Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µ m Kristin Sotzen, Kevin Stevenson, David Sing, Brian Kilpatrick, Hannah Wakeford, Joseph Filippazzo, Nikole Lewis, Sarah Hörst, Mercedes López-Morales, Gregory Henry, et al.

To cite this version:

Kristin Sotzen, Kevin Stevenson, David Sing, Brian Kilpatrick, Hannah Wakeford, et al.. Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µ m. Astronomical Journal, American Astronomical Society, 2020, 159 (1), pp.5. ￿10.3847/1538-3881/ab5442￿. ￿hal-03038422￿

HAL Id: hal-03038422 https://hal.archives-ouvertes.fr/hal-03038422 Submitted on 3 Dec 2020

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Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µm

Kristin S. Sotzen,1, 2 Kevin B. Stevenson,3, 4 David K. Sing,1 Brian M. Kilpatrick,4 Hannah R. Wakeford,4 Joseph C. Filippazzo,4 Nikole K. Lewis,5 Sarah M. Horst,¨ 1, 4 Mercedes Lopez-Morales,´ 6 Gregory W. Henry,7 Lars A. Buchhave,8 David Ehrenreich,9 Jonathan D. Fraine,10 Antonio Garc´ıa Munoz,˜ 11 Rahul Jayaraman,12 Panayotis Lavvas,13 Alain Lecavelier des Etangs,14 Mark S. Marley,15 Nikolay Nikolov,1 Alexander D. Rathcke,8 and Jorge Sanz-Forcada16

1Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA 2 JHU Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD 20723, USA 3 JHU Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD 20723 USA 4Space Telescope Science Institute, 3700 San Martin Dr, Baltimore, MD 21218, USA 5Department of Astronomy and Carl Sagan Institute, Cornell University, 122 Sciences Drive, Ithaca, NY 14853, USA 6Center for Astrophysics | Harvard & Smithsonian, 60 Garden St, Cambridge, Cambridge, MA 02138, USA 7Center for Excellence in Information Systems, Tennessee State University, Nashville, TN 37209, USA 8DTU Space, National Space Institute, Technical University of Denmark, Elektrovej 328, DK-2800 Kgs. Lyngby, Denmark 9Observatoire de lUniversit´ede Gen`eve, 51 chemin des Maillettes, 1290 Sauverny, Switzerland 10Space Science Institute, 4750 Walnut St #205, Boulder, CO 80301, USA 11Zentrum f¨urAstronomie und Astrophysik, Technische Universit¨atBerlin, EW 801, Hardenbergstrasse 36, D-10623 Berlin, Germany 12Brown University, Department of Physics, Box 1843, Providence, RI 02904, USA 13Groupe de Spectrom´etrieMoleculaire et Atmosph´erique,Universit´ede Reims Champagne Ardenne, Reims, France 14Institut d’astrophysique de Paris, UMR7095 CNRS, Sorbonne Universit´e,98bis Boulevard Arago, 75014 Paris, France 15NASA Ames Research Center, MS 245-3, Moffett Field, CA 94035, USA 16Centro de Astrobiolog´ıa(CSIC-INTA), E-28692 Villanueva de la Ca˜nada,Madrid, Spain

(Received 3 September 2019; Revised 31 October 2019; Accepted 3 November 2019)

ABSTRACT As part of the PanCET program, we have conducted a spectroscopic study of WASP-79b, an inflated hot Jupiter orbiting an F-type in with a period of 3.66 days. Building on the original WASP and TRAPPIST photometry of Smalley et al.(2012), we examine HST/WFC3 (1.125 - 1.650 µm), Magellan/LDSS-3C (0.6 - 1 µm) data, and Spitzer data (3.6 and 4.5 µm). Using data from all three instruments, we constrain the water abundance to be –2.20 ≤ log(H2O) ≤ –1.55. We present these results along with the results of an atmospheric retrieval analysis, which favor inclusion of FeH and H- in the atmospheric model. We also provide an updated ephemeris based on the Smalley, HST/WFC3, LDSS-3C, Spitzer, and TESS transit times. With the detectable water feature and its occupation of the clear/cloudy transition region of the temperature/gravity phase space, WASP-79b is a target of interest for the approved JWST Director’s Discretionary Early Release Science (DD ERS) program, with ERS observations planned to be the first to execute in Cycle 1. Transiting have been approved for 78.1 hours of data collection, and with the delay in the JWST launch, WASP-79b is now arXiv:1911.02051v1 [astro-ph.EP] 5 Nov 2019 a target for the Panchromatic Transmission program. This program will observe WASP-79b for 42 hours in 4 different instrument modes, providing substantially more data by which to investigate this hot Jupiter.

Keywords: methods — observational: atmospheres — planets and satellies: individual — WASP-79b

1. INTRODUCTION

Corresponding author: Kristin Showalter Sotzen Based on studies of planets and moons within the solar [email protected], [email protected] system and spectral analyses of exoplanets, a persistent atmosphere is generally accompanied by clouds and/or 2

hazes. Recent studies of hot Jupiters have revealed that estimate of approximately one MJup and such a large ra- many of the exoplanets observed in transmission have dius estimate, WASP-79b’s density is comparatively low, cloudy or hazy properties, with their spectra dominated implying that its atmosphere is extended. In addition, by strong optical Rayleigh and/or Mie scattering from the host star WASP-79 is a bright, quiet F-type star with high-altitude aerosol particles (e.g., Sing et al.(2016); consistent stellar activity, with variation in the baseline Stevenson et al.(2016a); Wakeford & Sing(2016); Lav- stellar flux within 0.1% (Section 2.3.4). vas & Koskinen(2017)). Clouds and hazes in exoplan- WASP-79b has a Teq ∼1800 K and a log g between 2.67 etary atmospheres can have a significant impact on the and 2.85 (Smalley et al. 2012), placing this planet in a detectable spectra for these worlds. In the optical range, transition region of the temperature/gravity phase space. small particles produce scattering that leads to steep On one side of this transition region, planets have been slopes that progressively become shallower as the parti- found to have muted water features due to clouds and cle radius increases (see e.g., Lavvas & Koskinen(2017)). hazes, while on the other side, planets have been found This scattering effectively dampens any features from the to have strong measured water features, implying clearer deeper atmosphere, including pressure-broadened alkali atmospheres (Stevenson 2016). Being in this transition Na and K lines, and can mute or obscure expected water region, WASP-79b provided an opportunity to further absorption features in the near-infrared (see e.g., Wake- study this relationship between temperature, gravity, and ford & Sing(2016)). the presence of atmospheric clouds and/or hazes. These The majority of current spectra are con- studies are important for predictions of atmospheric fea- structed from wavelengths in the optical and near- ture obscuration, which inform target selection and ob- infrared wavelengths, revealing information on the por- servations for telescopes like the Hubble Space Telescope tion of transmission spectra for aerosols where only scat- (HST). tering features are seen. When interpreting these ob- Additionally, with its broad observing windows (Bean servations, the slope of spectra in the optical regime is et al. 2018), WASP-79b presented an excellent candidate proportional to the temperature of the atmosphere and for a transmission spectroscopy study as well as a poten- can be indicative of specific species when small grain sizes tial Early Release Science (ERS) candidate for the James are considered (Wakeford & Sing 2016). Additionally, ab- Webb Space Telescope (JWST). It was therefore sched- sorption features in the near- and mid-infrared spectra uled for follow-up observations using HST, the Magellan can be identified as the vibrational modes of the major Large Dispersion Survey Spectrograph 3 (LDSS3), and bond pairs in certain potential condensates, providing the Spitzer Space Telescope to determine its value as a composition information (Wakeford & Sing 2016). candidate for JWST observation, with broad wavelength The survey analysis performed by Sing et al.(2016) coverage to evaluate its value as an ERS candidate. of ten hot Jupiters found that planets with predomi- In Sections 2.1, 2.2, 2.3, and 2.4, we describe obser- nantly clear atmospheres show prominent alkali and H2O vations, analysis methods, and results from TESS, HST, absorption, with infrared radii values commensurate or LDSS3, and Spitzer respectively. In Section3, we dis- higher than the optical altitudes, while heavily hazy and cuss the atmospheric retrieval analysis and expectations cloudy planets have strong optical scattering slopes, nar- for JWST observations, and in Section4, we present our row alkali lines, and H2O absorption that is partially or conclusions. completely obscured. Like many transiting exoplanets found using ground- 2. OBSERVATIONS based surveys, WASP-79b is a hot Jupiter with an ex- 2.1. TESS Data tended atmosphere. Discovered in 2012 by Smalley et The Transiting Exoplanet Survey Satellite (TESS) ob- al using photometry from the WASP-South and TRAP- served 12 transits of WASP-79b in January and February PIST telescopes, it was found to have a planetary mass of 2019. TESS provides data in the 0.6 - 1.0 µm band, of 0.90 ± 0.08 M and a large radius estimate, ranging Jup and the TESS light curve contains data covering 12 tran- from 1.7 ± 0.11 R using a main-sequence mass-radius Jup sits in Sectors 4 and 5. We fit the TESS WASP-79b constraint on the Markov Chain Monte Carlo (MCMC) 2-minute cadence transits using the Presearch Data Con- process, to 2.1 ± 0.14 R using a non-main sequence Jup ditioning (PDC) light curve, which has been corrected for constraint (Smalley et al. 2012). While both radius es- effects such as non-astrophysical variability and crowding timates were large for the available hot Jupiter data in (Jenkins et al. 2016). From the timeseries, we removed 2012, the estimate based on the non-main sequence con- all of the points which were flagged with anomalies. The straint would have made WASP-79b the largest exoplanet Barycentric TESS Julian Dates (BTJD) were converted discovered at the time (Smalley et al. 2012). With a mass to BJDTDB by adding 2,457,000 days. For each transit in Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µm , K.S. Sotzen et al 3

G141 GRISM to acquire spectra from 1.1 to 1.7 µm over 5 HST orbits, during which we collected 65 science frames using 138-second integrations. We provide an overview of the data analysis process below, and a detailed de- scription of the process can be found in Stevenson et al. (2014).

