO2, H-, Na, K, CO, VO | 55 | 64.50 | 113.15 | 468.28 | -2.16${}^{+0.49}_{-1.49}$ | -3.13${}^{+0.69}_{-1.58}$
5 (C) | H2O, CH4, CO2, H-, Na, K, CO, VO, TiO | 54 | 68.04 | 117.39 | 467.97 | -1.99${}^{+0.41}_{-0.98}$ | -2.94${}^{+0.58}_{-1.06}$
4 | H2O, CH4, CO2, H-, Na, K, CO, VO, CrH, FeH | 53 | 72.94 | 122.12 | 466.55 | -1.87${}^{+0.34}_{-0.59}$ | -2.80${}^{+0.51}_{-0.74}$
3 | H2O, CH4, CO2, H-, Na, K, CO, TiO, VO, CrH, FeH | 52 | 75.83 | 126.27 | 466.32 | -1.80${}^{+0.31}_{-0.49}$ | -2.72${}^{+0.46}_{-0.64}$
2 | H2O, CH4, CO2, H-, Na, K, CO, TiO, VO, CrH, FeH, | 50 | 82.04 | 134.95 | 465.31 | -1.69${}^{+0.27}_{-0.36}$ | -2.60${}^{+0.42}_{-0.53}$
| AlO, TiH |  |  |  |  |  |
1 | H2O, CH4, CO2, H-, Na, K, CO, TiO, VO, CrH, FeH, | 43 | 93.76 | 163.54 | 465.98 | -1.68${}^{+0.26}_{-0.31}$ | -2.59${}^{+0.43}_{-0.49}$
|  AlO, TiH, Li, SiO, AlH, CaH, SiH, HCN, SH (No CH4) |  |  |  |  |  |
A IR-Only | H2O, CH4, CO2, H-, TiO | 32 | 96.32 | 89.88 | 300.89 | -2.75${}^{+0.80}_{-1.24}$ | -3.81${}^{+0.98}_{-1.43}$
B IR-Only | H2O, CH4, CO2, Na, K, CO | 31 | 98.29 | 96.32 | 300.82 | -2.23${}^{+0.47}_{-0.87}$ | -3.22${}^{+0.65}_{-1.00}$
C IR-Only | H2O, CH4, CO2, H-, Na, K, CO, VO, TiO | 28 | 98.22 | 104.99 | 300.48 | -3.93${}^{+1.20}_{-0.68}$ | -4.81${}^{+1.21}_{-0.96}$
ATMO | Fixed [M/H], Includes opacities from: |  |  |  |  |  |
| (H2O, CH4, CO2, H-, Na, K, CO, TiO, VO, | 62 | 77.19 | 95.27 | - | -2.10${}^{+0.22}_{-0.78}$ | -3.93${}^{+0.41}_{-0.85}$
|  FeH, Li,Rb, Cs, PH3, H2S, HCN, C2H2, SO2, Fe, NH3) |  |  |  |  |  |
Figure 11: Retrieval statistics, retrieved parameter values and 1 $\sigma$
uncertainties for the suite of POSEIDON free chemistry retrievals (grey
points). Each retrieval is labelled 1 - 19 in order of H2O abundance from high
to low values. The retrieval preferred by reducing the $\chi^{2}$ CDF, 12, is
shown in green (referred to as Model A in the text and Figure 12). The purple
(17, B) and blue (5, C) points show retrievals from the low and high abundance
modes of H2O which have the best $\chi^{2}$ CDF. Retrievals B and C are used
throughout the text and in Figure 12 to highlight the differences between the
low and high abundance modes of H2O, the separation between which is shown by
the vertical green line in the H2O panel. Results from the ATMO equilibrium
chemistry retrieval are shown by the red points. The ln($Z$) for ATMO is not
shown as due to the differences in the retrieval two codes, the resulting
values are not comparable.

Figure 12: Top: Retrieved transmission spectra for the three highlighted
POSEIDON free chemistry retrievals (Model A in green, Model B in purple and
Model C in blue), and for the ATMO equilibrium chemistry retrieval (in red).
For each model, the 1 and $2\sigma$ uncertainties are given by the shaded
regions. The transmission spectrum of WASP-17b as obtained by HST and Spitzer
is shown by the black points. Bottom: Marginalised posterior probability
distribution histograms of the three highlighted POSEIDON free chemistry
retrievals (Model A in green, Model B in purple and Model C in blue) for a
selection of retrieved parameters, along with the inferred O/H, C/H and C/O
ratios (derived from the mixing ratio samples). In the case of retrieved
parameters, errorbars show the median retrieved values and the $1\sigma$
confidence level. Retrieved parameters from ATMO are shown as red errorbars at
the top of the relevant histogram plots.

