# Into the UV: A precise transmission spectrum of HAT-P-41b using
Hubble’s WFC3/UVIS G280 grism

H.R. Wakeford School of Physics, University of Bristol, HH Wills Physics
Laboratory, Tyndall Avenue, Bristol BS8 1TL, UK  Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  D.K. Sing
Department of Earth & Planetary Sciences, Johns Hopkins University, Baltimore,
MD, USA  Department of Physics & Astronomy, Johns Hopkins University,
Baltimore, MD, USA  K.B. Stevenson JHU Applied Physics Laboratory, 11100 Johns
Hopkins Rd, Laurel, MD 20723, USA  N.K. Lewis Department of Astronomy and Carl
Sagan Institute, Cornell University, 122 Sciences Drive, Ithaca, NY 14853, USA
N. Pirzkal  Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  T.J. Wilson University of Exeter, Physics Building,
Stocker Road, Exeter, Devon, Ex4 4QL, UK  Space Telescope Science Institute,
3700 San Martin Drive, Baltimore, MD 21218, USA  J. Goyal Department of
Astronomy and Carl Sagan Institute, Cornell University, 122 Sciences Drive,
Ithaca, NY 14853, USA  T. Kataria  NASA Jet Propulsion Laboratory, 4800 Oak
Grove Drive, Pasadena, CA 91109, USA  T. Mikal-Evans Kavli Institute for
Astrophysics and Space Research, Massachusetts Institute of Technology, 77
Massachusetts Avenue, 37-241, Cambridge, MA 02139, USA  N. Nikolov  Department
of Physics & Astronomy, Johns Hopkins University, Baltimore, MD, USA  J. Spake
Division of Geological and Planetary Sciences, California Institute of
Technology, Pasadena, CA 91125, USA

(Accepted to AJ February 29, 2020)

###### Abstract

The ultraviolet-visible wavelength range holds critical spectral diagnostics
for the chemistry and physics at work in planetary atmospheres. To date,
exoplanet time-series atmospheric characterization studies have relied on
several combinations of modes on Hubble’s STIS/COS instruments to access this
wavelength regime. Here for the first time, we apply the Hubble WFC3/UVIS G280
grism mode to obtain exoplanet spectroscopy from 200-800 nm in a single
observation. We test the G280 grism mode on the hot Jupiter HAT-P-41b over two
consecutive transits to determine its viability for exoplanet atmospheric
characterization. We obtain a broadband transit depth precision of 29–33 ppm
and a precision of on average 200 ppm in 10 nm spectroscopic bins. Spectral
information from the G280 grism can be extracted from both the positive and
negative first order spectra, resulting in a 60% increase in the measurable
flux. Additionally, the first HST orbit can be fully utilized in the time-
series analysis. We present detailed extraction and reduction methods for use
by future investigations with this mode, testing multiple techniques. We find
the results fully consistent with STIS measurements of HAT-P-41b from 310–800
nm, with the G280 results representing a more observationally efficient and
precise spectrum. HAT-P-41b’s transmission spectrum is best fit with a model
with Teq=2091 K, high metallicity, and significant scattering and cloud
opacity. With these first of their kind observations, we demonstrate that
WFC3/UVIS G280 is a powerful new tool to obtain UV-optical spectra of
exoplanet atmospheres, adding to the UV legacy of Hubble and complementing
future observations with the James Webb Space Telescope.

††software: POET pipeline (Stevenson et al., 2012b; Cubillos et al., 2013),
IRAF (Tody, 1986, 1993), IDL Astronomy user’s library (Landsman, 1995), NumPy
(Oliphant, 2006–), SciPy (Virtanen et al., 2019), MatPlotLib (Caswell et al.,
2019), AstroPy (Astropy Collaboration et al., 2018), Photutils (Bradley et
al., 2019)

##  1 Introduction

The characterization of planetary atmospheres in the solar system and beyond
has long leveraged the ultra-violet (UV) to near-infrared (NIR) spectroscopic
capabilities of the Hubble Space Telescope (HST). Observations with HST have
been critical in the exploration of the chemical composition, climate, and
aerosol properties of exoplanet atmospheres (see Kreidberg et al., 2018, and
references therein). With the help of HST we now know that clouds and hazes
are likely present in all types of exoplanetary atmospheres (e.g. Marley et
al., 2013; Helling, 2019; Wakeford et al., 2019), but we currently lack
information related to their abundances, physical properties and extent
throughout the atmosphere. We also know that exoplanets exhibit extended upper
atmospheres with evidence for atmospheric escape (e.g. Ehrenreich et al.,
2014; Bourrier et al., 2018; Sing et al., 2019), but struggle to connect
physical processes in the lower and upper portions of exoplanet atmospheres.

The UV through optical (200–800 nm) spectra of planets hold rich information
about the chemistry and physics at work across a broad range of atmospheric
pressures. In the solar system, UV and near-UV spectroscopy has been critical
in identifying and measuring the abundances of a variety of hydrocarbon and
sulfur-bearing species, produced via photochemical mechanisms, as well as
oxygen and ozone and more. For exoplanets, UV to near-UV spectroscopy has been
especially useful for constraining aerosol properties and exploring
atmospheric chemistry in hot ($>$1000 K) atmospheres (e.g. Sing et al., 2016;
Evans et al., 2018). To date, only a handful of exoplanets have been probed in
the critical 200–400 nm wavelength range that crosses the optical to UV
boundary. Results from these studies have been mixed, limited by the
wavelength coverage and sensitivity of the workhorse instrument for such
studies, HST’s Space Telescope Imaging Spectrograph (STIS) G430L and E230M
gratings.

It is important to remember that none of HST’s instruments or modes were
specifically designed to support exoplanet observations. It has only been
through the development of new observational strategies, such as spatial
scanning (McCullough & MacKenty, 2012; McCullough et al., 2014), and data
reduction techniques that the potential for HST to probe exoplanet atmospheres
has been achieved. In general, slitless spectroscopic observing modes have
been preferred for high-precision time-series observations of exoplanets that
transit, pass in front of, their host stars because they typically offer more
throughput and temporal stability. The slitless spectroscopy capabilities
HST’s Wide Field Camera 3 (WFC3) have been heavily used by the exoplanet
community at infrared wavelengths (750–1800 nm) with the G102 and G141 grisms.
However, HST’s WFC3 UV/Visible (UVIS) channel also offers slitless
spectroscopy in the UV through visible (200–800 nm) wavelength range that has
yet to be leveraged for exoplanet observations. In fact, this mode has only
been employed in a handful of scientific investigations, first used as part of
HST WFC3 early release science programs in cycle 16 (2006), however, none of
the G280 work was published from this study.

Here we detail for the first time the observations, spectral extraction, and
analysis processes taken to apply Hubble’s WFC3/UVIS G280 spectroscopic grism
to transiting exoplanet observations. We first introduce the challenges in
using the UVIS G280 grism in §2. In §3 we detail the observations and spectral
extraction procedures used. We then detail the broadband time-series analysis
using two systematic reduction techniques in §4. We use Spitzer transit
observations to refine system parameters and update the orbital ephemeris in
§4.1 and 4.2. We outline the spectroscopic analysis in §5 and discuss the
results in §6 including searching for evidence of atmospheric escape and
comparisons to STIS data. We then conclude with a summary of our results and
the potential of WFC3/UVIS G280 for future exoplanet investigations.

##  2 Introduction to the UVIS G280 grism

The WFC3 instrument on HST is fitted with two channels, UVIS and IR. Across
these two channels are three slitless spectroscopic grisms: G280 in UVIS and
G102 and G141 in the IR channel. The IR grisms have been extensively applied
to exoplanet atmospheric studies with increasing success at the advent of
spatial scanning (McCullough & MacKenty, 2012), where HST slews in the cross-
dispersion direction to spread the target light over a column of pixels (e.g.,
Deming et al. 2013; Kreidberg et al. 2014; Wakeford et al. 2013, 2016; de Wit
et al. 2016). However, the UVIS G280 grism has not had such usage despite
large throughput in the near-UV (NUV) and wide coverage from 200–800 nm. More
commonly, studies that cover 300–900 nm are conducted with multiple
observations using HST’s STIS G430L and G750L low resolution gratings from
300–550 nm and 500–900 nm respectively (e.g., Nikolov et al. 2014; Sing et al.
2016; Lothringer et al. 2018) despite their comparatively low throughput (Fig.
1).