2.2.2. Reduction, Extraction, and Calibration of Spectra The Transit Reduction, Extraction, and Calibration Software (T-RECS) pipeline produces multi-wavelength, systematics-corrected light curves from which we derive wavelength-dependent transit depths with uncertainties (Stevenson et al. 2014). The bias correction is performed using a series of bias frames stacked to form a single Figure 1. Transit depth estimates for the 12 transits of master bias frame that is applied uniformly to all of WASP-79b available from TESS. Estimates are shown with the science frames. We extract a pixel window centered 1σ uncertainties. The red lines indicate the weighted mean of on the spectrum that includes pixels along the spatial the transit depths with 1σ uncertainties. direction that are used in the optimal spectral extrac- tion as well as in the background subtraction (Stevenson the light curve, we extracted a 0.5 day window centered et al. 2014). We modeled the spectroscopic flat field us- around the transits and fit each transit event individu- ing the coefficients provided in the updated flat field file ally. We fit the data using the 4-parameter non-linear sedF F cube − both.fits. limb-darkened transit model of Mandel & Agol(2002) Because the background for HST is consistent over and included a linear baseline time trend. We calculated time, areas outside of the spectrum can be used to in- the limb-darkening coefficients as in Sing(2010) using a terpolate the background values for the region within the Kurucz stellar model finding coefficients of c = 0.5012, 1 spectrum by computing the median of each column. We c = 0.2630, c = −0.1034, and c = −0.0301. For each of 2 3 4 perform 5σ outlier detection by stacking the images in the 12 transits, we fit for six free parameters consisting of time and evaluating each pixel along the time axis for the central transit time, planet-to-star radius ratio, linear outliers. To account for imprecision in the instrument baseline, cosi, and a/R∗. The high-quality of the TESS pointing during data collection, each spectrum is cross- transit light curves places tight constraints on the sys- correlated with the first spectrum to measure and correct tem parameters, and we find a weighted-average inclina- for the pointing drift over time (Stevenson et al. 2014). tion of i=85.929±0.174 degrees and a/R∗=7.292±0.080. These planetary parameters were used as fixed values in 2.2.3. White Light Curve Fits the HST, LDSS3, and Spitzer analyses. Fixing the sys- tem parameters with these values for use in the trans- The raw transit light curves for WASP-79b exhibit mission spectra, we find a weighted-average value of ramp-like systematics comparable to those seen in previ- ous WFC3 data. Following standard procedure for HST Rpl(TESS)/Rstar = 0.10675 ± 0.00014, which is in good agreement with the HST, Spitzer, and LDSS values. transit light curves, we did not include data from the first orbit in our analysis (Kreidberg et al. 2014). We corrected 2.2. HST/WFC3 Observations and Data Analysis for systematics in the remaining orbits by modeling the systematics as a function of time, which includes an ex- 2.2.1. Observations ponential ramp term fitted to each orbit, a linear trend We analyzed WASP-79b WFC3 data from the Panchro- term, and a quadratic term for limb-darkening. matic Exoplanet Treasury (PanCET) program (HST GO- We modeled the band-integrated light curve in order 14767, P.I.s Sing & L´opez-Morales). During its primary to identify and remove systematics, most of which are transit in March of 2017, HST observed WASP-79b in wavelength-independent with WFC3, and to establish spatial scan mode, which slews the telescope during the the absolute transit depth when comparing transmission exposure and moves the spectrum perpendicularly to the spectra from different instruments using non-overlapping dispersion direction on the detector (Kreidberg et al. wavelengths (Stevenson et al. 2014). We created this 2014). This mode allows for longer integration times “white” light curve (WLC) by summing the flux values by distributing the incoming energy over multiple pixels. over the entire wavelength range. We used the Bayesian The Wide Field Camera 3 (WFC3) instrument utilized its Information Criterion (BIC) to select the best systemat- 4

250 White White 0 1.0000 250 1.125-1.160 500 3 2 1 0 1 2 0 1.125-1.160 1.160-1.195 500 1.195-1.230 0 1.160-1.195 0.9900 500 1.230-1.265 500 1.195-1.230 1.265-1.300 0 500 1.300-1.335 500 0 1.230-1.265 0.9800 1.335-1.370 500 1.370-1.405 0 1.265-1.300 500 1.405-1.440 500 1.300-1.335 1.440-1.475 0 0.9700 500 1.475-1.510 500 0 1.335-1.370 1.510-1.545 500 Normalized Flux 500 1.545-1.580 0 1.370-1.405 0.9600 500 1.580-1.615 500 0 1.405-1.440 1.615-1.650 500 Residuals (ppm) 500 0 1.440-1.475 0.9500 500 500 0 1.475-1.510 500 500 0 1.510-1.545 0.9400 500 3 2 1 0 1 2 3 4 500 0 1.545-1.580 Time from Measured Transit Center (hrs) 500 500 0 1.580-1.615 Figure 2. WASP-79b white and spectroscopic light curves 500 500 0 1.615-1.650 extracted from the HST/WFC3 data using the process de- 500 scribed in Stevenson et al.(2014). The results are binned, 3 2 1 0 1 2 normalized to the system flux, and vertically shifted for ease Time from Measured Transit Center (hrs) of comparison. The error bars represent 1σ uncertainties. The Figure 3. White and spectroscopic residuals for light curves black lines show the best-fit models, and the wavelength range extracted from the HST/WFC3 data. Values represent 1σ for each of the 15 channels is specified in µm (Stevenson et al. residuals. 2014). sufficient resolution to reveal features of interest while ics model component, and our final analytic model for the maintaining sufficient signal to noise in each bin. HST/WFC3 data takes the form: To construct a spectrum, we are interested only in the relative transit depths of the different wavelength F (t) = F sT (t)L(t)H(t) (1) bins. We can therefore estimate uncertainties with our Differential Evolution Markov Chain Monte Carlo (DE- where F (t) is the measured flux at time t; F is the out- s MCMC) algorithm, assuming fixed parameters for a/R* of-transit system flux; T (t) is the primary-transit model and cosi (Stevenson et al. 2014). For the HST, LDSS-3C component with unity out-of-transit flux (Mandel & Agol (Section 2.3), and Spitzer (Section 2.4), we assumed a 2002); L(t) = a(t − t ) + 1 is the time-dependent linear 0 fixed a/R* of 7.2900 and a cosi of 0.070993, based on an model component with a fixed offset, t , and free param- 0 analysis of the TESS data for WASP-79b (Section 2.1). eter, a; and H(t) = 1 − exp(−a × P + b) + c × P fits the The transit midpoint was carried as a free parameter and HST “hook” using a rising exponential with free param- estimated in the WLC analyses and then fixed for the eters a, b, where c, and P represents the number of HST spectroscopic analyses, as it is wavelength-independent. orbits since the beginning of the transit. The white light Figure2 shows results for the HST white light curve ex- curve extraction for the HST/WFC3 data resulted in a traction as well as results for the 15 wavelength bins from transit depth of 1.1282% ± 0.0032% (see Figure2). the spectroscopic light curve extraction. 2.2.4. Light Curve Fits The results of the HST/WFC3 analysis, which indicate the presence of water in WASP-79b’s atmosphere, are We use the Divide-White method described by Steven- discussed in later sections. son et al.(2014) to model the wavelength-dependent (i.e., spectroscopic) light curves, without making any prior as- sumptions about the form of the systematics, by utilizing 2.3. LDSS-3C Observations and Data Analysis information within the wavelength-independent (white) In order to obtain a more complete picture of WASP- light curves. This can be done for an arbitrary number 79b’s atmospheric structure and to assess the slope (if of wavelength bins, though ten to fifteen bins provides any) of the spectrum, we extended our analysis for this Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µm , K.S. Sotzen et al 5