To interpret the measured transmission spectrum of WASP-17b, we ran a suite of
atmospheric retrieval exercises to better understand the thermochemical
properties and compositions that contribute to the atmosphere. Atmospheric
retrieval analysis involves the fitting of atmospheric models to an observed
spectrum via a parameter estimation technique in order to obtain robust
inferences of the uncertainties and trends in the parameters (Madhusudhan &
Seager, 2009). Retrieval frameworks can operate using a free chemistry model
(e.g., POSEIDON MacDonald & Madhusudhan (2017)), in which the volume mixing
ratios of each chemical species included within a model atmosphere are treated
as separate free parameters, or using a chemical equilibrium model (e.g., ATMO
Amundsen et al. (2014); Tremblin et al. (2015); Wakeford et al. (2017)), in
which the abundances of the complete chemical inventory are solved for
assuming that the chemical processes governing the atmosphere are in
equilibrium. By utilising multiple retrieval methods, the impacts of different
modelling choices can be quantified, while comparison between them can hint at
processes that would be difficult to detect with a single method (e.g.,
Baudino et al., 2017; Barstow et al., 2018; Lewis et al., 2020; Rathcke et
al., 2021; Barstow et al., 2022). For example, comparing the results of free
and equilibrium chemistry retrievals can help to ascertain whether
disequilibrium processes are at play (e.g., Baudino et al., 2017; Lewis et
al., 2020). We therefore employ a suite of free chemistry retrievals which we
compare to the results of an equilibrium chemistry retrieval (see Section
6.2.2).

We conducted a comprehensive suite of free chemistry retrievals using the
POSEIDON retrieval framework (MacDonald & Madhusudhan, 2017). POSEIDON couples
an exoplanet radiative transfer module with a Bayesian sampling algorithm to
explore the range of atmospheric properties consistent with a given
transmission spectrum. As POSEIDON operates under free chemistry, we use this
framework to explore the combination of different chemical species and
retrieval set-ups, and later compare it to equilibrium chemistry, in which the
network of chemical species is fixed (see Section 6.2.2). We explore the
parameter space using `PyMultiNest` (Feroz & Hobson, 2008; Feroz et al., 2009;
Buchner et al., 2014a; Feroz et al., 2019), a Bayesian nested sampling
algorithm implemented in python, using 4,000 live points.

We investigated a broad range of scenarios to explain our WASP-17b
transmission spectrum. Across our suite of free retrievals we consider an
extensive list of optical and IR opacity sources, including: Na, K, Li, H2O,
CH4, CO, CO2, TiO, VO, AlO, SiO, TiH, CrH, FeH, AlH, CaH, SiH, HCN, and NH3.
Our cross sections are derived from ExoMol (Tennyson et al., 2016) and VALD3
(Pakhomov et al., 2017) line lists, as detailed in MacDonald (2019). We also
include continuum opacity from H- (John, 1988), H2-H2 and H2-He collision-
induced absorption from HITRAN (Karman et al., 2019), and Rayleigh scattering.
We describe aerosols via a cloud deck and a wavelength-dependent scattering
haze, both of which may be inhomogenous around the terminator (MacDonald &
Madhusudhan, 2017). The scattering is parameterised by log($a$), the Rayleigh
enhancement factor, and $\gamma$, the scattering slope. The cloud deck is
parameterised by log($P_{\mathrm{cloud}}$), representing the top pressure of a
cloud within infinite opacity at all wavelengths (see MacDonald & Madhusudhan,
2017, for further details). We consider both an isothermal, gradient and a
parametric pressure-temperature (P-T) profile (Madhusudhan & Seager, 2009).
The impacts of stellar contamination (Rackham et al., 2017) on WASP-17b’s
transmission spectrum are parameterised by the temperature and covering
fraction of stellar heterogeneities and the photosphere temperature (see
Rathcke et al., 2021). All retrievals also fit for the planetary radius at the
10 bar reference pressure, $R_{p,\,\rm{ref}}$. We compute our model spectra
from 0.28 to 5.2 $\micron$ at $R=$ 4,000 before binning down to the resolution
of the observations.

Throughout our retrieval analysis, we employ three statistical metrics to
determine the optimal retrieval, defined by a set of free parameters, from
which we draw its corresponding best fitting parameters to define the
transmission spectrum model. We make use of the maximisation of the evidence,
ln($Z$), along with the minimisation of the BIC to define the optimised
retrieval parameterisation. We also consider a frequentist metric given by the
cumulative distribution function (CDF) of the $\chi^{2}$ distribution (see
Wilson, 2021, for a discussion of the $\chi^{2}$ CDF in a model selection
context), which defines the probability that a better fit to the most likely
model in that retrieval can be obtained given a random draw of residuals with
the same uncertainties.