The UVIS grism, however, comes with several quirks that make it difficult to
observe with and challenging to analyze. A number of these challenges will
also affect observations with James Webb Space Telescope’s (JWST)
spectroscopic instrument modes. Therefore, WFC3/UVIS G280 is a current working
example of the challenges that will be faced with JWST. Here we detail each of
the challenges associated with WFC3’s UVIS grism and also the advantages it
has over other instrument modes in the NUV to optical wavelengths.

###  2.1 Challenges

We detail some of the challenges encountered with this dataset and those
expected in the general use of this instrument mode for exoplanet time-series
characterisation.

\- Curved spectral trace: The trace for spectral order with the G280 grism is
strongly curved at shorter wavelengths. The trace is best fit with a 6th order
polynomial function detailed by Pirzkal et al. (2017) and section 3. This
curvature causes it to be offset in the cross-dispersion direction from the
0th order position, meaning subarray sizes need to be carefully chosen. Unlike
the IR grisms, the spectra should not be spatially scanned as this would
result in overlapping wavelength regions along the scan direction.

The curved spectral trace also introduces a non-linear wavelength solution,
meaning each pixel has a slightly different wavelength range than the
surrounding pixels in that column. The wavelength solution is therefore
extracted relative to the fitted trace position on the detector with a 6th
order polynomial.

\- Multiple overlapping orders: Additional spectral orders, both positive and
negative, overlap with the first order spectra at wavelengths greater than 550
nm. In many cases these additional orders will be much dimmer than the first
order spectrum and not impact the observations. However, for stars bright in
the NUV such as O,B,A stars the additional spectral orders may impact the
spectral extraction.

In the presented case, the second order spectrum is $\approx$ 65$\times$
dimmer than the primary spectrum in both positive and negative orders. This
would negligibly contribute to the measured transit depths causing the
measured planetary radius ($R_{m}$) to be $\approx$ 99.24% of the true
planetary radius ($R$) following,

$\frac{R_{m}}{R}=\sqrt{\frac{1}{1+\frac{1}{65}}}=0.9924.$ (1)

\- Geometric distortion: Using the grism filters causes the spectra to be
offset spatially in the detector relative to their direct image X and Y
coordinates. For the UVIS array the offset varies as a function of the
position due to geometric distortion (Rothberg et al., 2011). The relationship
between the coordinates in x and y pixel position also needs to be taken into
account when planning the observations in X and Y arcsecond coordinates (see
WFC3 data handbook for conversion functions 111WFC3 Data Handbook Appendix
C.2).

\- Orientation constraints: The spectral trace of the positive and negative
orders extend across over 500 pixels each depending on the brightness of the
target. In a crowded field or where a target is part of a visual binary
system, tight orient constraints need to be placed on the observations to
prevent contamination from nearby stars. This is often mitigated in WFC3/IR
grism observations using spatial scans where the spectra can be extracted by
differencing the individual non-destructive reads within the final science
frame. However, as WFC3/UVIS grism observations can only be conducted in stare
mode, up-the-ramp sampling cannot be used to recover overlapping spectra.

\- Cosmic rays: The large wavelength coverage that extends significantly into
the blue wavelengths increases the number of detected comic rays compared to
the IR detectors.

\- JWST challenges: For JWST a number of the instrument modes that will be
utilized for exoplanet timeseries data exhibit curved spectral traces,
overlapping spectral orders, and contamination constraints from additional
sources on the sky. NIRISS SOSS mode is most similar to the G280 grism with
both strongly curved spectral traces and overlapping spectral orders. It is
also expected that NIRSpec Bright Object Time Series observations will also
have a slightly curved trace. For all slitless modes on JWST used for
exoplanet time series observations contamination overlap will need to be
carefully considered and orientation constrained carefully sampled.

###  2.2 Advantages

While we have detailed many challenges there are also significant advantages
to this instrument over other modes in the NUV and optical. We detail these
here.

\- Wide wavelength coverage: observations are conducted over the whole
wavelength range 200–800 nm in a single exposure. Low resolution spectra
across this wide wavelength range can address the two main exoplanet science
points revealed by HST observations; cloud opacities and atmospheric escape.
The G280 grism can measure both the lower atmosphere sensitive to aerosol
scattering, while large atmospheric escape signatures can be detectable in
narrow bands around strong Fe and Mg signatures at $<$300 nm.

Figure 1: Throughput curves for HST instruments and modes commonly used for
exoplanet time-series observations. Solid lines are the WFC3-UVIS G280 grism
+1 and -1 orders; the dark dot-dashed line is the combined transmission of
both orders. Dashed lines are STIS G430L and G750L gratings. -..- line shows
the COS G230L. Dotted lines are WFC3-IR G102 and G141 grisms.

\- Multiple spectral orders: both the positive and negative orders are
measured in each exposure. The UVIS CCD is split into two chips (1 & 2), with
each chip of 2051$\times$2048 pixels easily encompassing both spectral orders
which each cover $\approx$500 pixels in the dispersion direction. In the
presented observations we use chip 2 as it has been shown to be more stable
than chip 1. We therefore also recommend the use of chip 2 for future studies.

\- Throughput: WFC3/UVIS has the highest throughput amongst all HST
instruments in the wavelength range from its lower cut off at 200 nm to the
upper end at $\sim$800 nm. The throughput of UVIS G280 in the NUV is on
average 25 times that of STIS E230M between 200–400 nm, and roughly four times
that of STIS G430L at 350 nm. UVIS G280 also has the advantage of being able
to measure both positive and negative spectral orders that have a combined
throughput greater than STIS G430L across the whole wavelength range (see Fig.
1).

\- New calibration program: Prior to these observations there have been three
instrument science reports (Kuntschner et al., 2009; Rothberg et al., 2011;
Pirzkal et al., 2017) and no scientific papers using this grism. As demand for
time-series observations with this grism have increased there are now new
calibration programs being implemented to better characterize the detector and
improve the trace fitting for all spectral orders. Calibration of the
instrument and mode are important to understand the structure of the CCD, on-
sky changes in the PSF, and wavelength dispersion across the detector -
especially under the requirements of long term stability for exoplanet
investigations that span multiple HST orbits.

Overall the WFC3/UVIS G280 grism has many challenges that are difficult but
not impossible to overcome, and a significant advantage over other instrument
modes in this wavelength range. In the following sections we detail the
observations taken and the measurements made with the tools to overcome these
challenges.

##  3 UVIS G280 Observations

We used HST’s WFC3/UVIS channel with the G280 spectroscopic slitless grism to
observe the spectrum of the transiting exoplanet host star HAT-P-41 from
200–800 nm (GO-15288, PIs D.K. Sing & N.K. Lewis). Unlike the WFC3/IR G102 and
G141 grisms, the UVIS G280 grism produces a spectrum that is strongly curved,
with overlapping spectral orders at longer wavelengths, and a dimmer
($\sim$60%) -1 order spectrum compared to the +1 order. We designed an
observation strategy that would cover both +1 and -1 orders simultaneously to
examine this difference in flux and test the usability of the G280 grism for
time-series exoplanet studies.

We observed the target HAT-P-41, in the constellation of Altair, over two
visits, each consisting of five HST orbits, to measure the transit of the hot
Jupiter exoplanet HAT-P-41b. The two visits were separated by a single
planetary orbital period (visit 1: 2018 August 1st; visit 2: 2018 August 4th,
period = 2.694047 days), significantly reducing the potential impact of any
stellar variations on the transits of this quiet F6 star.

Each visit consists of 54 exposures, over 5 HST orbits, with exposure times of
190 seconds. We used a 2100 $\times$ 800 sub-array, with a POS TARG Y offset
of -50” to center the spectrum on chip 2. The sub-array is cut out of the full
2051 $\times$ 4096 pixel CCD which contains chip 1 and 2, where chip 1 and
chip 2 are separated by 1.2”. Our target star, HAT-P-41, has a nearby
companion separated by 3.615”, equivalent to $\approx$ 91.5 pixels on the
detector. The nearby companion resulted in a number of tight orientation
constraints on the observation. However, our sub-array is large enough to
capture both full +1 and -1 spectral orders around the 0th order trace. The
maximum flux obtained in a single pixel in the spectral trace is $\approx$
36,000 e-, keeping it well within the saturation and non-linearity limit of
the detector, which is approximately 67,000–72,000 e- (Gilliland et al.,
2010).

###  3.1 Spectral Extraction

The spectral traces for both visits and both +1 and -1 orders were extracted
using calibration files provided by the WFC3 team. A complete extraction and
reduction of the provided data requires the following steps:  a) cosmic ray
removal  b) background subtraction  c) aperture determination, and  d) trace
fitting  We then use the WFC3 UVIS calibration files to compute the wavelength
solution for each spectral order. We also performed spectral extraction with
IRAF and custom IDL routines as a second check on the extraction procedure as
this is the first published analysis of G280 grism data for time-series
spectroscopy (see 3.1.1 for details).