planet to the visible and near-infrared using data from the transit that was caused by the telescope rotating as the Low Dispersion Survey Spectrograph (LDSS) optical it passed through zenith (Figure4). For the systemat- imaging spectrograph on the 6.5 m Magellan II (Clay) ics component, we tested various combinations of linear Telescope at Las Campanas Observatory (LCO) in Chile. and quadratic models in combination with rotation and We used the LDSS-3C VPH-red grism (bandpass 0.6 - 1.0 intra-pixel functions to account for the aforementioned µm), which extended our spectral analysis of WASP-79b rotation and pixel shift to determine which combination into the visible wavelengths where we expected to en- of models provided the best fit, based on the BIC and χ2 counter the effects of Rayleigh scattering due to aerosols. values. Our final analytical model takes the form: Our reduction, calibration, white light curve fitting, and spectroscopic light curve fitting processes use the F (t) = F sT (t)R(t)Q(t)I(t) (2) T-RECS analysis pipeline and match the processes de- where F (t) is the measured flux at time t; F s is the out- scribed in detail in Stevenson et al.(2016a). We will of-transit system flux; T (t) is the primary-transit model therefore only discuss details pertaining to this specific component with unity out-of-transit flux; R(t) = 1+aA+ observation set. bcos(π/180×(θ(t)+θ0)) is the time-dependent instrument 2.3.1. Observations, Reduction, and Calibration rotation model component with free parameters a, b, and θ , where A = airmass; Q(t) uses a quadratic polynomial We observed the primary transit of WASP-79b on the 0 to fit a pixel response ramp in the data; and I(y) fits night of 2016 Dec 20 for nearly 8 hours (00:31 - 08:14 UT, the pixel shift using a linear function in the dispersion airmass = (1.1 – 1.0 – 1.8) (4)), collecting 1230 science direction. The white light curve for the Dec 2016 LDSS- frames using 7-second integrations. We utilized LDSS- 3C data resulted in a transit depth of 1.1626% ± 0.0152%. 3C’s turbo read mode with low gain and applied 2x2 pixel binning to minimize readout times, overall achiev- 2.3.3. Light Curve Fits ing a duty cycle of 31%. The most recent upgrade of As with the HST/WFC3 data, we apply the Divide − the instrument to LDSS-3C constituted an upgrade to a W hite technique (Stevenson et al. 2014) to remove the deep-well detector that eliminated the fringing issues seen wavelength-independent systematics. To account for the previously (Stevenson et al. 2016b). wavelength-dependent systematics, each spectroscopic Our science masks utilized three, 12”-wide slits for ob- channel requires a rotation correction with airmass, a servations of our target star (WASP-79, V = 10.1) and the quadratic function in time, and an intra-pixel response two comparison (V = 10.8, 12.7). The brighter com- shift correction. Due to unfavorable weather effects dur- parison star is a G dwarf star with a T eff of 5834 K. The ing the night of the LDSS-3C observation, the displaced spectra from the dimmer comparison star were too noisy reference star, and the telescope rotation, we found the to provide reliable atmospheric corrections, so we relied data to be very noisy with significant numbers of outliers strictly on the brighter reference star. Unfortunately, the in most channels. To remove these outliers, we performed brighter reference star was sufficiently displaced from the the following iterative outlier rejection process: target star on the detector (146.7 arcsec) that the result- ing atmospheric corrections are not necessarily consistent. 1. We ran the simulation with no masking or out- This results in relatively large error bars on the transit lier rejection so that we could visually determine depth estimates for the LDSS-3C data. whether there were any sections of the data that should be removed entirely. Based on the results of 2.3.2. White Light Curve Fits this run and the weather information for the obser- As described in Stevenson et al.(2016a), we correct vation timeframe, we removed times 02:16:14 UT - for the observed flux variations caused by fluctuations 02:47:25 UT and times 03:24:58 UT - 04:11:56 UT in Earth’s atmosphere by dividing the WASP-79b light for all channels. Additionally, based on the normal- curve by the comparison star. We start by fitting the ized flux values (see Figure5), the 6540-6590 and white light curve (0.625 - 1.025 µm) to maximize the 7570-7700 channels were masked to remove them signal-to-noise ratio (SNR), using both transit and sys- from the light curve analysis, as they showed at- tematics model components. The first utilizes a Mandel mospheric absorption that could not be accounted & Agol(2002) transit model with selected free parameters for using the reference star, which was artificially in- and fixed quadratic limb-darkening parameters derived creasing the transit depths in those channels. There from stellar Kurucz models (Castelli & Kurucz 2004) as- were significant changes in the local humidity over suming a stellar temperature of 6500 K and log g of 4.2. the course of the night, particularly between ∼05:00 We found early in the analysis that there was a shift of UTC and ∼08:00 UTC that may have contributed the illuminated pixels on the detector in the middle of to the noise in the data. 6

10250 1.04 50 25 9750 1.03 0 9250 1.02 25

50 8750 1.01 75 8250 1.00 100 Cassegrain Position Rotator Angle (deg) 125 (a) 7700 0.99 7570 150 Normalized Flux 3 2 1 0 1 2 3 4 7250 0.98 Time from Measured Transit Center (hrs) Wavelength (Angstroms) 1.0 6750 0.97

1.2 6250 0.96 0 200 400 600 800 1000 Frame Number 1.4

Airmass 1.6 Figure 5. Two-dimensional light curve for the Dec 2016 LDSS-3C WASP-79b observations showing the flux of the tar- 1.8 (b) get star normalized against the flux of the reference star. Nor- malized flux is shown per wavelength as a function of frame 3 2 1 0 1 2 3 4 Time from Measured Transit Center (hrs) number. The 6540-6590, 7570-7700, and 9250-9750 channels show increased absorption, particularly early on in the obser- 1.355 vation. The 6540-6590 and 7570-7700 channels were masked 1.350 to remove their influence from the light curve extraction. The 1.345 solid vertical lines indicate the times for which data was re- 1.340 moved based on visual inspection as described in Step 1 of the iterative outlier rejection process. 1.335

1.330

Relative Flux (WLC) (c) shows the calculated spectrum drift and stretch over time 1.325 3 2 1 0 1 2 3 4 for both the target and reference stars, and it can be seen Time from Measured Transit Center (hrs) that the spectral drift was in excess of 1 pixel for both Figure 4. (a) The Cassegrain Position Rotator Angle as a the target and reference stars. function of time for the white light curve (WLC) transit ex- The results of the spectroscopic light curve extraction traction. Note that the telescope passed through zenith, as for the LDSS-3C data are shown in Figure7. Due to indicated by both the telescope position and the airmass (b). the large amount of noise in the data, we restricted the This resulted in a shift of the illumination on the detector and spectroscopic LDSS-3C analysis to 8 channels to increase an associated shift in the relative flux between the target and reference stars, as shown in (c). the SNR.

2. We then ran two consecutive boxcar median masks 2.3.4. Results with 3σ rejection on the photon flux data. Because the opacity of the exoplanet atmosphere varies 3. We re-ran the simulation on the results from step 2, with wavelength, the apparent size of the planet, and and ran three consecutive 3σ outlier rejection masks therefore the depth of the transit, also varies with wave- on the residuals for the resulting transit models. length. Having performed the spectroscopic light curve extraction and the systematics normalization via the 4. Finally, we re-ran the simulation on the results from Divide-White method, we can construct a spectrum from step 3, masking the outliers identified in steps 2 and the relative transit depths of the selected wavelength bins. 3. Figure9 shows the relative transit depths of the WASP- 79b HST data for 15 wavelength bins for the light curve In addition to the expected drift in the dispersion di- extraction using the Divide-White normalization method. rection of the LDSS-3C spectrum over the course of the In this figure, the positive y-axis represents increasing observation, Diamond-Lowe et al.(2018) found a stretch- transit depth, i.e., more absorption by the WASP-79b ing of the spectrum equal to approximately 4 pixels for atmosphere. The resulting spectrum displays a noticeable the target star and 2 pixels for the comparison star. To peak centered at 1.4 µm, which represents a water feature. account for this effect, we calculated the stretch and the This feature is consistent with water features found in the drift by optimizing a cubic spline fit of the target spec- spectra of other hot Jupiters (Sing et al. 2016), and an trum normalized to the reference spectrum. Figure6 atmospheric retrieval corroborates this feature. Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µm , K.S. Sotzen et al 7

Spectrum Drift: Target Star

1 Pixels 0

0 200 400 600 800 1000 Spectrum Drift: Reference Star 2

1

Pixels 0

0 200 400 600 800 1000 Spectrum Stretch: Target Star 1.001

1.000 Pixels

0.999 0 200 400 600 800 1000 Spectrum Stretch: Reference Star 1.0005

1.0000

Pixels 0.9995

0.9990 0 200 400 600 800 1000 Frame Number

Figure 6. Spectral drift in the dispersion direction and spectral stretch over the course of the observation for the target and reference stars. The drift was in excess of 1 pixel for both the target and reference stars, while the stretch was 4 pixels for the target star over the course of the observation.