###  6.1 Determining Retrieval Model Complexity

We first conduct several exploratory retrievals to assess the necessary model
complexity to fit WASP-17b’s transmission spectrum. Our initial explorations
of the transmission spectrum test three P-T profiles, an isotherm, a
temperature gradient linear in log-pressure space, and the 6-parameter profile
from Madhusudhan & Seager (2009). The retrievals indicated that the
transmitting atmosphere of WASP-17b is best modelled by an isothermal P-T
profile, with a $\Delta$BIC of 16 and 26 compared to the gradient and
Madhusudhan & Seager (2009) profiles respectively. These $\Delta$BIC values
represent strong evidence against the need for the more complex P-T profiles.

We next determined the minimum complexity cloud parameterisation required. We
ran a series of retrievals with the same chemical species but varying the
inclusion of one or both of uniform and wavelength dependent scattering cloud
prescriptions. Our tests indicate the need to include both a cloud deck and a
scattering parmeterisation, with a minimum 15% improvement in the $\chi^{2}$
CDF compared to those with just one or without either prescription considered.
We found that inhomogenous ‘patchy’ clouds were disfavoured by the $\chi^{2}$
CDF by 10% and $\Delta$BIC of 11, but did show a minimal improvement in the
lnZ of $<$ 1, given these statistics and the current data quality we do not
include these parameterisations in further retrieval fits shown in this paper.

Finally, as stellar activity can mimic the presence of scattering slopes
(McCullough et al., 2014; Oshagh et al., 2014), we additionally test the
impacts of stellar contamination on the transmission spectrum from stellar
spots and plages. The retrieved heterogeneity temperatures were fully
consistent with the stellar photospheric temperature of WASP-17, with $<$10%
retrieved covering fractions, consistent with 0% within the 2 $\sigma$
uncertainties. This, combined with a log$R^{\prime}_{\mathrm{HK}}$ value of
-5.531 (Sing et al., 2016), and no prior evidence of significant stellar
variability, suggests that stellar activity does not play a role in the
observed transmission spectrum, and we therefore do not consider its inclusion
further.

###  6.2 Exploring the Chemical Inventory

Due to its 1770 $K$ equilibrium temperature, WASP-17b lies at a critical
juncture within the close-in giant planet population, sitting between the
ultra-hot and hot Jupiters at the traditional pM/pL boundary (Fortney et al.,
2008), where atmospheres become warm enough for TiO and VO to have a
significant opacity. Furthermore, P-T profiles by Kataria et al. (2016) show
that the range of temperatures from the day- to the night-side of WASP-17b
spans 1000-2500 $K$, resulting in a wide variety of plausible
spectroscopically active species. As a result of the potential chemical
diversity of the atmosphere of WASP-17b, we explored a wide range of species
within our `POSEIDON` free-retrievals to evaluate the dominant absorbing
species influencing WASP-17b’s measured atmosphere.

Across the suite of retrievals used to analyse the chemical composition we
include scattering and uniform cloud parameterisations (three additional free
parameters, log($a$), $\gamma$ and log($P_{\mathrm{cloud}}$)), an isothermal
P-T profile (one free parameter, $T$), and opacities from a combination of
chemical species, including H-, Na, K, Li, H2O, CH4, CO, CO2, TiO, VO, AlO,
SiO, TiH, CrH, FeH, AlH, CaH, SiH, HCN, and NH3. We summarise the priors for
our free parameters in Table 7. Our full exploration resulted in a suite of 19
retrievals that include absorption from a minimum of three spectroscopically
active species (H2O, CH4 and CO2) to a maximum of 20 (see Table 8). The
retrievals are labelled from 1 - 19, with retrieval 19 representing the most
basic configuration and 1 the most complex, as shown in Figure 11.

We find that, based on minimising the $\chi^{2}$ CDF, the most statistically
favoured model includes opacity sources from H2O, CO2, CH4, H- and TiO, and is
shown in Figure 12, labelled as A and plotted in green. Despite this retrieval
seemingly being preferred, the posteriors for many parameters in this
retrieval were bimodal or spanned a large parameter space, resulting in both
high and low chemical abundances being possible, as seen in Figures 11 and 12.
To explore the apparent degeneracies in the retrieval, resulting in a bimodal
distribution in a number of key factors, we test a large number of other
species combinations, to determine if a more physically viable solution could
be retrieved (see Figure 12).

Within the suite, the best-fit models from each retrieval are ordered from the
highest (Model 1) to the lowest (Model 19) retrieved H2O abundance (see Figure
11). We find a continuum of median H2O abundances from -1.68 to -4.9, with
multiple retrievals constraining abundances at either end of this range, and a
selection of retrievals with bimodal distributions in between. As ordered by
H2O abundance in Figure 11, our $\chi^{2}$ CDF preferred retrieval A is the
12th configuration. To compare the best fitting spectra obtained by the high
and low abundance modes, we also highlight the retrieval with the best
$\chi^{2}$ CDF which obtains a well constrained H2O abundance for each mode.
As shown in Figures 11 and 12, these are retrieval 17 for the low abundance
mode and the retrieval 5 for the high abundance mode (henceforth referred to
as B and C). The best fit models of A, B, and C show broad agreement at
optical wavelengths, with small differences due to the range of opacity
sources included in each retrieval. The three highlighted models diverge the
most into the IR beyond 1.6 $\micron$, with A and C fitting the downturn in
the final four WFC3/G141 data points, and B producing a flatter spectrum after
1.6 $\micron$ due to the difference in the more dominant species in each
retrieval.