##### Cosmic ray removal

We use the “flt” files from the Calwfc3 pipeline to analyze each exposure.
Cosmic rays were then rejected by examining the time series for each pixel,
and flagging and replacing 3.5-$\sigma$ outliers in an iterative process. We
also applied a further spatial cosmic ray cleaning step by rejecting cosmic
rays through Laplacian Edge Detection (van Dokkum, 2001). We do a final cosmic
ray cleaning on the extracted 1D stellar spectra by comparing them to the
median combined spectra and replacing outliers which deviated by more than 3.5
$\sigma$. Where cosmic rays are flagged temporally we replace the pixel value
with a median of the time sampled pixel, where cosmic rays are spatially
flagged a median of the surrounding pixels in the same frame is used.

Figure 2: Modal background count for each exposure in visit 1 (solid) and
visit 2 (dashed) across the whole sub-array. The dotted vertical lines
indicate the start of a new HST orbit. The background is higher at the start
of each HST orbit with a bi-model distribution, perhaps due to stray
Earthshine or orbital effects on the telescope.

##### Background subtraction

We use the local background estimation, similar to WISE (see section 4.4 c
Cutri et al. 2012 222WISE All-sky release explanatory supplement, Section 4.4
c), by computing the pixel mode for each image and subtracting that from each
pixel. The mode, or most common binned histogram value, tends to be robust
against the bright tail of the distribution that is caused by other stars and
cosmic-ray (or hot) pixels in the exposure. We compared this to the mean and
median sigma clipped pixel values and found good agreement, giving weight to
the mode being resistant to outliers. In each visit the first exposure of each
orbit has much higher background than the other exposures with a slightly bi-
modal distribution around the peak of the histogram (see Fig. 2), perhaps due
to stray Earthshine or orbital effects on the telescope. We remove the first
exposure of each orbit in both visits in the lightcurve analysis.

Figure 3 shows the visual difference between the original “flt” images and a
cleaned-background-subtracted exposure. We save the cleaned and background
subtracted images as fits files to be used for the trace fitting routines.

Figure 3: HST WFC3 UVIS/G280 spectral image. Top: “flt” file processed and
available on the MAST archive. Bottom: cleaned file with cosmic rays and hot
pixels removed, and flat fielding applied. In this comparison you can clearly
see the difference between the original and cleaned data demonstrating the
requirement for accurate and precise treatment of detector artifacts and
cosmic ray hits.

##### Trace fitting

To extract the target spectrum using the provided calibration files for UVIS
G280333G280 UVIS grism files, the sub-array image needs to be re-embedded into
the full frame (Rothberg et al., 2011). This can be done using the embedsub
routine in wfc3tools444https://github.com/spacetelescope/wfc3tools. This
routine also requires the “spt” files be downloaded from the MAST database and
contained within the same folder as the cleaned fits files generated from the
previous steps.

Figure 4: HST WFC3 UVIS/G280 spectral image. Top: visit 1 +1 spectral order
(left) and -1 spectral order (right). Bottom: visit 2 +1 spectral order (left)
and -1 spectral order (right). All images are background subtracted and cosmic
rays have been removed. The dotted black line shows the calculated trace
center, with the extent of the +-12 pixel aperture shown in orange dashed
lines. At lower flux values the spectral trace does not fit quite as well but
the full flux is captured inside the selected aperture. Color shows flux,
truncated at 25 e-s-1.

Direct images of the target were taken with the F300X filter at the start of
each visit to provide an accurate location of our target on the detector.
Visits 1 and 2 were positioned on the detector within 1 pixel of each other
with x, y centroid positions of [2040.8756, 1063.8825] and [2041.0399,
1062.9073] respectively.

Using the description of the spectral trace of the G280 UVIS grism from
Pirzkal et al. (2017), we computed the expected location of the trace in each
exposure of our G280 datasets. In summary, Pirzkal et al. (2017) compute the
trace location as a function of the x-pixel on the detector and a high order
2D polynomial is fit across the trace. The best fit trace is defined by a 6th
order polynomial function with a linear 2D field dependence. The reference
column for the polynomial fit is chosen to be close to the inflection point of
the trace to ensure the best fit to both the highly curved spectrum at short
wavelengths and the near-linear trace at longer wavelengths. The polynomial
function reproduces the position of both the +1 and -1 spectral orders to
within a fraction of a pixel from 200–800 nm. Figure 4 shows the central trace
position for both visits and computed for the +1 and -1 spectral orders. The
trace fits are currently best calibrated to the +1 order, however, the authors
note that there is a new WFC3/UVIS G280 calibration program that will fully
characterize the -1 and additional spectral orders. At longer wavelengths,
toward the tail end of the spectral trace, fringing effects come into play
that divert the spectra from the fit polynomial trace (see Fig. 5).

A simple extraction of the spectrum contained in each dataset was created by
adding up the observed count rates in pixels above and below the computed
location of the trace. We tested apertures ranging from $\pm$5 pixels around
the central trace to $\pm$50 pixels. To determine the best aperture we
minimized the standard deviation of the residuals for out-of-transit counts.
We find that the optimal aperture is $\pm$12 pixels (see Fig. 4), to account
for the slightly extended wings of the trace (Kuntschner et al., 2009). Both
the +1 and -1 spectra orders were processed in this manner.

The overlapping spectral orders are expected to impact the spectrum in the
long wavelengths approximately beyond 400 nm. However, these observations were
not ideal to show the impact of overlapping spectral orders as the brightness
of the star in the shorter wavelengths is too dim, $\approx$65$\times$ dimmer
than the first order trace. We discuss potential corrections to this in more
detail in § 5.

##### Wavelength solution

The wavelength solution is calculated from the trace position using the
equation detailed in Pirzkal et al. (2017) which is calibrated from 190 to 800
nm. The extracted wavelength solution is good to +/- 0.7 nm which is roughly
half of a UVIS resolution element. We measure the mean spectral dispersion in
the first order which varies from $\sim$1.1–1.6 nm per pixel over the full
spectral range 200–800 nm.

We plot the stellar spectra for both visits and first order spectra in Fig. 5,
showing the 16-84 percentile range of each spectrum with remarkable agreement
between visits, demonstrating the stability of the instrument. Beyond 800 nm
the target spectrum shows extreme fringing effects and is not calibrated, thus
we remove it from this analysis. It is also clear to see that the -1 order is
significantly dimmer across the whole wavelength range with a large impact on
the short wavelengths, short of 250 nm, where the flux drops to near-zero.

Figure 5: The 16–84 percentile range of each visit and orders spectral trace.
The +1 orders and -1 orders overlap closely, making it difficult to tell the
two visits apart and demonstrating the stability of the instrument and the
star. The -1 orders are $\sim$50% dimmer than the +1 orders, with little to no
flux short of 250 nm. Above 800 nm fringing patterns can clearly be seen in
the stellar spectra and we do not use these wavelengths for the lightcurve
analysis.

####  3.1.1 IRAF APALL Spectral Extraction

We also performed spectral extraction with IRAF and custom IDL routines. The
images were first background subtracted and cosmic rays were removed in the
same way as detailed above. We then used IRAF’s APALL routine to extract the
spectra for each image in the time series, finding an 8th order legendre
polynomial was optimal for the spectral trace extraction as measured by the
trace root mean square residuals. We note that with IRAF, the fixed aperture
center varies smoothly to follow changes in the position of the spectrum
across the dispersion axis and partial pixels are used at the ends. We
extracted the spectra with a wide range of aperture sizes, finding a 24 pixel
aperture was optimal. Similar to the UVIS calibration pipeline routines, the
extracted spectra still exhibited a few cosmic rays not cleaned in previous
processes, we also then perform the 1D stellar spectra cosmic ray removal
step. Using IRAF APALL we were unable to replicate the wavelength solution
calculation and therefore used the one calculated following Pirzkal et al.
(2017) that required the trace fitting following the UVIS calibration
pipeline.

Both spectral extraction techniques produce near identical stellar spectra and
transmission spectra. However, in the following sections we adopt and present
the analysis based on the spectra extracted using the UVIS calibration
pipeline as it is widely accessible, publicly available extraction method that
does not rely on proprietary custom routines, and has a fully consistent
wavelength solution.