0.0025 0.0000 White 1.0000 White 0.0025 0.002 0.625-0.675 0.000 0.625-0.675

0.9800 0.002 0.675-0.725 0.002

0.000 0.675-0.725 0.725-0.757 0.002 0.9600 0.002

0.770-0.825 0.000 0.725-0.757

0.002 0.9400 0.825-0.875 0.002 0.000 0.770-0.825

Normalized Flux 0.875-0.925 0.002 0.002

0.9200 Residuals (ppm) 0.925-0.975 0.000 0.825-0.875

0.002 0.975-1.025 0.002 0.9000 0.000 0.875-0.925

0.002 0.002

0.000 0.925-0.975 2 0 2 4 Time from Measured Transit Center (hrs) 0.002 0.002 Figure 7. WASP-79b white and spectroscopic light curves 0.000 0.975-1.025 0.002 extracted from December 2016 LDSS-3C data using the fitting 3 2 1 0 1 2 3 process described in Stevenson et al.(2016a). As with the Time from Measured Transit Center (hrs) WFC3 data, the results are binned and normalized to the system flux, and the error bars represent 1σ uncertainties. Figure 8. White and spectroscopic residuals for the light The black lines show the best-fit models, and the wavelength curves extracted from the LDSS-3C data. Values represent range for each of the 8 channels is specified in µm (Stevenson 1σ residuals. The gaps in the spectroscopic plots indicate et al. 2016a). The grey points represent the original data, and times for which noisy observation data were masked. the colored points represent the data that were retained from the noise and outlier masking process. light curve extraction using the Divide-White normaliza- tion method. It should be noted that the transit depth Figure 10 shows the relative transit depths of the estimate for the 0.65 µm channels is likely somewhat low WASP-79b LDSS-3C data for 8 wavelength bins for the due to detector cutoff at the blue edge. Rackham et al. 8

1.17 1.836 1.250 7.65

1.225 6.12 1.16 1.224 1.200 4.59 1.15 0.612 1.175 3.06 White Transit Depth 1.14 0.000 1.150 1.53 TESS Transit Depth 1.13 0.612 1.125 0.00

1.100 1.53 1.12 1.224 Scale Height Scale Height

Transit Depth (%) Transit Depth (%) 1.075 3.06 1.11 1.836 1.050 4.59

1.10 2.448 1.025 6.12 1.1 1.2 1.3 1.4 1.5 1.6 1.7 0.6 0.7 0.8 0.9 1.0 1.1 Wavelength ( m) Wavelength ( m)

Figure 9. Spectrum constructed from transit depths of 15 Figure 10. Spectrum constructed from transit depths of 8 wavelength bins of HST/WFC3 data. Inversion of the transit wavelength bins of LDSS-3C data. The large spread in transit depth provides a representation of the relative absorption at depth estimates - particularly noticeable at 0.9 and 0.95 µm different wavelengths. The increased absorption at 1.4 µm - is likely due to interference from Earth’s atmosphere that indicates a water absorption feature. The horizontal error bars could not be fully accounted for due to the distance of the indicate the wavelength bins used for the light curve analysis. reference star from the target star. The 0.65 µm point may be low due to detector cutoff at the blue edge. The transit (2017) also found decreased transit depths at bluer wave- depth estimates for the white light curve analysis described in 2.3.2 and for the TESS analysis (Section 2.1) are provided for lengths for GJ 1214b, a sub-Neptune orbiting a M4.5 comparison. dwarf star, which they attribute to the presence of faculae on the unocculted stellar disk. However, observations of depth estimates are multiplied by the maximum corre- WASP-79 indicate that its stellar activity is low. We col- lated noise factor for each light curve. lected XMM-Newton observations of WASP-79 on 2017- Transit data for WASP-79b from the HST Space Tele- 07-18, with S/N=3.4. Its X-ray emission, L = 5.7×1028 X scope Imaging Spectrograph (STIS) instrument are cur- erg/s (for a d=248 pc, c.f. GAIA DR2) yields a ratio log rently being analyzed. STIS provides data from 0.3 – L /L = −5.5, indicating a low activity level, as ex- X bol 1.0 µm, and these data should have smaller uncertainties pected for an early F star (Sanz-Forcada et al. in prep.). than the LDSS-3C data, providing more insight into the The TESS data baseline varies within 1σ < 0.1%, so these atmospheric structure of this hot Jupiter. data do not show evidence of short-term stellar activity variations in WASP-79. Furthermore, photometric ob- 2.4. Spitzer Data servations of WASP-79 with the Tennessee State Univer- 2.4.1. Observations sity C14 Automated Imaging Telescope (AIT) at Fairborn Observatory (see, e.g., Sing et al.(2015) for a description The observations analyzed here are part of Program ID of AIT operations) show no significant brightness vari- 13044 (PI: Drake Deming). The target was observed dur- ability within the 2017, 2018, and 2019. Nor does ing transit with IRAC channel 1 (3.6 µm) and channel 2 the AIT see significant variability from to year over (4.5 µm) (Fazio et al. 2004). The Astronomical Observing the same interval to a limit of ∼0.005 mag, confirming the Requests (AOR) are 62173184 and 62173696 for channels absence of longer-term activity variations. The photo- 1 and 2 respectively. All of these observations were car- metric stability of WASP-79 suggests that the decreased ried out in sub-array mode (32 × 32 pixels, 39” × 39”) transit depth at shorter wavelengths is not likely to be with a 30 minute peak-up observation preceding them. due to inhomogeneities in the stellar . Given The use of a peak up observation allows the instrument the low resolution of the LDSS it is not obvious what is to stabilize the image on the detector ‘sweet spot’ and causing the positive slope in the spectrum at bluer wave- decreases the likelihood of a ramp in the data (Ingalls lengths. et al. 2012). The frame time for both observations was 2 As discussed in Section 2.3.1, the atmospheric correc- seconds. tions likely do not fully account for the atmospheric dy- namics during the observation, and the very deep transit 2.4.2. Methods depth at 0.95 µm is likely exaggerated by interference For each AOR we began with Basic Calibrated Data from H2O in Earth’s atmosphere. To account for red (BCD) available on the Spitzer Heritage Archive. Each noise in the data, the uncertainties in the LDSS transit BCD file contains a cube of 64 frames of 64 × 64 pix- els. Each set of 64 images comes as a single FITS file Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µm , K.S. Sotzen et al 9

with a time stamp corresponding to the start of the first Smalley image. We determine the time of each frame in the set 6 LDSS by adding the appropriate multiple of the frame time to HST 5 the time stamp of the first image. The photometric ex- Spitzer traction was performed following the methods detailed in 4 TESS