We see a broad range of trends in the retrieved parameters within our suite,
with the abundances of CO2 and CH4 following the same pattern as that of H2O.
There is also a degeneracy between the chemical abundances and aerosol
properties of the atmosphere, and trends in the inclusion of H-. All
retrievals find no evidence of Na or K where included. All three of the
highlighted retrievals obtain consistent best fit values of $T$, and
posteriors of the C/O ratios consistent with sub-solar values (see Figure 12).
However, given the extent of bimodality within our retrieval suite, there is a
lack of clarity as to the exact chemical composition of the atmosphere.
Ordinarily, adding redundant parameters to the retrieval with the highest
Bayesian evidence should not result in any significant changes in the
conclusions of that retrieval. Such model instability likely speaks to poorly
constraining data at key wavelengths, and we therefore must investigate the
role of the data in these wavelength ranges.

####  6.2.1 Inclusion of Optical Data

To better explore the role of the optical data in the retrieved atmospheric
abundances, we ran each of our three highlighted retrieval set-ups without the
STIS/G430L and G750L optical data. In doing this, we are able to assess to
what extent our chemical compositions and abundances are driven by the higher
precision IR data alone, and are better able to understand the importance of
the inclusion of optical data when constraining the impacts of uniform and
wavelength-dependant aerosol scattering.

In all cases, we find that removing the STIS/G430L and G750L optical data from
the retrievals results in worse $\chi^{2}$ CDF and leads to significantly
poorer constraints on all three aerosol scattering parameters. Both the full
transmission spectrum and IR-only retrievals achieve chemical abundances
within $\sim 1\sigma$ for the key species included in all retrievals (H2O, CH4
and CO2). We also obtain consistent abundances and uncertainties for CO and
the optical species considered in each retrieval, implying that the poorer
precision achieved by STIS due to the partial transits observed (see Section
2) is a limiting factor in our ability to retrieve a better constrained
inventory of the optical species present within the atmosphere.

We show a comparison between the full transmission spectrum and the IR-only
retrieval for our statistically preferred configuration, retrieval A, in
Figure 13. This highlights the similarity between the best fitting models at
IR wavelengths, and also demonstrates the disparity at the optical wavelengths
which drive constraints on aerosol scattering. Additionally, the removal of
the optical data enables the retrieval to better converge on regions of the
phase space as opposed to producing bimodal posterior distributions, with a
$\sim 0.4$ dex improved constraint when considering only the IR data (see
Table 8).

Although IR-only retrievals can obtain improved constraints on the H2O
abundance, we continue to consider the STIS data in our analysis of the
atmosphere of WASP-17b to gain a more complete understanding of the processes
and chemistry present, and to aid in anchoring the abundance constraints. The
increased wavelength coverage provided by STIS holds critical information on
the role of aerosol scattering, which in turn impacts the understanding of
retrieved abundances (Wakeford et al., 2018), and enables us to holistically
interpret the atmosphere.

Figure 13: Comparison between the retrieved transmission spectra of a POSEIDON
free chemistry retrieval of the full 0.3-5 µm transmission spectrum using
STIS, WFC3/IR and Spitzer (blue), and the of same retrieval without the
inclusion of STIS data (orange). Both retrievals use the retrieval A
configuration as outlined in Table 8. For each model, the 1 and $2\sigma$
uncertainties are given by the shaded regions. The transmission spectrum of
WASP-17b as obtained by WFC3/IR and Spitzer is shown by the black points,
while the STIS data is shown in grey. The difference in the $\sigma$
uncertainties between 0.3-0.8 µm highlights the importance of optical data in
constraining the scattering slope properties of the atmosphere.

####  6.2.2 Comparison with Equilibrium Chemistry

To better assess the physical nature of our free-chemistry retrievals, we also
explore the implications of equilibrium chemistry on the atmosphere of
WASP-17b. To do so, we utilised the `ATMO` retrieval code (Amundsen et al.,
2014; Tremblin et al., 2015; Wakeford et al., 2017) which has been previously
bench-marked against `POSEIDON`’s free chemistry (Lewis et al., 2020; Rathcke
et al., 2021). `ATMO` computes the chemistry for each atmospheric layer and
wavelength such that the opacities are physically self-consistent with the
retrieved P-T profile and chemical composition for every likelihood evaluation
step. The equilibrium chemical network includes abundances for 175 neutral, 9
ionic, and 93 condensate species. The retrieval considers 22 spectrally active
opacity species (see Goyal et al. 2018 and Goyal et al. 2020 for a full
description and Table 8 for a full list of species), and rainout chemistry
such that condensed materials are removed from the gaseous species in layers
above their condensation point.