##  4 Broadband white-light analysis

Prior to measuring the transmission spectrum of HAT-P-41b, we first analyze
the broadband white lightcurve from 200–800 nm. In this section we detail the
analysis of the broadband whitelight transit depth measured in the UVIS G280
transits for each visit and spectral order based on two different systematic
treatment methods - instrument systematic marginalization (Wakeford et al.,
2016) and jitter decorrelation (Sing et al., 2019).

##### Instrument systematic marginalization

uses a pseudo-stochastic grid of corrective systematic models to measure the
desired lightcurve parameters, namely the transit depth, via an evidence-based
weight assigned by the data to each potential systematic model. We run a grid
of 50 systematic models in the form of an extended polynomial;

$S(\mathbf{x})=t_{1}\phi_{t}\times\sum^{n}_{i=1}p_{i}\phi^{i}_{HST}\times\sum^{n}_{j=1}l_{j}\delta^{j}_{\lambda}+1$
(2)

where $\phi_{t}$ is the planetary phase representing a linear slope over the
whole visit, $\phi_{HST}$ is the HST orbital phase accounting for “HST thermal
breathing” effects, and $\delta_{\lambda}$ is the positional shift in the
wavelength direction on the detector over the visit. Each of these parameters
have scaling factors with the linear slope defined by $t_{1}$, and “HST
breathing” and positional shifts fit up to a 4th order polynomial function
defined by $p_{1-n}$ and $l_{1-n}$, respectively. Each of the scaling
parameters are then either fit as free parameters to activate the systematic
model or fixed to zero. The whole grid of 50 systematic models used in this
analysis can be found in Table 2 of Wakeford et al. (2016) note the table is 0
indexed.

We approximate the evidence (marginal likelihood) of each systematic model fit
to the data using the Akaike Information Criterion (AIC). We then calculate
the evidence-based weight ($W_{q}$) across all 50 systematic models and use
the information from all models to marginalize over the desired parameter
($\alpha_{q}$).

$\alpha_{m}=\sum^{N_{q}}_{q=0}(W_{q}\times\alpha_{q})$ (3)

Equation (15) of Wakeford et al. (2016), where $N_{q}$ is the number of models
fit, and $\alpha_{m}$ is the resulting marginalized parameter. The uncertainty
is then calculated in a similar way based on the weights (see Equation (16) of
Wakeford et al. 2016).

Figure 6: Spectral position changes over the course of each visit, measured by
cross-correlating to a template spectrum (points, Cc), and by fitting
background sources on the full exposure image (shaded regions, stars). Each
are shown relative to the final exposure for comparison. The spectral shifts
are accounted for in the systematic treatment of each lightcurve. Figure 7:
Mean flux in Spitzer’s 3.6 $\mu$m channel. Plotting on a logarithmic scale
reveals HAT-P-41’s faint, nearby companion at pixel position (12,15). We limit
our photometry aperture size to 2.25 pixels to minimize contamination from the
companion. Bad pixels are masked in white.

##### Jitter decorrelation

uses HST’s Pointing Control System to detrend photometric time-series data.
Based on the results of (Sing et al., 2019), we include optical state vectors
traditionally used for STIS (Sing et al., 2011) as well as several jitter
vectors. The full systematics model, $S(\mathbf{x})$, used to detrend the
lightcurve is written as,

$S(\mathbf{x})=p_{1}\phi_{t}+\sum^{4}_{i=1}p_{i+1}\phi^{i}_{HST}+p_{6}\delta_{\lambda}\\\
+p_{7}X_{psf}+p_{8}Y_{psf}+p_{9}RA+p_{10}DEC\\\
+p_{11}Vn_{roll}+p_{12}Vt_{roll}+1,$ (4)

where $\phi_{t}$ is a linear baseline time trend, $\phi_{HST}$ is the 96
minute HST orbital phase, $X_{psf}$ and $Y_{psf}$ are the detector positions
of the PSF as measured by the spectral trace, $\delta_{\lambda}$ is the
wavelength shift of each spectra as measured by cross-correlation, $V2\\_roll$
and $V3\\_roll$ are roll of the telescope along the V2 and V3 axis, $RA$ and
$DEC$ are the right ascension and declination of the aperture reference, and
$p_{1..12}$ are the fit systematic parameter coefficients. The first portion
of this function was found to be the best functional form of the additional
systematic features and corresponds to one of the models used in the
marginalization grid. This function is then fit for all transit lightcurves in
this form and is not marginalized over to determine the optimal functional
form in each lightcurve. The full jitter decorrelation set results in up to
twelve total terms used to describe the instrument systematics of the dataset
in question. However, in practice not all of these parameters are needed. For
each visit and each of the two orders, we used the AIC and measured red noise,
$\sigma_{\rm r}$, to determine the optimal optical state vectors to include
from the full set without over-fitting the data and minimizing the red noise.

Both systematic marginalization and jitter decorrelation require a measurement
of the spectral positional changes on the detector across the duration of the
observation ($\delta_{\lambda}$). To calculate the shift, we cross-correlate
the 1D stellar spectra to a template spectrum and measure the displacement
across the whole wavelength range. To demonstrate that this accurately
represents the physical shift on the detector, we measured the position for
three background sources distributed across the exposure image. We selected
the most Gaussian-like sources from the full image and used a 2D-Gaussian fit
to their 0th order spectrum in each exposure of each visit. In this case we
cannot use the 0th order of the target or its stellar companion to measure
this shift as they are both saturated on the detector. Figure 6 shows
$\delta_{\lambda}$ for visits 1 and 2 measured using the cross-correlation
method (Cc) and the range of positional values measured from the three
background sources (stars). The form of the positional shifts are very similar
with the vertical breaks showing where the telescope is reset after each HST
orbit. The magnitude of the positional shifts is on the sub-pixel scale and is
easily accounted for with either of the systematic treatments detailed. Using
the 2D-Gaussian fit to the background sources, we find that positional shifts
in the y-direction are negligible and do not improve the fit to the data.

Due to the phase coverage of HST observations, resulting from Earth
occultation events, we are unable to accurately fit for the inclination, a/R∗,
and orbital period of the system. Unfortunately, HAT-P-41b was not observed by
the Transiting Exoplanet Survey Satellite (TESS) which would have allowed us
to easily constrain the system parameters. To fit for these vital parameters
we instead use two transit observations from the Spitzer Space Telescope IRAC
instrument to obtain accurate system parameters for the inclination and a/R∗
of HAT-P-41b, detailed in §4.1. In §4.2 we present the measured center of
transit times for these and previous transit observations of HAT-P-41b to
determine the period of the planet, and in §4.3 we present the results of the
UVIS G280 broadband lightcurves for the two visits and for each spectroscopic
order using both systematic treatments.

Figure 8: Transit light curves of HAT-P-41b using Spitzer’s 3.6 $\mu$m (left)
and 4.5 $\mu$m (right) channels. We bin the data for plotting purposes only.
The 3.6 $\mu$m residuals demonstrate a small amount of correlated noise at
timescales shorter than the transit duration.

###  4.1 Spitzer Data Analysis

Spitzer program 13044 (PI: Deming) acquired transit observations of HAT-P-41b
at 3.6 and 4.5 $\mu$m on 2017 January 18 and 2017 February 3, respectively.
The IRAC instrument (Fazio et al., 2004) acquired 32$\times$32 pixel subarray
frames at 2 second intervals in batches of 64. Each observation acquired a
total of 21,632 frames over a span of $\sim$12 hours.

Using the POET pipeline (Stevenson et al., 2012a; Cubillos et al., 2013), we
apply a double-iteration, $4\sigma$ outlier rejection routine, 2D Gaussian
centroiding, and $5\times$ interpolated aperture photometry over a range of
aperture sizes. We convert times to BJDTDB using the JPL Horizons interface.

We find that the best aperture size (as defined by the lowest standard
deviation of the normalized residuals) is 3.0 pixels; however, at this size
there is noticeable contamination from the nearby binary companion. This is
evidenced by the correlation between aperture size and transit depth
(significant at $3.3\sigma$). HAT-P-41’s stellar companion is located $\sim 3$
pixels away, in the wings of the primary star’s point response function. This
is shown in Figure 7, where we depict the mean flux at 3.6 $\mu$m on a
logarithmic scale. We find that the impact of the stellar companion on the
measured transit depth is minimal ($<1\sigma$) for apertures $\leq 2.25$
pixels and, thus, adopt this value for our final analyses. We note that the
transit time, inclination, and semi-major axis parameters do not vary with our
choice of aperture size.