Kilpatrick et al.(2017) and Kilpatrick et al.(2019) uti- 3 lizing both fixed and variable apertures across a range of sizes. Background subtraction and determination of the 2 stellar centroid and noise pixel parameter were performed 1 in each case. 0 Each transit fit was based on the model of Mandel & Agol(2002) implemented in Python by the BATMAN 1 package (Kreidberg et al. 2015). We assumed an orbital Observed - Computed (minutes) 0 100 200 300 400 500 600 700 800 # Orbits since Sept 2011 eccentricity of zero and used the a/R* and cosi values derived from the TESS data from Sectors 4 and 5. Stel- Figure 11. Comparison of observed transit times with com- lar limb darkening parameters were derived from ATLAS puted transit times for Smalley, WFC3, LDSS-3C, Spitzer, models and interpolated bi-linearly from tables presented and TESS observations. Computed transit times are based in Sing(2010). We choose to use the quadratic form and on the updated ephemeris and orbital period provided in Ta- fix coefficients to [0.04735, 0.15251] and [0.0604, 0.11834] ble2. for channels 1 and 2 respectively. The intrapixel sensitiv- ity variation (Ingalls et al. 2012), the change in measured 1 and 0.009743 ± 0.00035 days (14.0 ± 0.5 minutes) in flux as a function of stellar centroid position and meth- channel 2. ods of correction, are well documented (e.g. Ingalls et al. Table1 provides the wavebands, normalized tran- 2016). Here, we employ the Nearest Neighbors method sit depths, and 1σ transit depth uncertainties for the (NNBR), otherwise known as Gaussian Kernel Regression previously-described data sets. Table2 provides the with data (Lewis et al. 2013; Kilpatrick et al. 2017). transit ephemerides and uncertainties for the TESS, For each AOR, the best fit values for all free parame- HST/WFC3, and LDSS-3C observations. We used these ters were initially determined using matrix inversion. The transit times in conjunction with the Smalley et al.(2012) standard deviation of the normalized residuals (SDNR) ephemeris to re-compute a new ephemeris and period for times the βred factor (Gillon et al. 2010) was used as a WASP-79b. metric for selecting the best fit out of the multiple aper- Table 1. Normalized Transit Depths and Uncertainties tures. The results from the best fit aperture were passed to a Markov Chain Monte Carlo implemented by emcee 2 Instrument Waveband (Rp/R∗) σ 2 (Foreman-Mackey et al. 2013) to derive uncertainties of (Rp/R∗) each free parameter. The uncertainty on each data point (µ m) in the light curve is inflated by the βred factor to account TESS 0.586 – 1.031 1.1396 0.014 for the unresolved correlated noise. We use a number of 0.625 – 0.67 1.0725 0.0316 walkers at least twice the number of free parameters and run for 105 steps per walker before testing for conver- 0.675 – 0.725 1.0955 0.0206 gence using Gelman Rubin statistics with a threshold for 0.725 – 0.757 1.1026 0.0101 acceptance of 1.01 (Gelman & Rubin 1992). The initial LDSS-3C 0.770 – 0.825 1.1175 0.0073 10% of steps for each walker are discarded to remove the 0.825 – 0.875 1.1209 0.0204 ‘burn-in’ period. 0.875 – 0.925 1.1610 0.0205 0.925 – 0.975 1.2071 0.0215 Results 2.4.3. 0.975 – 1.025 1.1282 0.0332 At 4.5 µm we find a transit depth of 1.1396 % ± 0.0103 1.125 – 1.160 1.1486 0.0050 %. The SDNR of this observation was 0.04875 with a 1.160 – 1.195 1.1514 0.0053 βred factor of 1.09. At 3.6 µm we find a transit depth of 1.195 – 1.230 1.1398 0.0051 1.1224 % ± 0.0080 % with an SDNR of 0.005505 and β red 1.230 – 1.265 1.1395 0.0047 factor of 1.41. We find the center of transit time to occur 0.009835 ± 0.0008 days (14.15 ± 1.15 minutes) later than the predicted transit time (Smalley et al. 2012) in channel Table 1 continued 10

Table 1 (continued) We performed two atmospheric retrievals on the HST, 2 Instrument Waveband (R /R ) σ 2 LDSS, TESS, and Spitzer data using the ATMO code, p ∗ (Rp/R∗) which is described extensively in other works (Amundsen (µ m) et al. 2014; Tremblin et al. 2015, 2016, 2017; Drummond 1.265 – 1.300 1.1385 0.0061 et al. 2016; Goyal et al. 2018; Mikal-Evans et al. 2019). 1.300 – 1.335 1.1431 0.0052 We performed a chemical equilibrium retrieval as well as - 1.335 – 1.370 1.1418 0.0051 a free-chemistry retrieval with FeH and H , as FeH is one HST/WFC3 1.370 – 1.405 1.1634 0.0053 of the few molecules likely to be found at these temper- atures that has a maximum opacity at 1 µm (Tennyson 1.405 – 1.440 1.1524 0.0051 & Yurchenko 2018). For the and radius, we 1.440 – 1.475 1.1533 0.0061 assumed the main sequence values published by Smal- 1.475 – 1.510 1.1532 0.0053 ley et al.(2012)– R∗ = 1.64 R and M∗ = 1.56 M 1.510 – 1.545 1.1412 0.0054 – since their radius is consistent with that in the Gaia 1.545 – 1.580 1.1420 0.0065 Data Release 2. We used a Differential-evolution MCMC 1.580 – 1.615 1.1287 0.0056 to marginalize the posterior distribution (Eastman et al. 1.615 – 1.650 1.1201 0.0072 2013). We ran twenty-two chains each for 30,000 steps and discarded the first 2% of each chain as burn-in be- Spitzer 3.18 – 3.94 1.1224 0.0080 fore combining them into a single chain. 3.94 – 5.06 1.1396 0.0103 For the model assuming chemical equilibrium, the rel- ative elemental abundances for each model were calcu- lated in equilibrium on the fly, with the elements fit as- suming solar values and varying the metallicity ([M/H]). Table 2. Transit Times and Uncertainties However, we allowed for non-solar elemental compositions by varying the carbon, oxygen and potassium elemental Instrument Transit Times Transit Time Error abundances ([C/C ], [O/O ], [K/K ]) separately. For

(BJDTDB) the spectral synthesis, we included the spectrally active molecules of H , He, H O, CO , CO, CH , NH , Na, K, Spitzer 2457713.37538 8.0e-04 2 2 2 4 3 TiO, VO, FeH, and Fe. The temperature was assumed 2457720.70005 3.5e-04 to be isothermal, fit with one parameter, and we also in- LDSS-3C 2457742.674342 6.7e-05 cluded a uniform grey cloud parameterized by an opacity HST/WFC3 2457815.92219 1.1e-04 and a cloud top pressure level. 2458412.89196 5.4e-04 Figure 12 shows the chemical equilibrium retrieval spec- 2458416.55480 2.9e-04 trum with the estimated transit depths. Since the LDSS- 3C spectrum for WASP-79b shows an unexpected positive 2458427.54200 3.0e-04 slope from 0.65 µm to 0.8 µm, rather than the anticipated 2458431.20355 3.1e-04 negative slope due to Rayleigh scattering, the model has 2458434.86644 2.9e-04 a hard time reproducing the LDSS-3C data in the shorter TESS 2458438.52868 3.1e-04 wavelengths. This retrieval is driven toward a low tem- 2458442.19138 2.9e-04 perature of ∼800 K, which would be unexpected for this 2458445.85332 3.0e-04 planet, as the equilibrium temperature is ∼1800 K. The 2458449.51586 3.3e-04 retrieval is also driven toward high clouds by the muted 1.3 µm range of the HST data, which is relatively flat and 2458453.17815 3.2e-04 high compared to the 1.4 µm feature, which is large and 2458456.84066 3.2e-04 dips down comparatively far at 1.6 µm. The chemical 2458460.50406 3.0e-04 equilibrium model essentially is forced to use clouds to New 2455545.23874 3.7e-04 fit these features, though with a BIC of 70.75, this model New Period (days) 3.66239264 5.6e-07 does not provide a particularly good fit. For the free-chemistry retrieval, we assumed a constant abundance for each molecule that was independently fit, - and we varied the H2O, CO, Na, K, VO, FeH and H 3. DISCUSSION abundances; we included only these molecules as we ex- 3.1. Transmission Spectra Retrieval Analysis pect them to have strong spectral features in the wave- Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µm , K.S. Sotzen et al 11

0.108 0.108 ) ) s s /R /R pl 0.107 pl 0.107

0.106 0.106

0.105 0.105

0.104 0.104 Planet-to-star radius ratio (R Planet-to-star radius ratio (R 0.103 0.103

0.102 0.102 .3 .4 .5 .6 .7 .8 .9 1 1.5 2 2.5 3 3.5 4 5 .3 .4 .5 .6 .7 .8 .9 1 1.5 2 2.5 3 3.5 4 5 Wavelength (µm) Wavelength (µm)

Figure 12. Atmospheric spectrum from chemical equilibrium Figure 13. Atmospheric spectrum from ATMO free- ATMO retrieval based on HST, LDSS, TESS, and Spitzer chemistry retrieval based on HST, LDSS, TESS, and Spitzer transit depth estimates. The red line shows the best-fit model, transit depth estimates. The red line shows the best-fit model. and the blue areas indicate the 1, 2, and 3σ uncertainties. Due With the FeH and H-, this model better accommodates the to the high cloud deck that the model is driven to by the opac- slope of the water feature at longer wavelengths as well as the ity at ∼1µm, this model does not fit the decreased absorption diminished opacity in the bluer wavelengths. This model also at 1.6 µm or the positive slope in the bluer wavelengths. This accommodates a clearer atmosphere than the chemical equi- model had a BIC of 70.75 for 25 data points and 8 free pa- librium model, as well as a higher temperature (∼1200 K). rameters. This model had a BIC of 60.75 for 25 data points and 8 free parameters. bands corresponding to the data. Similar to the equilib- rium model, we also included a grey cloud and assumed its opacity. We also see a degeneracy between FeH and an isothermal temperature profile. The free-retrieval re- H-, implying an upper limit to the amount of H- that sults in a better fit, with a BIC of 60.75 for the same we can expect in this atmosphere. The upper limit on number of data points and free parameters, as it fills in VO implies that there is no significant amount in this the 1.2 µm HST opacity, where we would expect to see atmosphere. The Spitzer data weakly constrain the upper a larger dip at ∼1 µm if water were the only absorber limits for CO/CO2 but do not provide a lower limit. at these wavelengths (Figure 14,(Tennyson & Yurchenko While we don’t spectrally resolve Na, the free-chemistry 2018)). With the opacity of FeH at ∼1µm, this model retrieval includes it because the TESS transit depth is better accommodates the slope of the water feature at deeper than that for LDSS-3C, and the TESS data extend ∼1.6 µm as well as the diminishing opacity in the bluer into wavebands where Na features are present. This can wavelengths. The H- provides additional opacity in the lift the retrieval model of the TESS data point above the 0.7 to 1.3 µm range, decreasing the amount of FeH in LDSS-3C spectrum. In practice, other absorbers may be the atmosphere that is needed to reproduce the opac- causing absorption shortward of the LDSS-3C data. ity in the HST data. The H2O volume mixing ratio is Bean et al.(2018) provides the atmospheric retrieval re- well-constrained to an abundance of –2.20 ≤ log(H2O) ≤ sults for WASP-79b including only the HST/WFC3 ob- –1.55, which is 40x solar. Similar results have been found servation data with contributions from haze scattering. for WASP-121b (Evans et al. 2018; Mikal-Evans et al. Figure 16 shows the retrieval spectrum with simulated 2019). This model also allows for a clearer atmosphere JWST observation data and demonstrates the constraints than the chemical equilibrium model. The temperature is that the LDSS-3C data place on the scattering slope for still lower than that expected by equilibrium (1140 K ± WASP-79b. With the large error bars of the LDSS-3C 180), though the temperature uncertainties are large, and data and the precise TESS data, the LDSS-3C data do the marginalized distribution differs with the equilibrium not highly constrain the retrieval, but they do help rule value by less than 3-sigma confidence. out large scattering slopes, as was previously thought to As can be seen in the posterior distribution in Figure be likely (Bean et al. 2018) 15, water and temperature are well-constrained. For the Using the methods described in Stevenson(2016), we cloud top, we see a degeneracy between its altitude and compute a H2O - J(H) index for WASP-79b of 0.659. 12