Table 9: Statistics and retrieved parameters for the ATMO equilibrium
chemistry retrieval. See Table 8 for the full list of spectrally active
species.

| 175 neutral, 9 ionic, and
---|---
Species | 93 condensate species with
| 22 spectrally active
Free Parameters | 6
Degrees of Freedom | 62
$\chi^{2}_{\nu}$ | 1.13
BIC | 95.27
$\chi^{2}$ CDF (%) | 77.19
$R_{p}$ ($R_{J}$)$\dagger$ | 1.895${}^{+0.033}_{-0.013}$
$T$ ($K$) | 1250.3${}^{+167.7}_{-265.7}$
log($Z/Z_{\odot}$) | 1.52${}^{+0.24}_{-0.57}$
log($X_{\mathrm{H_{2}O}}$)$\dagger$ | -2.10${}^{+0.22}_{-0.78}$
log($X_{\mathrm{CH_{4}}}$) | -9.64${}^{+2.54}_{-1.13}$
log($X_{\mathrm{CO}}$)$\dagger$ | -1.77${}^{+0.23}_{-0.57}$
log($X_{\mathrm{CO_{2}}}$)$\dagger$ | -3.93${}^{+0.41}_{-0.85}$
log($a$) | ln($\sigma/\sigma_{0}$) = 6.34${}^{+0.75}_{-2.01}$
$\gamma$ | -4.27${}^{+0.52}_{-0.38}$
log($P_{\mathrm{cloud}}$) | ln($\sigma/\sigma_{0}$) = 1.27${}^{+0.96}_{-3.89}$

$\dagger$ Volume mixing ratio at 1 mbar, $R_{p}$ at 1 mbar.

As with the free-chemistry retrievals, we used an isothermal P-T profile and
included the effects from both wavelength-dependent scattering and uniform
cloud opacities. In addition, `ATMO` fits for the overall atmospheric
metallicity, which can be used to define the abundances for each active
species when scaled relative to solar. The results of this retrieval are
detailed in Table 9. To further evaluate the derived equilibrium chemistry
solution, we additionally ran an `ATMO` retrieval wherein the elemental
abundances of carbon and oxygen were free parameters along with an overall
atmospheric metallicity parameter. We find that carbon and oxygen were fully
consistent with each other, with a C/O ratio consistent with solar.

We show the retrieved transmission spectrum from the `ATMO` equilibrium
chemistry retrieval and its 1 and 2$\sigma$ uncertainties in Figure 12,
alongside our highlighted free chemistry retrievals. The equilibrium chemistry
model is in good agreement with Models A and C in the NIR, although `ATMO`
derives a shallower slope in the optical spectrum where we see the majority of
our degeneracies. The equilibrium chemistry retrieval finds similar
temperatures and H2O abundances to the free chemistry retrieval high abundance
mode (Model C), with a more moderate CO2 abundance, a low CH4 abundance, and a
significantly higher CO abundance, resulting in the difference in fit at 4.5
µm (See lower panels of Figure 12). Our `ATMO` retrieval finds a $33\times$
solar metallicity atmosphere, in agreement with the super-solar atmosphere
found under free chemistry `POSEIDON` configurations where a larger number of
spectroscopically active species were considered.

While the BIC of the `ATMO` retrieval indicates a good fit to the data, given
the low number of free parameters required, the somewhat relatively poorer
$\chi^{2}$ CDF demonstrates that equilibrium chemistry is potentially not
fully able to explain the measured transmission spectrum of WASP-17b. At the
retrieved temperatures, `ATMO` cannot include the opacities of species such as
H-, TiO and VO, as seen in free chemistry. Under equilibrium, Ti and V should
exist predominantly in solid condensates at temperatures $\lesssim 1200$ $K$
(Fortney et al., 2008), while the production of significant enough quantities
of H- would require temperatures $\gtrsim$ 2500 $K$ (Kitzmann & Heng, 2018).
To this end, it is therefore necessary to consider the potential implications
of disequilibrium chemical processes upon the measured atmosphere of WASP-17b.

####  6.2.3 Disequilibrium Chemistry

In this section, we consider the role of disequilibrium chemistry in the
interpretation of WASP-17b’s measured transmission spectrum. In the context of
exoplanetary atmospheres, disequilibrium processes such as photochemistry
(Fleury et al., 2019), quenching due to vertical and horizontal mixing (Prinn
& Barshay, 1977; Cooper & Showman, 2006) and ion chemistry (Lavvas et al.,
2008), can allow for the production of molecules at abundances that would not
be plausible if the atmosphere were in thermochemical equilibrium. Therefore,
the presence or overabundance of certain molecules within an observed
transmission spectrum can indicate the presence of such processes.
Disequilibrium chemistry is likely to play a key role in governing the
chemical make-up of the atmospheres of exoplanets (e.g., Zahnle et al., 2009;
Moses et al., 2011; Moses et al., 2013; Fleury et al., 2019; Steinrueck et
al., 2019; Mollière et al., 2020), and is thus an important consideration for
WASP-17b.