To derive our best-fit values (see Tables 1 and 2), we fit both Spitzer
channels simultaneously using the transit model described by Mandel & Agol
(2002), a linear trend in time, and a BLISS map (Stevenson et al., 2012a) to
account for intrapixel sensitivity variations. We estimate uncertainties using
the Differential-Evolution Markov Chain Monte Carlo technique (ter Braak &
Vrugt, 2008) and test for convergence using the Gelmin-Rubin statistic (Gelman
& Rubin, 1992) by ensuring that the potential scale reduction factor is within
1% of unity. Figure 8 shows Spitzer’s normalized light curves and residuals.
The best-fit 3.6 and 4.5 µm transit depths are $0.992\,{\pm}\,0.008$ % and
$1.028\,{\pm}\,0.013$ %, respectively.

Figure 9: Observed minus calculated (O-C) diagram of measured HAT-P-41b transit times. The dashed line shows the 1-sigma uncertainty. Table 1: Star and Planet parameters used in the lightcurve fitting process for this analysis. Parameter | Value | Reference
---|---|---
Star |  |
V (mag) | 11.087 | Hartman et al. (2012)
M∗ (M⊙) | 1.418 | Hartman et al. (2012)
R∗ (R⊙) | 1.786 | Morrell & Naylor (2019)
Teff (K) | 6340 | Morrell & Naylor (2019)
[Fe/H] (dex) | 0.21 | Hartman et al. (2012)
log(g) | 4.14 | Hartman et al. (2012)
Planet |  |
Mp (MJ) | 0.795 | Bonomo et al. (2017)
Rp (RJ) | 1.685 | Hartman et al. (2012)
Period (days) |  2.69404861 $\pm$0.00000092 | This work
T0 (days) |  2456600.29325$\pm$0.00050 | This work
inclination (∘) |  89.17 $\pm$ 0.62 | This work
a/R∗ |  5.55 $\pm$ 0.04 | This work
ecc | 0.0 | Bonomo et al. (2017)

###  4.2 Updated Orbital Ephemeris

We used previous and current data to calculate an up-to-date orbital period
for HAT-P-41b, including the ephemeris from the discovery (Hartman et al.,
2012), as well as HST and Spitzer transit data (see Table 2). The HST data
includes the WFC3/UVIS transits where the +1 and -1 orders were treated
independently (see §4.3), as well as WFC3/IR and STIS transits from the Hubble
PanCET program (GO-14767, PIs D.K. Sing & M. Lopez-Moralez, Sheppard 2020 in
prep - private communication). We converted all of the available transit times
to BJDTDB using the tools from (Eastman et al., 2010). These times were fit
with a linear function of the period $P$ and transit epoch $E$,

$T(E)=T_{0}+EP.$ (5)

The resulting ephemeris is given in Table 2, with the linear function giving a
reasonable fit to the data (see Fig. 9), with a $\chi^{2}$ value of 14.47 for
9 degrees of freedom (DOF).

Table 2: Center of transit times used in Fig.9 to calculate the period of the planetary orbit as well as the resulting best-fit orbital ephemeris. All times have been converted to BJDTBD. Instrument | Mode | Epoch | Note
---|---|---|---
|  |  (BJDTDB) (days) |
|  |  2454983.86247 $\pm$ 0.00107 | Hartman et al. (2012)
HST WFC3-IR | G141 |  2457677.912139 $\pm$ 0.0008 |
Spitzer IRAC | CH1 |  2457772.20477 $\pm$ 0.00021 |
Spitzer IRAC | CH2 |  2457788.36879 $\pm$ 0.00027 |
HST STIS | G430L |  2458001.197547 $\pm$ 0.001151 | visit 1
HST STIS | G430L |  2458246.357040 $\pm$ 0.000339 | visit 2
HST STIS | G750L |  2458281.379682 $\pm$ 0.000363 |
HST WFC3-UVIS | G280 |  2458332.566558 $\pm$ 0.000656 | Visit 1, +1 order
HST WFC3-UVIS | G280 |  2458332.564321 $\pm$ 0.001366 | Visit 1, -1 order
HST WFC3-UVIS | G280 |  2458335.260623 $\pm$ 0.000303 | Visit 2, +1 order
HST WFC3-UVIS | G280 |  2458335.259912 $\pm$ 0.000290 | Visit 2, -1 order
Period $P$ (days) |  |  $T_{0}$ (BJDTDB) (days) |
2.69404861$\pm$0.000000918 |  |  2456600.293253 $\pm$ 0.000504 |

###  4.3 UVIS G280 Broadband Lightcurve Results

Figure 10: Top: broadband lightcurves. We show the raw extracted lightcurve
for the visit 1 +1 spectral order to demonstrate the stability of the 1st HST
orbit in the time series (light grey). The systematic corrected and normalized
white lightcurves for each visit and spectroscopic order (colored labeled
points) with the best fit transit model. Each point represents a single
exposure. Each lightcurve is offset for clarity. Middle: residuals from each
ligthcurve fit using the systematic marginalization method. Bottom: residuals
for each lightcurve fit using the jitter decorrelation method. We measure the
combined transit depth of HAT-P-41b to be $(R_{p}/R_{*})^{2}$ = 1.0406 $\pm$
0.0029 % (SDNR = 221 ppm) and 1.0330 $\pm$ 0.0033 % (SNDR = 281 ppm), for each
method respectively. Figure 11: The evidence-based weight for each systematic
model used in instrument systematic marginalization for each visit and order
for the broadband lightcurve analysis. The table of systematic models relating
to each number can be found in Wakeford et al. (2016).

We measure the broadband transit depth for UVIS G280 by summing the flux from
200–800 nm and correcting for systematics via systematic marginalization and
jitter decorrelation independently for both visits and both spectral orders.
We measure a combined transit depth of all four transit timeseries
measurements of $(R_{p}/R_{*})^{2}$ = 1.0406 $\pm$ 0.0029 % and 1.0330 $\pm$
0.0033 %, with an average standard deviation on the residuals of 221 ppm and
281 ppm, using the systematic marginalization and jitter decorrelation methods
respectively. There is a 1.7$\sigma$ difference between the two methods,
likely due to the small differences between the uncertainties on each exposure
for each analysis method that can be seen by comparing the bottom two panels
of Fig. 10. In each analysis we use the same extracted stellar spectra, the
same limb-darkening coefficients derived using the 3D stellar models presented
in Magic et al. (2015), and the same system parameters shown in Table 1.

We show the four transit lightcurves (2 visits + 2 orders) corrected in Fig.
10. The lightcurves shown have been corrected using the most favored model
applied in systematic marginalization, with the underlying models derived from
the same most-likely systematic model. For both data analysis methods,
systematic marginalization and jitter decorrelation, the transit model is fit
iteratively with the systematic model to measure the transit depth. We note
that the lightcurves in Fig.10 only represents a portion of the information
obtained through marginalization as all the information from corrected data
using other weighted systematic models also go into the final marginalized
transit depth measurement (contribution weights can be seen in Fig. 11). Using
jitter decorrelation, we derive a single solution for the lightcurve
corrections and transit depth for each visit and spectral order. The
individual lightcurves from jitter decorrelation are indistinguishable by eye
compared to the systematic marginalization ones presented here. For a more
direct comparison we show the residuals of both systematic analyses at the
bottom of Fig. 10 with their related uncertainties, both achieving near photon
noise precision.

Figure 12: Intensity plot of the spectroscopic lightcurve residuals for each
wavelength bin using the systematic marginalization method. The color bar
shows the residuals amplitude for all intensity plots. For the -1 orders we do
not compute the transmission below 250 nm as the flux is too low to produce
convergent results in the systematic analysis.

While jitter decorelation uses a fixed systematic model plus the jitter files
directly from the telescope as a main decorelation factor, systematic
marginalization derives its information from evidence obtained from an array
of independent systematic models. Systematic marginalization therefore
accounts for the unknown factors affecting the lightcurves by weighting them
according to the reduced data rather than the telescopes fine guidance
sensors. Using systematic marginalization we find that each transit and
spectral order favors slightly different combinations of systematic
corrections. For visit 1 both orders predominantly favor models with a
quadratic correction to $\delta_{\lambda}$, while both orders of visit 2 favor
a 3rd order $\phi_{HST}$ correction with additional correction for
$\delta_{\lambda}$. Given the similarity in the $\delta_{\lambda}$ trend for
each visit and spectral order, as shown in Fig. 6, the more favored correction
of the HST breathing in visit 2 suggests that this movement on the detector is
likely connected with the thermal effects of the telescope and thus the
corrections themselves are interchangeable in this specific case where the
structure of the systematic is similar. For each lightcurve there is a
marginal preference to correct for a linear trend in time across the whole
visit; however, it is slightly more significant in visit 1. This linear trend
across the whole visit has been noted in several other HST timeseries
observations (e.g., Deming et al., 2013; Sing et al., 2015; Kreidberg et al.,
2014; Wakeford et al., 2016), and is thus likely related to the observatory as
a whole rather than a specific instrument. For each visit and order we show
the weighting assigned to each systematic model in the systematic
marginalization reduction for the broadband analysis in Fig.11, these model
weights are later applied to the spectroscopic lightcurves. The weights shown
correspond to the systematic models shown in Table 2 of Wakeford et al.
(2016). The structure of this grid is such that it first loops through
polynomials correcting for $\delta_{\lambda}$, followed by added corrections
for $\phi_{HST}$ with the second half of the grid (25-49) adding in
corrections for $\phi_{t}$. The overall structure of the computed weights
shows that the corrections for $\delta_{\lambda}$ are the dominant factor
given causing the loop every four models.