H2O CO and this feature is likely best explained by a uniform FeH CO2 scattering cloud (Wakeford et al. 2017). WASP-121b, H- He Na+K Total however, has a Teq ∼2400 K, putting it in a temperature regime comparable to WASP-79b. Evans et al.(2016) compared models including haze only, TiO/VO, and TiO/VO/FeH and found that the models excluding FeH could not reproduce the WFC3 transmission spectrum at wavelengths near 1.3 µm (Evans et al. 2016). The comparable Teqs and similar spectrum shapes of WASP- 121b and WASP-79b imply that FeH may be a spectral mechanism for both planets and should be considered in the models for similar exoplanets. As Sing et al.(2016) note, hot Jupiters occupy a large parameter space with a wide range of gravities, metal- Figure 14. Atmospheric spectra from ATMO free chemistry licities, and temperatures, all of which affect a planet’s retrieval showing opacity contributions from potential atmo- atmospheric structure, circulation, and condensate for- spheric components. H O and FeH constitute the bulk of the 2 mation. It is therefore difficult to predict the spectral atmospheric opacity, with FeH providing increased opacity at ∼1µm. The H- provides additional opacity in the 0.7 to 1.3 µm features of a given exoplanet. In their investigation of range, decreasing the amount of FeH in the atmosphere that the influences of nonuniform cloud cover on transmis- is needed. This model allows for a clearer atmosphere than sion spectra, Line & Parmentier(2016) found that the the chemical equilibrium model, as well as a higher tempera- presence of inhomogeneous clouds along the terminators ture of ∼1200 K, which is more consistent with the expected of transiting exoplanets can strongly influence our inter- equilibrium temperature of this planet. pretation of current transit transmission spectra; that a nonuniform cloud cover along the planetary termina- Given its temperature and log g, this H2O - J(H) being tor can influence the observed transmission spectra; and less than 1.0 rules out the diagonal dashed line in Figure that failing to account for nonuniform cloud cover can 2 of Stevenson(2016) as a suitable boundary between bias molecular abundance determinations. They demon- clear and cloudy atmospheres and provides a better con- strated that the spectrum of a globally uniform deeper straint on the empirical relationship between water fea- cloud has a flatter shape and deeper trough than that of ture strength and . a nonuniform cloud cover, but that a nonuniform cloud 3.2. JWST Expectations cover spectrum was nearly identical to that produced by an atmosphere with a high mean molecular weight (Line JWST simulated observations were generated using & Parmentier 2016). Pandexo (Batalha et al. 2017) with the retrieval model However, the shape of the ingress and the egress of spectrum, assuming stellar T = 6600 K, log g = eff the transit is determined by the shape of the planetary 4.2, and [Fe/H] = +0.03 (Smalley et al. 2012). Fig- limb and can potentially be used to constrain the cloud ure 16 shows the simulated observations for the free- distribution over the planet limb and break the degen- chemistry retrieval model, providing an update to Bean eracies between partial cloudiness and high mean molec- et al.(2018)’s Figure 7 – which was generated using just ular weight atmospheres. The shape of the residuals the HST data – based on the inclusion of the LDSS, strongly depends on the distribution of clouds, and while TESS, and Spitzer data in addition to the HST data. the ingress and egress are symmetric in the case of po- Given these additional data, we expect to see a flatter lar clouds, they are antisymmetric in the case of morning spectrum with less pronounced Rayleigh scattering and clouds (Line & Parmentier 2016). H O and CO features than was originally predicted for 2 2 These are just a few reasons why exoplanet transit the JWST observations. transmission data are needed from JWST, a 6.5 m, space- WASP-121b (Evans et al. 2016) and HAT-P-26b based, near- to mid-infrared telescope. Unlike HST, (Wakeford et al. 2017) also showed a similar shape in which is maintained in a low Earth orbit that carries it the WFC3 spectrum, with muted depth in the 1.2 – 1.3 around the globe approximately every 90 minutes, JWST µm wavelength interval compared to the depth of the wa- will orbit at the Sun-Earth L2 point, giving it an unin- ter feature at 1.6 µm. Given the relatively moderate T eq terrupted view of the sky (Wakeford & Sing 2016). With of 990 K for HAT-P-26b, it would be unexpected for FeH this uninterrupted view, JWST should be able to provide to be present in its atmosphere in sufficient abundance to transit data with sufficiently precise timing to enable de- impact the transmission spectrum (Visscher et al. 2010), Transmission Spectroscopy of WASP-79b from 0.6 to 5.0 µm , K.S. Sotzen et al 13

1.00 1.0 0.80 0.8 1000 0.60 0.6 Normalized Normalized Histogram Histogram Density Density

0.40 0.4

0.20

0.2 Relative Freq.

0.00 0.0 500 1000 Temperature (K) 1500 2000

1.00 1.0 1.711.71 1.71

1.70 0.8 800 1.701.70

1.69 1.69 0.6 Normalized Histogram Density

1.69,1 mbar) ,1 mbar) Jup

1.68 Jup

(R 1.68

1.68pl 0.4 R (R

1.67

pl 1.671.67 0.2 Relative Freq. R

1.66 1.66

500 1000 1500 2000 0.00 500 1000 1500 2000 1.66 Temperature (K) 0.0 1.66 1.67 1.68 Rpl(RJup,1 mbar) 1.69 1.70 1.71 500 1000 1500 2000 ) 0 Temperature (K) 1.00 1.0 σ / σ ) 0

5 55

σ 5 / 5 0.8 σ 600 0.6 ) 0 σ / σ ) 0 σ / σ

0 00 cloud opacity ln( opacity cloud cloud opacity ln( opacity cloud 00 Normalized Histogram Density 0.4

−5 −5 −−55

−cloud opacity ln( 5

0.2 Relative Freq.

500 1000 1500 2000 1.66 1.67 1.68 1.69 1.70 1.71 0.00

500 1000 Temperature (K) 1500 2000 1.66 1.67 1.68 Rpl(RRpl(RJupJup,1,1 mbar) mbar) 1.69 1.70 1.71

−5 0 5 0.0 cloud opacity ln(σ/σ0) cloud opacity ln( 500 1000 1500 2000

Temperature (K) 1.00

0 0 0 00 1.0 00 Density

−1 −1 −1 −1 −−11 0.8 −) 1 pl

−2 −2 −2 −2 −2 400 −2−2 0.6 ) ) pl pl Cloud Top (R Cloud Top (R Cloud Top (bar) Normalized Histogram Density −3 −3−3 −3 −3 −−33 0.4

−4 −4 −4 −−44 −Cloud Top (R 4−4

−5 −5 −5 −−55 −5 0.2 Relative Freq. Cloud Top (bar) −5

1.661.671.681.691.701.71 500 1000 1500 2000 1.66 1.67 1.68 1.69 1.70 1.71 −5 0 5 0.00

500 1000 Temperature (K) 1500 2000 Rpl(RJup,1 mbar) −5 cloudcloud opacity opacity0 ln( ln(σ/σ0)σ/σ0) 5