Under equilibrium chemistry and solar metallicity conditions, CO2, CO and H2O
are well-mixed throughout the atmosphere (Moses et al., 2011). Furthermore, at
the temperatures and metallicities retrieved and the pressures probed for our
observations of the atmosphere of WASP-17b, CO should be the dominant carbon-
bearing species over CO2, and CH4 would be disfavoured, as the retrieved
temperatures would be too high for stable CH4 production (Lodders & Fegley
2002, see also our `ATMO` chemical equilibrium retrieval, Figure 11). However,
across our suite of `POSEIDON` retrievals, we find that the carbon chemistry
is dominated by the abundance of CO2, which, along with H2O, shows an
increasing abundance trend with CH4. On the other hand, CO is consistently
retrieved to a low abundance, albeit with poor constraints. The significant
abundance of CO2 compared to CO in the atmosphere could be indicative of a
high overall metallicity (Heng & Lyons, 2016). However, as the carbon-bearing
species in the measured transmission spectrum are encapsulated by the two
Spitzer photometric points alone, our understanding of the carbon chemistry
must remain limited to detections rather than precise abundance constraints
(see Section 6.2.4).

Our free chemistry retrieval suite results are further at odds with
equilibrium chemistry due to the potential presence of an overabundance of H-
in many retrievals across our suite. Across our free chemistry retrieval
suite, the increasing abundance of H- is driven by the downturn in the final
four WFC3/G141 data points, as seen for example in retrievals A and C in
Figures 11 and 12. Here, H- acts like a uniform opacity source in the optical
which turns off at around 1.6 $\micron$ coinciding with these data points.
Under equilibrium chemistry conditions, the abundances of H- retrieved in our
suite would only be possible at temperatures $\gtrsim$ 2500 $K$ (Kitzmann &
Heng, 2018). However, the production of appreciable quantities of H- could be
possible under disequilibrium processes. As described by Lewis et al. (2020),
enhanced e-, H and H2 mixing ratios (Lavvas et al., 2014) with production of
H- by H2 dissociative electron attachment and destruction by atomic H
collisional detachment, can enable mixing ratios of H- which are at the orders
of magnitude of the retrieved abundances, even for the temperatures expected
within the atmosphere of WASP-17b. The conditions required for these processes
are likely for hot Jupiters orbiting F-type stars such as WASP-17, making the
abundances of H- seen in our retrieval suite plausible outside of equilibrium
conditions. While the downturn at 1.6 $\micron$, which seems to drive the
inclusion of H-, could be explained by patchy clouds (e.g., Line & Parmentier,
2016; MacDonald & Madhusudhan, 2017), our explorations of the cloud
parameterisation found patchy clouds to be statistically disfavoured for the
measured transmission spectrum (see Section 6.1). Additional IR observations
around 1.6 $\micron$ which overlap with the existing G141 data would help to
corroborate the shape of the transmission spectrum in this region, shedding
light as to whether H- is indeed present within the atmosphere of WASP-17b.

####  6.2.4 Contextualising Molecular Detections

Table 10 lists the derived sigma significances (e.g., Benneke & Seager, 2013)
for each retrieval highlighted in Figure 12, obtained via Bayesian model
comparisons (see Trotta, 2008). We confirm the presence of H2O in WASP-17b’s
atmosphere (to $>7\,\sigma$ confidence) and report evidence of CO2 absorption
($>3\,\sigma$), and conclude that the detection of H2O and the inference of
CO2 is robust across all our considered retrievals. We also see tentative
evidence of CH4 in the A and C retrievals ($\sim 2\,\sigma$).

Our detection of H2O is largely driven by the precise WFC3/G141 data, as seen
for other hot Jupiters with similarly high-quality WFC3 spectra (e.g. Deming
et al., 2013; Wakeford et al., 2018). We also benefit from the extended
wavelength coverage provided by WFC3/G102, which allows us to probe multiple
H2O features, similarly seen in the atmospheres of planets such as HAT-P-26b
(Wakeford et al., 2017), WASP-39b (Wakeford et al., 2018) and WASP-107b (Spake
et al., 2018). Our evidence of CO2, meanwhile, arises from the large offset
between the two Spitzer IRAC photometric points. WASP-17b therefore joins
WASP-127b (Spake et al., 2021) as one of the only two exoplanets known to
exhibit evidence of CO2 absorption. Without the inclusion of optical data (see
Section 6.2.1) we see no change in the statistical significance of the
detected molecules, confirming that our detection of H2O and CO2 are driven by
the WFC3/IR and Spitzer data.