Figure 13: The individual and combined transmission spectra using both
systematic marginalization and jitter decorrelation. The two visits and +1/-1
spectral orders are shown as colored shaded regions representing the range of
the uncertainties for each spectrum. The final transmission spectrum combining
the results of all four are shown as joined black points with errorbars.

##  5 Spectroscopic Analysis

To measure the transit depth as a function of wavelength and produce an
atmospheric transmission spectrum for HAT-P-41b, we divide the stellar flux
into 10 nm bins ($\sim$5 detector resolution elements) from 200 – 800 nm. We
note that it is possible to sample the transmission spectrum at a higher
resolution ($>$2 resolution elements) in the most optimal portions of the
spectrum where the flux is high; however, we use uniform bins across the whole
wavelength range for consistency and accurate comparison.

We analyze each individual spectroscopic lightcurve in the same way, as
described in §4 for the broadband lightcurve, using both systematic
marginalization and jitter decorrelation methods. In jitter decorrelation, the
systematic correction model is unchanged between wavelength bins, thus
assuming all systematics are wavelength independent. Using systematic
marginalization, we account for any wavelength dependent systematics by
running the full grid of systematic models in each spectroscopic lightcurve.
We then use the evidence based weights for each of those models measured in
the broadband lightcurve (see Fig 11) to marginalize over the measured values
for each model in each lightcurve. By fixing the systematic model weighting to
those derived from the broad-band analysis, the uncertainty is then more
representative of the dominant wavelength independent systematics while
incorporating the scatter measured across wavelength dependent systematics
being fit to the data.

Figure 14: Direct comparison of the final combined transmission spectrum for
each systematic treatment: jitter decorrelation (dark squares) and systematic
marginalization (light circles). The horizontal dashed lines show the measure
broadband depth using each method.  Figure 15: The standard deviation between
the four individual transmission spectra in each wavelength bin for systematic
marginalization (pink) and jitter decorrelation (purple).

Each visit and +1/-1 spectral orders were analyzed separately using the
parameters detailed in Table 1 fixing the period, inclination, and a/R∗, and
using the center of transit times listed in Table 2. Using both jitter
decorrelation and systematic marginalization independently, we find consistent
results across both visits and spectral orders. Both methods reach photon
noise precision in each of the channels determined by calculating the white
and red noise associated with the fits (see Pont et al. 2006), and finding a
beta value of 1 consistent with no correlated noise. We show the residuals
from each of the spectroscopic lightcurves for the systematic marginalization
analysis in Fig. 12 as an intensity residual map to show any global structure
in the fit. From the residuals it is clear that the -1 order lightcurves are
noisier than the +1 orders. There is also an increase in the scatter at the
edges of the wavelength regime, with shorter wavelengths dominating the
overall noise range associated with the pure count rates measured from the
stellar spectrum in each of the bins (see Fig. 5).

In Fig. 13, we present the transmission spectrum measured using both methods
for each visit and each +/- first order spectrum with the combined
transmission spectrum overlaid. We show a direct comparison between the
combined transmission spectrum measured using the two systematic treatments in
Fig. 14, with 90% of the points overlapping at the 1-$\sigma$ uncertainty
level. A direct comparison between the two methods is best demonstrated by
looking at the standard deviation and uncertainty in the transit depth
measured across the four transits analyzed (see Fig. 15). It is again evident
in the standard deviations and uncertainties that the lower counts measured in
the near-UV wavelengths ($<$300 nm) introduce larger scatter and uncertainty
to the transit depths. The standard deviation in the short wavelengths
indicates that that derived transit depths in each lightcurve are more similar
within the uncertainties using systematic marginalization compared to the
Jitter decorrelation method. However, there is added scatter with the
marginalization method at longer wavelengths. Both methods have similar
uncertainty profiles indicating the ability to analyse these data with
multiple methods. The unique contribution of the UV points to the transmission
spectrum of an exoplanet atmosphere in combination with the optical from a
single observation with this low-resolution grism cannot be overstated.

##  6 Discussion

We present HST’s WFC3/UVIS G280 grism as a reliable observational mode to
measure the transmission spectrum of exoplanet atmospheres from 200–800 nm,
critically reaching down to near-UV and optical wavelengths not accessible to
JWST. This wavelength range is important to understand and measure cloud
opacity sources and their scattering profiles that are defined by the particle
sizes (e.g., Lecavelier des Etangs et al. 2008; Wakeford & Sing 2015; Wakeford
et al. 2017), escaping atmospheres (e.g., Ehrenreich et al. 2014; Sing et al.
2019), and absorption from Na.

To test this new mode, we measured the atmosphere of the hot Jupiter HAT-P-41b
over the course of two consecutive transits with the WFC3/UVIS G280 grism. We
obtained the positive and negative first order spectra of the target star in
each observation and extracted the stellar flux following the methods outlined
by the UVIS calibration pipelines (Kuntschner et al., 2009; Rothberg et al.,
2011; Pirzkal et al., 2017). We analysed the transit data for each visit and
spectral order using two well established techniques, instrument systematic
marginalization (Wakeford et al., 2016) and jitter decorrelation (Sing et al.,
2019). Both analysis techniques produced statistically similar transmission
spectra for the atmosphere of HAT-P-41b. We obtain a precision of 29–33 ppm on
the broadband transit depth from 200–800 nm, and an average precision of
$\approx$200 ppm in 10 nm spectroscopic bins.

##### Comparison to STIS Observations

Figure 16: Transmission spectrum of HAT-P-41b measured with WFC3/UVIS G280
grism using systematic marginalization combining two HST observations (pink),
compared to STIS G430L combined spectra from two HST observations (dark green)
and one observation with the HST STIS G750L grating (Sheppard, 2020 in prep -
private communication). The WFC3/UVIS G280 grism is able to efficiently
measure the atmosphere of a transiting exoplanet from 200–800 nm to high
precision, matching and exceeding that of STIS.  Figure 17: Transmission model
fit using the planetary specific grid with rainout condensation by Goyal et
al. (2018). Both the jitter decorrelated and systematic marginalization G280
spectra were fit independently with the Spitzer data to the full grid of
HAT-P-41b models. Both datasets found the same best fit model with Teq = 2091
K, [M/H] = +2.0, C/O = 0.7, 1100$\times$scattering, cloud = 0.2.

We compare the transmission spectrum measured of HAT-P-41b with WFC3/UVIS G280
grism to that measured using STIS G430L and G750L gratings. We find that the
combination of the two HST observations in the G280 UVIS grism results in
resolution and precision exceeding that of STIS, which required the
combination of three HST observations to cover the whole wavelength range
compared to two for UVIS. Figure 16 shows the transmission spectrum derived
using systematic marginalization from two transits with UVIS G280 compared to
the transmission spectrum from three transits with STIS G430L and G750L
presented by Sheppard (2020 in prep - private communication).

Assessing the overall use of UVIS G280 over the STIS gratings, there are a
number of trade offs to consider. As G280 cannot be scanned and the throughput
is much higher it will likely be more difficult to observe bright (Vmag $<$ 7)
targets, especially considering the impact of overlapping spectral orders that
will make it difficult to extract individual spectral bins at this resolution.
Therefore, bright targets will be more efficiently observed with STIS/G430L in
particular. Additionally, although UVIS G280 can efficiently measure a wide
wavelength range in a single observation it does not extend to wavelengths
spanning the potassium absorption line that can only be accurately captured
with the STIS G750L grating. However, the extended wavelength coverage into
the UV compared to the G430L grism and the comparable resolution means that a
potential Na line can be resolved just as easily with UVIS as with STIS but
with potentially higher precisions in UVIS. The measured UVIS spectrum far
exceeds the resolution and precision over the comparative wavelengths than can
be achieved by STIS/G750L (see Fig. 16).