−5 −4 −3 −2 −1 0 0.0 Cloud Top (bar) 500 1000 1500 2000

Temperature (K) 1.00 −1.5−1.5 −1.5 −1.5−1.5 −1.5−1.5 −1.5−1.5 1.0 O)

−2.0 −2.0−2.0 −2.0−2.0 −2.0−2.0 0.8 2 −2.0−2.0 O) 2 0.6 200 O) O) O) 2 2 2 O) O) O) 2 2 2 VMR ln(H VMR ln(H VMR ln(H VMR VMR log(H VMR log(H VMR log(H VMR Normalized Histogram Density −2.5−2.5 −2.5 −2.5−2.5 −2.5−2.5 −2.5−2.5 0.4 VMR ln(H −3.0−3.0 −3.0 −3.0−3.0 −3.0−3.0 −3.0−3.0 Relative Freq. VMR log(H 0.2

−3.5 −3.5 −3.5−3.5 −3.5−3.5 −3.5−3.5

500500 10001000 15001500 20002000 1.661.66 1.671.67 1.681.68 1.691.69 1.701.70 1.711.71 −−5 00 55 −−5 −−4 −−3 −−2 −−1 00 0.00

Temperature (K) R (RRpl(RJup,1,1 mbar) mbar) cloudcloud opacity opacity ln( ln(σ/σ0) / ) CloudCloud Top Top (R (bar)pl) −3.5 pl Jup σ σ0

−3.5 −3.0 −2.5 −2.0 −1.5 0.0 VMR log(H2O) 500 1000 1500 2000

Temperature (K) 1.00 −2−2 −2 −−22 −−22 −−22 −−22 1.0

−4 −4 −4 −4 −4 −4−4 −4 −4 −4 −4 0.8

−6 −−66 −−66 −−66 −−66 0 −6−6 0.6 VMR ln(CO) VMR ln(CO) VMR ln(CO) VMR ln(CO) VMR VMR log(CO) VMR log(CO) VMR log(CO) VMR log(CO) Normalized Histogram Density Histogram Normalized −8−8 −8 −−88 −−88 −−88 −−88

VMR ln(CO) 0.4 −10−10 −10 −10−10 −10−10 −10−10 −10−10 VMR log(CO)

−12 −12−12 −12−12 −12−12 −12−12 −12−12 0.2 Relative Freq.

0.00

500500 10001000 15001500 20002000 1.661.66 1.671.67 1.681.68 Rpl(RJup,1 mbar) 1.691.69 1.701.70 1.711.71 −−5 cloud opacity00 ln(σ/σ0) 55 −−5 −−4 −−3 Cloud Top (Rpl) −−2 −−1 00 −−3.53.5 −−3.03.0 −−2.52.5 VMR ln(H2O) −−2.02.0 −−1.51.5 Temperature (K) R (R ,1 mbar) cloud opacity ln( / ) Cloud Top (bar) VMR log(H O) pl Jup σ σ0 2 0.0 −12 −10 −8 −6 −4 −2 VMR log(CO) 500 1000 1500 2000

Temperature (K) 1.00 −2−2 −2 −−22 −−22 −−22 −−22 −−22 1.0

−4 −4 −4 −4 −4 −4 −4−4 −4 −4 −4 −4 −4 0.8

−6−6 −6 −−66 −−66 −−66 −−66 −−66 0.6 VMR ln(Na) VMR ln(Na) VMR ln(Na) VMR ln(Na) VMR ln(Na) VMR VMR log(Na) VMR log(Na) VMR log(Na) VMR log(Na) VMR log(Na) Normalized Histogram Density Normalized Histogram Density −8−8 −8 −−88 −−88 −−88 −−88 −−88

VMR ln(Na) 0.4 −10−10 −10 −10−10 −10−10 −10−10 −10−10 −10−10 VMR log(Na)

−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 0.2 Relative Freq.

0.00

500500 10001000 15001500 20002000 1.661.66 1.671.67 1.681.68 Rpl(RJup,1 mbar) 1.691.69 1.701.70 1.711.71 −−5 cloud opacity00 ln(σ/σ0) 55 −−5 −−4 −−3 Cloud Top (Rpl) −−2 −−1 00 −−3.53.5 −−3.03.0 −−2.52.5 VMR ln(H2O) −−2.02.0 −−1.51.5 −−12 −−1010 −−8 VMR ln(CO) −−6 −−4 −−2 Temperature (K) R (R ,1 mbar) cloud opacity ln( / ) Cloud Top (bar) VMR log(H O) VMR log(CO) pl Jup σ σ0 2 0.0 −12 −10 −8 −6 −4 −2 VMR log(Na) 500 1000 1500 2000

−2 −−22 −−22 −−22 −−22 −−22 −−22 −2−2 Temperature (K) 1.00 1.0

−4−4 −4 −−44 −−44 −−44 −−44 −−44 −−44 0.8 −6−6 −6 −−66 −−66 −−66 −−66 −−66 −−66 0.6 VMR ln(K) VMR ln(K) VMR ln(K) VMR ln(K) VMR ln(K) VMR ln(K) VMR log(K) VMR log(K) VMR log(K) VMR log(K) VMR log(K) VMR log(K) VMR Normalized Histogram Density Density Histogram Histogram Normalized Normalized −8−8 −8 −−88 −−88 −−88 −−88 −−88 −−88 VMR ln(K)

−10 −10−10 −10−10 −10−10 −10−10 −10−10 −10−10 0.4 −10−10 VMR log(K)

−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 0.2 Relative Freq. Relative Freq.

0.00

500500 10001000 15001500 20002000 1.661.66 1.671.67 1.681.68 Rpl(RJup,1 mbar) 1.691.69 1.701.70 1.711.71 −−5 cloud opacity00 ln(σ/σ0) 55 −−5 −−4 −−3 Cloud Top (Rpl) −−2 −−1 00 −−3.53.5 −−3.03.0 −−2.52.5 VMR ln(H2O) −−2.02.0 −−1.51.5 −−12 −−1010 −−8 VMR ln(CO) −−6 −−4 −−2 −−1212 −−10 −−8 VMR ln(Na) −−6 −−4 −−2 Temperature (K) R (R ,1 mbar) cloud opacity ln( / ) Cloud Top (bar) VMR log(H O) VMR log(CO) VMR log(Na) pl Jup σ σ0 2 0.0 −12 −10 −8 −6 −4 −2 VMR log(K) 500 1000 1500 2000

Temperature (K) 1.00 1.0

−8−8 −8 −−88 −−88 −−88 −−88 −−88 −−88 −−88 0.8

−10 −10 −10−10 −10−10 −10−10 −10−10 −10−10 −10−10 −10−10 0.6 VMR ln(VO) VMR ln(VO) VMR ln(VO) VMR ln(VO) VMR ln(VO) VMR ln(VO) VMR ln(VO) VMR log(VO) VMR log(VO) VMR log(VO) VMR log(VO) VMR log(VO) VMR log(VO) VMR log(VO) −10 Normalized Normalized Histogram Histogram Density Density

VMR ln(VO) 0.4 −12−12 −12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 VMR log(VO) 0.2 Relative Freq. Relative Freq.

0.00

500500 10001000 15001500 20002000 1.661.66 1.671.67 1.681.68 Rpl(RJup,1 mbar) 1.691.69 1.701.70 1.711.71 −−5 cloud opacity00 ln(σ/σ0) 55 −−5 −−4 −−3 Cloud Top (Rpl) −−2 −−1 00 −−3.53.5 −−3.03.0 −−2.52.5 VMR ln(H2O) −−2.02.0 −−1.51.5 −−12 −−1010 −−8 VMR ln(CO) −−6 −−4 −−2 −−1212 −−10 −−8 VMR ln(Na) −−6 −−4 −−2 −−12 −−10 −−8 VMR ln(K) −−6 −−4 −−2 Temperature (K) R (R ,1 mbar) cloud opacity ln( / ) Cloud Top (bar) VMR log(H O) VMR log(CO) VMR log(Na) VMR log(K) pl Jup σ σ0 2 0.0 −12 −10 −8 VMR log(VO) 500 1000 1500 2000

−2 −−22 −−22 −−22 −−22 −−22 −−22 −−22 −2 −2 −2−2 Temperature (K) 1.00 1.0 −4−4 −4 −−44 −−44 −−44 −−44 −−44 −−44 −−44 −4 −4 0.8 −6−6 −6 −−66 −−66 −−66 −−66 −−66 −−66 −−66 −6 −6 0.6 VMR ln(FeH) VMR ln(FeH) VMR ln(FeH) VMR ln(FeH) VMR ln(FeH) VMR ln(FeH) VMR ln(FeH) VMR ln(FeH) VMR VMR log(FeH) VMR log(FeH) VMR log(FeH) VMR log(FeH) VMR log(FeH) VMR log(FeH) VMR log(FeH) VMR log(FeH) VMR Normalized Normalized Histogram Histogram Density Density −8−8 −8 −−88 −−88 −−88 −−88 −−88 −−88 −−88 −8 −8