While the evidence of carbon species places WASP-17b amongst a rare collection
of atmospheres, the lack of IR wavelength coverage currently available limits
the properties which can inferred from this evidence, and in the case of
WASP-17b, such evidence is also statistically tentative. In the near future,
the GTO-1353 observations of WASP-17b with JWST will obtain the complete
transmission spectrum of the atmosphere across 0.6–14 $\micron$, filling in
the missing IR coverage. Such a transmission spectrum should be able to
confirm the tentative evidence of carbon species, and will likely obtain
significantly improved constraints on all atmospheric properties, however the
specific predictions are beyond the scope of this work and will be explored in
a future study.

Table 10: Sigma confidence levels of detections of H2O, CO2, and CH4 absorption features for the three POSEIDON retrievals presented in Figure 12. For full details on species considered in each retrieval, see Table 8. | A | B | C
---|---|---|---
H2O | 7.90 | 8.19 | 8.17
CO2 | 3.17 | 3.88 | 3.98
CH4 | 2.03 | <1.0 | 2.30

##  7 Conclusions

We have presented a consistent and comprehensive analysis of new HST WFC3/IR
and a reanalysis of existing HST STIS and Spitzer IRAC observations of the
atmosphere of WASP-17b. We take advantage of the implementation of WFC3’s
spatial scanning mode, along with the many advances in analysis techniques
developed in recent years to produce a precise transmission spectrum of
WASP-17b from 0.3–5 µm. With our spatial scan mode observations, we achieve
average uncertainties for WFC3/G141 of 120 ppm, a marked improvement over
previous observations of WASP-17b with this instrument. Across our entire
spectrum, we achieve an average uncertainty of 272 ppm. We also leverage the
long temporal baseline provided by TESS observations in order to refine the
orbital ephemerides of WASP-17b, resulting in a precise orbital period of
3.73548546 $\pm$ 0.00000027 d.

We interpret the measured atmosphere of WASP-17b with an extensive suite of
retrieval analyses under both free and equilibrium chemistry using the
`POSEIDON` and `ATMO` retrieval codes respectively. The data results in
bimodal solutions, with high and low metallicity modes plausible,
demonstrating the importance of utilising multiple statistics for model
selection.

We find that WASP-17b is best fit by an isothermal P-T profile, consistent
with a $\sim 1200\pm 200K$ limb temperature, and requires both a uniform cloud
deck and wavelength-dependent scattering aerosol prescription, with a super-
Rayleigh gradient. Stellar activity has no discernible impact on the observed
transmission spectrum, in agreement with prior observations of the host
WASP-17 (Sing et al., 2016; Khalafinejad et al., 2018).

We detect absorption due to H2O at $>7\,\sigma$ and find evidence of
absorption due to CO2 at $>3\,\sigma$. We find no evidence of absorption due
to Na i or K i in the atmosphere of WASP-17b, likely as a result of the poorer
precision achieved by HST STIS, which was negatively impacted by the partial
transits observed by this instrument. Observations of WASP-17b with WFC3/UVIS
which utilise the newly refined orbital period could enhance our understanding
of its atmosphere, as UVIS has been seen to be able to produce transmission
spectra from 0.2–0.8 µm with superior precision and fewer systematics compared
to STIS (Wakeford et al., 2020).

The retrieval with the best $\chi^{2}$ CDF results in bimodal posterior
distributions for a host of model parameters, and within our full suite of
free chemistry retrievals, a wide range of abundances for key species such as
H2O, CO2 and CH4 are obtained. Retrievals with and without the inclusion of
the STIS optical data indicate that the current available precision at optical
wavelengths prevents the retrieval from achieving precise constraints on the
abundances of optical species such as Na and K. The optical data improves the
constraints on the aerosol scattering properties towards bluer wavelengths, as
both the observed spectrum and our interpretation of the data are dependent on
these properties. This therefore highlights the need for panchromatic
observations when accurately interpreting the transmission spectra of
exoplanet atmospheres.

Our free chemistry retrieval results potentially imply the presence of
disequilibrium processes within the atmosphere of WASP-17b. However, given the
quality of the optical data, and the lack of coverage in the IR, our limited
ability to place constraints on the retrieved abundances of detected molecules
mean we can only infer rather than confirm this. A consistent sub-solar C/O
ratio is retrieved in all the free chemistry cases, although we are unable
place non-model dependent constraints on this due to the limited measurements
which probe carbon species.