This direct comparison for the same planet demonstrates that the UVIS G280
grism can easily exceed the precision and resolution of STIS in an equivalent
number of observations, while being more efficient and requiring less
observing time. UVIS G280 also has the advantage of spanning the whole
wavelength range in one shot, dramatically reducing the potential impact of
stellar activity and systematics which can cause offsets between datasets from
different instrument modes. In summary, for targets with Vmag $\geqslant$ 7
the UVIS G280 grism shows reduced systematics, higher resolution, precision,
and wavelength coverage with more efficient observing compared to STIS G430L
and G750L gratings.

##### Searching for Evidence of Atmospheric Escape

The UVIS G280 grism has ideal wavelength coverage to search for signatures of
atmospheric escape of the Fe II at 240 nm and 260 nm, and the prominent Mg II
doublet at 279.63 nm. A single resolution element for the G280 grism is
$\sim$2 nm which encompasses the whole Mg II doublet absorption line, thus
limiting us to strong, low resolution detections. At a single resolution
element of the detector, the scatter becomes large and we were unable to
converge on a solution to fit the lightcurve systematics. We therefore
conducted an analysis of the HAT-P-41b transit data in 4 nm bins (2 resolution
elements) across the 230 – 290 nm, with individual moving analyses in 10 nm
steps to search for excess absorption from escaping ions. In this analysis, we
find little significant evidence for additional absorption by Fe II and Mg II
in the atmosphere. In a single 4 nm bin centred at 280 nm we measure
additional 0.2% absorption compared to the average transit depth which could
potentially correspond to Mg II. However, this absorption is not seen in bins
centered 10 nm either side of 280 nm that encompass the peak of the
absorption. The scatter is on the order of 0.3% across the whole sampled
range.

We conducted our search predominantly using the positive spectral orders for
each visit as the throughput and flux levels are high enough for the precision
needed at these wavelengths. However, for strong signatures such as those seen
in WASP-121b (Sing et al., 2019) or KELT-9b (Hoeijmakers et al., 2018), which
also orbit bright stars, the absorption signature will likely also be
measurable in the negative order spectra as well. We conclude that there is no
evidence of significant Fe II and Mg II escaping from the atmosphere of
HAT-P-41b based on the precision of these measurements. However, we cannot
currently conclude where this places HAT-P-41b in the comparative phase space
as more measurements with this mode or similar to that shown in Sing et al.
(2019) will be required over a wide temperature phase space to examine the
likelihood of detection.

##### Planetary Specific Model Comparison

We ran each of the transmission spectra including the measured Spitzer transit
depths through the planetary specific forward model grid for HAT-P-41b using
rainout condensation presented by Goyal et al. (2018, 2019). In each case, the
model fits have the same number of degrees of freedom with the only additional
fitting parameter being the absolute altitude of the model. For each UVIS G280
spectrum, we trim the first and last two data points that are likely most
affected by low flux and fringing, respectively, and append on the Spitzer
transit depths. Each transmission spectrum independently favors the same
atmospheric model that has: Teq = 2091 K, atmospheric metallicity [M/H] =
+2.0, C/O = 0.7, 1100$\times$scattering profile, and uniform cloud opacity =
0.2 (see Fig. 17). We find a $\chi^{2}_{\nu}$ = 1.45 and 1.72 when fitting the
most favored model to the jitter decorrelated and marginalized transmission
spectrum, respectively.

The model shows prominent TiO/VO features in the near-UV fitting the UVIS G280
data well in the optical with a wavelength dependent slope associated with a
scattering opacity source composed of small sub-micron particles. This model
predicts a muted H2O feature in the near-IR that would be detectable with
WFC3’s G102 and G141 grisms. The Spitzer IR is dominated by CO2 that would add
additional constraints on the atmospheric metallicity (Moses et al., 2011) and
can be validated by JWST NIRSpec observations.

##  7 Conclusions

We present HST’s WFC3/UVIS G280 grism as a new and ideal instrument mode for
exoplanet time-series characterisation. This is the first time scientific
analysis of any observation with this instrument mode has been published. As
such, we provide a detailed breakdown of the challenges and advantages of the
instrument, detailed instructions on the spectral extraction with reference to
data files and programs provided through UVIS calibration files, and a
comparative study of two well established systematic reduction methods.

To test the UVIS G280 grism for time-series data, we observed the transit of
the hot Jupiter HAT-P-41b over two consecutive transit events. This allowed us
to measure the overall stability of the instrument, the precision, and
resolution without additional concerns associated with potential stellar
activity. We obtained both positive and negative first order spectra from each
observations providing four different datasets from 200–800 nm. We analysed
each dataset separately before combining the information to produce the final
atmospheric transmission spectrum of HAT-P-41b. We applied two different
extraction and systematic analysis techniques to the data and find them to be
statistically similar across the whole transmission spectrum demonstrating the
robust and consistent nature of the instrument critical for accurate exoplanet
transmission spectral studies.

We measure the complete transmission spectrum of the hot Jupiter HAT-P-41b
from 200–800 nm in 10 nm bins and at 3.6 and 4.5 $\mu$m with Spitzer’s IRAC
instrument. In the broadband UVIS lightcurves, we reach a precision of 29-33
ppm, with an average of $\approx$200 ppm in 10 nm wide spectroscopic channels.
The transmission spectrum shows evidence of TiO/VO in the near-UV to optical
with significant absorption from CO2 in the Spitzer 4.5 $\mu$m channel. We fit
a grid of forward models specifically derived for HAT-P-41b to the
transmission spectrum from multiple reduction pipelines and find constant
results with a Teq = 2091 K, [M/H] = +2.0, C/O = 0.7, scattering $\times$1100,
and cloud opacity = 0.2 for rainout condensation (see Goyal et al. 2018,
2019). Additional measurements in the near-IR will further aid the
interpretation of this planets atmospheric transmission and will be detailed
in future publications.

We demonstrate that Hubble’s WFC3 UVIS G280 grism is superior to the
combination of STIS G430L and G750L gratings for time-series observations in
terms of efficiency, precision, and resolution from 300–800 nm for exoplanet
time-series observations. Notably the UVIS G280 grism also allows access to
wavelengths as short as 200 nm with the potential to measure the escaping
atmosphere of giant exoplanets via Fe II and Mg II absorption lines and a
broad of range of other atmospheric processes. The wavelength coverage offered
by the UVIS G280 grism (200–800 nm) provides a perfect complement to the
spectroscopic capabilities of the James Webb Space Telescope (600–14000 nm),
which together can probe the full extent of atmospheric processes in
exoplanets that closely orbit their host star.

## Acknowledgements

We thank D. Deming for use of data from his Spitzer program 13044 that
provided the two transits used to obtain accurate system parameters. Thanks to
S. Morrell for discussions on the stellar parameters with updates from Gaia.
We acknowledge private communication from N. Nikolov and K. Sheppard for the
STIS data analysis from the Hubble PanCET program (Sheppard, 2020 in prep -
private communication).

This work is based on observations made with the NASA/ESA Hubble Space
Telescope, https://archive.stsci.edu/hst/search.php (catalog HST-GO-15288),
that were obtained at the Space Telescope Science Institute, which is operated
by the Association of Universities for Research in Astronomy, Inc. A portion
of this work is based on observations made with the Spitzer Space Telescope,
which is operated by the Jet Propulsion Laboratory, California Institute of
Technology under a contract with NASA. H.R. Wakeford acknowledges support from
the Giacconi Prize Fellowship at the Space Telescope Science Institute, which
is operated by the Association of Universities for Research in Astronomy, Inc.

Author contributions: H.R. Wakeford led the UVIS data analysis with detailed
comparisons provided by D.K. Sing. N. Pirzkal provided the UVIS calibration
pipeline and knowledge of the instrument. K.B. Stevenson analysed the Spitzer
data and helped with discussions on the UVIS analysis. N.K. Lewis aided with
the interpretation of the results and providing context for the observations.
T.J. Wilson aided in the statistical analysis. All authors provided text and
comments for the manuscript.