−10 −10−10 −10−10 −10−10 −10−10 −10−10 −10−10 −10−10 −10 −10 0.4 −VMR ln(FeH) 10−10

−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12 −12 Relative Freq. VMR log(FeH) −12−12 0.2

0.00

500500 10001000 15001500 20002000 1.661.66 1.671.67 1.681.68 Rpl(RJup,1 mbar) 1.691.69 1.701.70 1.711.71 −−5 cloud opacity00 ln(σ/σ0) 55 −−5 −−4 −−3 Cloud Top (Rpl) −−2 −−1 00 −−3.53.5 −−3.03.0 −−2.52.5 VMR ln(H2O) −−2.02.0 −−1.51.5 −−12 −−1010 −−8 VMR ln(CO) −−6 −−4 −−2 −−1212 −−10 −−8 VMR ln(Na) −−6 −−4 −−2 −−12 −−10 −−8 VMR ln(K) −−6 −−4 −−2 −−12 VMR ln(VO) −−10 −−8 Temperature (K) R (R ,1 mbar) cloud opacity ln( / ) Cloud Top (bar) VMR log(H O) VMR log(CO) VMR log(Na) VMR log(K) VMR log(VO) pl Jup σ σ0 2 0.0 −12 −10 −8 −6 −4 −2 VMR log(FeH) 500 1000 1500 2000

Temperature (K) 1.00 1.0 −8−8 −8 −−88 −−88 −−88 −−88 −−88 −−88 −−88 −8 −8 −−88 0.8

−10 −10 −10−10 −10−10 −10−10 −10−10 −10−10 −10−10 −10−10 −10 −10 −10−10 0.6 VMR ln(H_) VMR ln(H_) VMR ln(H_) VMR ln(H_) VMR ln(H_) VMR ln(H_) VMR ln(H_) VMR ln(H_) VMR ln(H_) VMR log(H_) VMR log(H_) VMR log(H_) VMR log(H_) VMR log(H_) VMR log(H_) VMR log(H_) VMR log(H_) VMR log(H_) VMR −10 Normalized Normalized Histogram Histogram Density Density

VMR ln(H_) 0.4 −12−12 −12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12−12 −12 −12 −12−12 VMR log(H_)

0.2 Relative Freq.

0.00

500 1000 1500 2000 1.66 1.67 1.68 R (R ,1 mbar) 1.69 1.70 1.71 −5 cloud opacity0 ln( / ) 5 −5 −4 −3 Cloud Top (R ) −2 −1 0 −3.5 −3.0 −2.5 VMR ln(H O) −2.0 −1.5 −12 −10 −8 VMR ln(CO) −6 −4 −2 −12 −10 −8 VMR ln(Na) −6 −4 −2 −12 −10 −8 VMR ln(K) −6 −4 −2 −12 VMR ln(VO) −10 −8 −12 −10 −8 VMR ln(FeH) −6 −4 −2 pl Jup σ σ0 pl 2 0.0 500 1000 1500 2000 500 1000Temperature1500 (K) 20001.661.671.681.691.701.71 −5 0 5 −5 −4 −3 −2 −1 0 −3.5 −3.0 −2.5 −2.0 −1.5 −12 −10 −8 −6 −4 −2 −12 −10 −8 −6 −4 −2 −12 −10 −8 −6 −4 −2 −12 −10 −8 −12 −10 −8 −6 −4 −2 −12 −10 −8 Temperature (K) Rpl(RJup,1 mbar) cloud opacity ln(σ/σ0) Cloud Top (bar) VMR log(H2O) VMR log(CO) VMR log(Na) VMR log(K) VMR log(VO) VMR log(FeH) VMR log(H_)

Figure 15. Pairs plot for the free chemistry atmospheric retrieval showing variable correlations and constraints. The orange crosses indicate the median best fit values, and the dashed lines show the 1σ uncertainties. Water and temperature are well- constrained. For the cloud top, we see a degeneracy between its altitude and its opacity. We also see a degeneracy between FeH and H-, implying an upper limit to the amount of H- that we can expect in this atmosphere. The lack of constraint on VO implies that it is not present in this atmosphere. The combination of the deeper TESS transit depth and shallower short-wavelength LDSS3 data caused the model to include Na in the atmosphere. tection of clouds at the terminator. These more precise HST/WFC3 data (1.1 - 1.7 µm) and the process described observations in a broader range of wavelengths will al- in Stevenson et al.(2014). We have detected a probable low JWST observations of WASP-79b to contribute to water feature centered at 1.4 µm that is consistent with the identification of clouds vs hazes in the atmosphere of the spectra of other hot Jupiters. The LDSS-3C data this hot Jupiter. With its muted but detectable water (0.6 - 1.0 µm) are noisy, and the location of the refer- feature and its occupation of the clear/cloudy transition ence star relative to the target star hindered negation region of the temperature/gravity phase space, WASP- of atmospheric effects occurring during the observation. 79b continues to represent an interesting target for the The spectrum extracted from the LDSS-3C data is there- ERS program. fore difficult to interpret, but overall looks relatively flat. In conjunction with the muting of the water feature in 4. CONCLUSIONS the HST/WFC3 spectrum, this may indicate the presence As part of the PanCET program, we have performed of clouds in the atmosphere of this hot Jupiter, though a spectral analysis of the hot Jupiter WASP-79b using ATMO models indicate that including the absorbers FeH 14

Figure 16. JWST simulated observations (left) and anticipated temperature and water constraints (right) from the PanCET Program observations of WASP-79b. Left: the simulated observations were generated using Pandexo (Batalha et al. 2017), based on the free-chemistry atmospheric retrieval spectrum and the observation data described previously. Simulated observations are shown with the estimated LDSS, TESS, HST, and Spitzer transit depths. Results are binned to R = 100 (left). The LDSS-3 data constrain the scattering slope, compared to Figure 7 in Bean et al.(2018), which shows the Pandexo results for the best-fit solution for just the HST/WFC3 data with contributions from haze scattering. Right: anticipated constraints (red) on the atmospheric temperature and water abundance compared with constraints from HST (blue). The constraints are improved by orders-of-magnitude due to increased data resolution and the presence of multiple water features (Greene et al. 2016). and H- provides a better fit to the data and allows for work is also based on observations made with the LCO a temperature more consistent with the equilibrium tem- Magellan Clay Telescope. Travel to LCO/Magellan was perature. The XMM Newton, TESS, and AIT observa- funded by the Sagan Fellowship Program, supported by tion data indicate that the decreased transit depths in NASA and administered by the NASA Exoplanet Sci- bluer wavelengths of the LDSS-3C data are not caused ence Institute (NExScI). We would like to thank Hannah by stellar faculae or plage, though the low resolution of Diamond-Lowe and Zach Berta-Thompson for their assis- these spectral data makes it difficult to determine what tance with the LDSS-3C stretching analysis. Work done may be causing these shallower transit depths. The tran- by B.M. Kilpatrick was supported by NASA Headquar- sit depths estimated from the TESS, LDSS, HST, and ters under the NASA Earth and Space Science Fellowship Spitzer data are all in good agreement, indicating the vi- Program under Grant Number 80NSSC17K0484. This ability of the methods described herein. portion of the work is based on observations made with WASP-79b represents a primary target for the PanCET the Spitzer Space Telescope, which is operated by the Jet program, and given the detectable water feature and the Propulsion Laboratory, California Institute of Technol- delayed launch of the JWST, it is now a primary target for ogy under a contract with NASA. Alain Lechavelier des the JWST Early Release Science (ERS) program (Bean Etangs acknowledges support from the Centre National et al. 2018) and will be scheduled for 42 hours of JWST d’Etudes´ Spatiale (CNES). Jorge Sanz-Forcada acknowl- observation time in four different modes. These observa- edges funding by the Spanish MINECO grant AYA2016- tions will provide more precise data over a broader range 79425-C3-2-P. This project has received funding from the of wavelengths, providing a more detailed spectrum and European Research Council (ERC) under the European possibly allowing for the detection of terminator clouds Unions Horizon 2020 research and innovation programme and/or vibrational modes of condensate species. (project Four Aces; grant agreement No 724427). It has also been carried out in the frame of the National Centre for Competence in Research PlanetS supported by the Swiss National Science Foundation (SNSF). Acknowledgements Support for program GO-14767 was provided by NASA through a grant from the Space Software: ATMO(Amundsenetal.2014;Tremblinetal. Telescope Science Institute (STScI), which is operated 2015, 2016, 2017; Drummond et al. 2016; Goyal et al. 2018; by the Association of Universities for Research in As- Mikal-Evansetal.2019),BATMAN(Kreidbergetal.2015), tronomy, Inc., under NASA contract NAS 5-26555. This T-RECS (Stevenson et al. 2016a)

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