Future planned observations with the James Webb Space Telescope as part of
GTO-1353 will characterise the transmission spectrum of WASP-17b from 0.6–14
$\micron$. This additional wavelength coverage, higher resolution and
precision will dramatically improve constraints on the carbon inventory of the
atmosphere. When combined with the existing transmission spectrum and
understanding of the cloud opacity presented in this work, future JWST
observations should be able to distinguish between the degenerate solution we
currently obtain, and will enable constraints to be placed upon key
atmospheric chemical species that will help to trace the formation and
evolution of WASP-17b and the processes shaping its atmosphere today.

## Acknowledgements

We thank the anonymous referee for their helpful suggestions and comments. We
also thank T.J. Wilson for useful discussions on the statistics of retrieval
comparison. This work is based on observations made with the NASA/ESA Hubble
Space Telescope, HST-GO-14918 and GO-12473, that were obtained at the Space
Telescope Science Institute, which is operated by the Association of
Universities for Research in Astronomy, Inc. We thank J-M Desert for use of
data from their Spitzer program 90092. L. Alderson acknowledges funding from
STFC grant ST/W507337/1 and from the University of Bristol School of Physics
PhD Scholarship Fund. N.K.L and R.J.M acknowledge support from NASA grant
80NSSC20K0586 issued through the James Webb Space Telescope (JWST) Guaranteed
Time Observer Program to the JWST Telescope Scientist Team.

Author contributions: L. Alderson led the data analysis, interpretation, and
write-up of this study. H.R. Wakeford conducted the STIS analysis. E. M. May
conducted the analysis of the Spitzer IRAC data. L. Alderson and D. Grant
conducted the analysis on the TESS data and fitting of the orbital period with
insight from K. Stevenson. L. Alderson, R. MacDonald, H.R. Wakeford, and D.K.
Sing conducted the retrieval analysis. L. Alderson and J. Fowler developed the
data extraction pipeline. J. Goyal consulted on the use of forward models used
in this study. H.R. Wakeford conceived and proposed the study along with, N.K.
Lewis, D.K. Sing, N.E. Batalha, M. Clampin and T. Kataria. H.R. Wakeford and
N.K. Lewis advised throughout the study. All authors read and approved the
manuscript.

## Data Availability

All HST and TESS data are available through MAST666exo.MAST.STScI/WASP-17b.
WFC3 G102 and G141 data can be found on MAST as part of HST GO-14918 (PI. H.R.
Wakeford) visits 1 and 32. Please note the pointing failed during visit 2 of
this program resulting in unusable data. STIS/G430L and G750L data can be
found on MAST as part of GO-12473 (PI. D.K. Sing) visits 5, 6, and 19. WASP-17
was measured with TESS in sector 12 and 38 with corrected light curves
available through MAST. Spitzer IRAC data is part of program 90092 (PI J-M,
Desert) and can be found through IPAC.

## Software Used

This analysis made use of components of the IDL Astronomy Users Library
(Landsman, 1995) and the Python packages: NumPy (Oliphant, 2006), SciPy
(Virtanen et al., 2019), MatPlotLib (Caswell et al., 2019), AstroPy (Astropy
Collaboration et al., 2018), and Photutils (Bradley et al., 2019), Batman
(Kreidberg, 2015), exoctk (Bourque et al., 2021), lightkurve (Lightkurve
Collaboration et al., 2018), emcee (Foreman-Mackey et al., 2013), celerite
(Foreman-Mackey et al., 2017; Foreman-Mackey, 2018), PyMultinest (Buchner et
al., 2014b). This research made use of ExoTiC-ISM (Laginja & Wakeford, 2020),
a software package for marginalised transit parameters, which was developed
based on the work by Wakeford et al. (2016).

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##  Appendix A Additional Light Curve Figures

In this appendix we show the individual light curves from each of our datasets
and the associated spectroscopic channels.

Figure 14: Transit light curves of WASP-17b obtained by TESS. Top: Raw light
curves for Sector 12 (left) and Sector 38 (right). The unbinned flux is shown
by the black data points while the binned flux is shown in blue. The
systematic model is given by the black line. Middle: Detrended light curves
for Sector 12 (left) and Sector 38 (right). The unbinned flux is shown by the
black data points while the binned flux is shown in blue. The transit model is
given by the black line. Bottom: Phase folded transit light curve of WASP-17b
for both Sectors 12 and 38 along with residuals. Figure 15: Spectroscopic
light curves of WASP-17b from WFC3/G102 visit 32. Left: Raw light curves.
Middle: Corrected light curves and best fit model obtained with an ExoTiC-ISM
routine. Right: Corrected light curve residuals. The central wavelength and
standard deviation of the residuals for each wavelength bin are shown below
the corresponding residuals. Figure 16: Spectroscopic light curves of WASP-17b
from WFC3/G141 visit 1. For details see Figure 15.

Figure 17: Spectroscopic light curves of WASP-17b from STIS/G430L visit 5
(left) and visit 6 (right). For details see Figure 15. Figure 18:
Spectroscopic light curves of WASP-17b from STIS/G750L visit 19. For details
see Figure 15.