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Table 3: Transmission spectrum of HAT-P-41b based on the combined spectrum of +1 and -1 spectral orders over two transt events. for both systematic marginalization and jitter decorrelation  | Systematic marginalization | Jitter Decorrelation | Limb-darkening coefficients∗
---|---|---|---
Wavelength | Transit depth | Uncertainty | Transit depth | Uncertainty | c1 | c2 | c3 | c4
(nm) | (%) | (%) | (%) | (%) |  |  |  |
200-800 | 1.04056 | 0.00293 | 1.03303 | 0.00331 |  |  |  |
205 | 1.07731 | 0.14700 | 0.85089 | 0.11217 | 0.27642 | -0.27861 | 0.17437 | 0.81199
215 | 1.04749 | 0.09769 | 1.04495 | 0.06266 | 0.44951 | -1.31326 | 2.39714 | -0.53880
225 | 1.11641 | 0.06163 | 0.97186 | 0.04573 | 0.26022 | -0.34716 | 0.79494 | 0.26854
235 | 0.95137 | 0.08046 | 1.02503 | 0.05374 | 0.44189 | -0.57725 | 1.16157 | -0.07411
245 | 1.09086 | 0.03881 | 0.94739 | 0.04950 | 0.54045 | -1.11394 | 2.37372 | -0.82917
255 | 0.93706 | 0.08189 | 0.98717 | 0.05082 | 0.48574 | -0.52757 | 1.52725 | -0.54102
265 | 1.05554 | 0.07504 | 1.01539 | 0.03792 | 0.33614 | -0.12161 | 1.23718 | -0.49873
275 | 0.92522 | 0.06059 | 1.01556 | 0.04516 | 0.51787 | -0.22060 | 0.71649 | -0.07533
285 | 0.95923 | 0.04902 | 0.97523 | 0.03401 | 0.47085 | -0.29824 | 1.30007 | -0.53323
295 | 1.00307 | 0.02041 | 1.00242 | 0.02578 | 0.43969 | -0.17729 | 1.45827 | -0.79410
305 | 1.06577 | 0.01350 | 1.03449 | 0.02395 | 0.40399 | 0.12345 | 0.91689 | -0.53110
315 | 0.99512 | 0.02188 | 0.97180 | 0.02567 | 0.34789 | 0.38909 | 0.43002 | -0.26221
325 | 1.05841 | 0.01795 | 1.01076 | 0.01979 | 0.41584 | 0.42379 | 0.33271 | -0.27752
335 | 0.98227 | 0.01837 | 1.00338 | 0.01746 | 0.32808 | 0.83687 | -0.47180 | 0.19469
345 | 0.99562 | 0.01689 | 0.98657 | 0.02122 | 0.50090 | 0.47375 | -0.04764 | -0.04352
355 | 1.03245 | 0.01882 | 0.99715 | 0.02232 | 0.49485 | 0.49152 | -0.08977 | -0.02466
365 | 1.00155 | 0.01805 | 1.00064 | 0.01927 | 0.49130 | 0.55355 | -0.23093 | 0.04401
375 | 1.00250 | 0.01921 | 1.01414 | 0.02044 | 0.51901 | 0.65211 | -0.42396 | 0.11740
385 | 1.04525 | 0.01217 | 0.99727 | 0.01692 | 0.44683 | 0.40462 | 0.26623 | -0.24303
395 | 0.93250 | 0.03636 | 1.03279 | 0.02120 | 0.48246 | 0.16780 | 0.44794 | -0.21622
405 | 1.03092 | 0.01341 | 1.02334 | 0.01455 | 0.48142 | 0.45253 | 0.03247 | -0.08316
415 | 1.01568 | 0.01890 | 1.00675 | 0.01593 | 0.43310 | 0.43124 | 0.12149 | -0.10687
425 | 1.03996 | 0.01103 | 1.01964 | 0.01239 | 0.47865 | 0.31563 | 0.19400 | -0.11728
435 | 1.02160 | 0.00993 | 1.03207 | 0.01504 | 0.50686 | 0.42439 | -0.02042 | -0.06239
445 | 1.02758 | 0.01111 | 1.01087 | 0.01519 | 0.62723 | 0.00768 | 0.49756 | -0.26869
455 | 1.03755 | 0.01292 | 1.03748 | 0.01523 | 0.66796 | -0.03296 | 0.39110 | -0.16936
465 | 1.03632 | 0.00862 | 1.04188 | 0.01242 | 0.64089 | 0.10690 | 0.17586 | -0.07282
475 | 1.03320 | 0.01313 | 1.01325 | 0.01207 | 0.68519 | 0.04928 | 0.14144 | -0.03468
485 | 1.02089 | 0.01256 | 1.02408 | 0.01507 | 0.79901 | -0.19234 | 0.36598 | -0.16162
495 | 1.04370 | 0.01210 | 1.02236 | 0.01338 | 0.77433 | -0.20489 | 0.40845 | -0.15622
505 | 1.03363 | 0.01274 | 1.00958 | 0.01437 | 0.71140 | -0.05044 | 0.22921 | -0.08213
515 | 1.06672 | 0.01122 | 1.03961 | 0.01399 | 0.64107 | -0.00460 | 0.31643 | -0.15507
525 | 1.02409 | 0.01215 | 1.01568 | 0.01591 | 0.73764 | -0.16794 | 0.39393 | -0.17123
535 | 1.03226 | 0.00823 | 1.03075 | 0.01383 | 0.79821 | -0.32921 | 0.54626 | -0.23226
545 | 1.05347 | 0.01105 | 1.04898 | 0.01551 | 0.82715 | -0.38564 | 0.54019 | -0.20221
555 | 1.04829 | 0.01132 | 1.06219 | 0.01434 | 0.81141 | -0.35516 | 0.49708 | -0.18347
565 | 1.04683 | 0.01666 | 1.02657 | 0.01682 | 0.82890 | -0.41148 | 0.54895 | -0.20500
575 | 1.03509 | 0.01478 | 0.98730 | 0.01769 | 0.84535 | -0.43068 | 0.52234 | -0.18103
585 | 1.07381 | 0.01539 | 1.05872 | 0.01698 | 0.83910 | -0.44512 | 0.56000 | -0.20974
595 | 1.04651 | 0.01203 | 1.04453 | 0.01652 | 0.88137 | -0.54670 | 0.63637 | -0.22889
605 | 1.04134 | 0.01189 | 1.03169 | 0.01478 | 0.88844 | -0.57180 | 0.65069 | -0.23386
615 | 1.03707 | 0.01348 | 1.00632 | 0.01922 | 0.81257 | -0.38094 | 0.42040 | -0.13042
625 | 1.01444 | 0.01252 | 0.98434 | 0.01837 | 0.87418 | -0.59117 | 0.70278 | -0.27158
635 | 1.01662 | 0.01658 | 1.02845 | 0.01979 | 0.87714 | -0.58646 | 0.66610 | -0.24723
645 | 1.02919 | 0.01724 | 1.00875 | 0.01949 | 0.86284 | -0.56322 | 0.65497 | -0.25552
655 | 1.06727 | 0.01847 | 1.05593 | 0.02030 | 0.95153 | -0.74127 | 0.72558 | -0.26379
665 | 1.00227 | 0.01846 | 1.02700 | 0.02128 | 0.90248 | -0.67572 | 0.74030 | -0.27779
675 | 1.03065 | 0.01782 | 1.03617 | 0.02138 | 0.87733 | -0.62328 | 0.65601 | -0.22774
685 | 1.01072 | 0.01601 | 0.94646 | 0.02119 | 0.88769 | -0.66447 | 0.70310 | -0.25098
695 | 1.01402 | 0.02043 | 1.00378 | 0.02471 | 0.89369 | -0.70395 | 0.75795 | -0.27823
705 | 1.07414 | 0.01838 | 1.07324 | 0.02316 | 0.88686 | -0.68657 | 0.72303 | -0.26083
715 | 1.03237 | 0.02539 | 0.99668 | 0.02136 | 0.87414 | -0.65906 | 0.67614 | -0.23359
725 | 1.04279 | 0.02215 | 1.03922 | 0.02722 | 0.89617 | -0.72378 | 0.74812 | -0.26705
735 | 1.02738 | 0.01973 | 1.03978 | 0.02539 | 0.89467 | -0.73795 | 0.77170 | -0.28323
745 | 1.04238 | 0.01927 | 1.01427 | 0.03020 | 0.86509 | -0.64972 | 0.65973 | -0.23664
755 | 1.04771 | 0.02398 | 1.02146 | 0.02712 | 0.89132 | -0.75184 | 0.79302 | -0.29984
765 | 1.06149 | 0.01834 | 0.98611 | 0.03130 | 0.88774 | -0.75638 | 0.78905 | -0.29437
775 | 1.05751 | 0.02014 | 1.09123 | 0.02938 | 0.88279 | -0.73219 | 0.73173 | -0.25886
785 | 1.10846 | 0.02307 | 1.10165 | 0.03805 | 0.88973 | -0.77932 | 0.81639 | -0.31113
* Based on using the +1 order throughput curve and 3D stellar models from Magic et al. (2015) .