11institutetext: Department of Physics & Astronomy, University of Leicester,
University Road, Leicester, LE1 7RH, UK, 22institutetext: Southwest Research
Institute, San Antonio, Texas, TX, USA 33institutetext: Jet Propulsion
Laboratory, California Institute of Technology, 4800 Oak Grove Drive,
Pasadena, CA, 91109, USA

# Refining Saturn’s deuterium-hydrogen ratio via IRTF/TEXES spectroscopy

James S.D. Blake (1)  Leigh N. Fletcher (1)  Thomas K. Greathouse (2)  Glenn
S. Orton (3)  Henrik Melin (1)  Mike T. Roman (1)  Arrate Antuñano (1)
Padraig T. Donnelly (1)  Naomi Rowe-Gurney (1)  Oliver King (1)
<EMAIL_ADDRESS>
(Received 3 July 2020 / Accepted 25 January 2021)

The abundance of deuterium in giant planet atmospheres provides constraints on
the reservoirs of ices incorporated into these worlds during their formation
and evolution. Motivated by discrepancies in the measured deuterium-hydrogen
ratio (D/H) on Jupiter and Saturn, we present a new measurement of the D/H
ratio in methane for Saturn from ground-based measurements. We analysed a
spectral cube (covering 1151-1160 cm-1 from 6 February 2013) from the Texas
Echelon Cross Echelle Spectrograph (TEXES) on NASA’s Infrared Telescope
Facility (IRTF) where emission lines from both methane and deuterated methane
are well resolved. Our estimate of the D/H ratio in stratospheric methane,
$1.65_{-0.21}^{+0.27}\times 10^{-5}$ is in agreement with results derived from
Cassini CIRS and ISO/SWS observations, confirming the unexpectedly low CH3D
abundance. Assuming a fractionation factor of $1.34\pm 0.19$ we derive a
hydrogen D/H of $1.23_{-0.23}^{+0.27}\times 10^{-5}$. This value remains lower
than previous tropospheric hydrogen D/H measurements of (i) Saturn $2.10(\pm
0.13)\times 10^{-5}$, (ii) Jupiter $2.6(\pm 0.7)\times 10^{-5}$ and (iii) the
proto-solar hydrogen D/H of $2.1(\pm 0.5)\times 10^{-5}$, suggesting that the
fractionation factor may not be appropriate for stratospheric methane, or that
the D/H ratio in Saturn’s stratosphere is not representative of the bulk of
the planet.

###### Key Words.:

Saturn – Atmospheres: composition – Atmospheres: dynamics

††offprints: J.S.D. Blake,

##  1 Introduction

Saturn | Jupiter |  References |  Instrument/Model
---|---|---|---
Hydrogen | Methane | Hydrogen | Methane |  |
- | $1.7_{-0.8}^{+1.7}$ | - | - |  Owen et al (1986) |  Model – Fourier Transform instrument, Kitt Peak National Observatory
- | $1.7(\pm 1.1)$ | - | - |  Noll and Larson (1991) |  Model – Fourier Transform Spectrometer, IRTF
$2.3_{-0.8}^{+1.2}$ | - | - | - |  Griffin et al. (1996) |  ISO – LWS
$1.85_{-0.6}^{+0.85}$ (m) |  $2.0_{-0.7}^{+1.4}$ (m) |  $2.4\pm 0.4$ (m) |  $2.2\pm 0.7$ (m) |  Lellouch et al (2001) |  ISO - SWS
- | $2.4\pm 0.5$ | - | - |  Bézard et al. (2003) |  TEXES
- | - | - | $2.4\pm 0.7$ |  Bjoraker (1986) |  Voyager IRIS
- | $1.6\pm 0.2$ | - | - |  Fletcher et al. (2009b) |  CIRS
$2.1\pm 0.13$ (m) |  $2.82_{-0.55}^{+0.6}$ (i) | $2.95\pm 0.55$ | - |  Pierel et al. (2017) |  CIRS
$1.62\pm 0.61$ | - | - | - |  |  ISO - SWS
- | - | $2.6\pm 0.7$ | - |  Mahaffy et al. (1998) |  Galileo
$1.23_{-0.23}^{+0.27}$ (i) |  $1.65_{-0.21}^{+0.27}$ (m) | - | - |  This work |  TEXES
Table 1: The quoted D/H ratios are of the order $\times 10^{-5}$. Entries
marked (i) are inferred from corresponding measurements marked (m) for
Lellouch et al. (2001), Pierel et al. (2017) and this work. The values quoted
for the Methane column assume a fractionation factor of $1.34\pm 0.19$
(Lellouch et al. 2001).

The abundance of deuterium in planetary bodies has long been a focus of
research owing to its usefulness in constraining models of planet formation
and evolution (Hersant et al. 2001). The deuterium-hydrogen ratio (D/H) is
known to increase with heliocentric distance due to the fractionation of
deuterium in the formation of icy grains. As such, measuring the D/H ratio in
planetary atmospheres and ice-rich bodies (moons and comets) as a function of
heliocentric distance helps to constrain the distribution of ices in the early
Solar System. Molecular hydrogen constitutes the most abundant source of
hydrogen in the atmosphere (around 85% by volume) and has been used previously
to determine the HD/H2 ratio in planetary tropospheres using far-infrared
spectra (Feuchtgruber et al., 2013). The D/H ratio in planetary stratospheres
can also be assessed using a mid-IR spectral setting that contains both
methane (CH4) and deuterated methane (CH3D) emission lines as described in
Section 2.

There are currently no known methods for the natural production of deuterium,
with the exception of the formation of the Universe. There is however a
natural loss mechanism, the nucleosynthesis of helium in stars, including the
Sun (Griffin et al., 1996, Ferlet & Lemoine, 1998, Lellouch et al., 2001).
Thus, the Solar System D/H ratio in H2 has been decreasing from the protosolar
value of $2.1(\pm 0.5)\times 10^{-5}$ (Geiss and Gloeckler, 1998) to the local
interstellar medium value of $1.5(\pm 0.1)\times 10^{-5}$ throughout time. We
therefore expect the D/H ratio of Jupiter and Saturn to be representative of
the protosolar value incorporated into the icy building blocks during
planetary formation. Several previous measurements of the D/H have been made
for each of the giant planets, a sample of which is shown in Table 1. The D/H
ratio for the two gas giants is smaller than that measured for the ice giants
Uranus and Neptune - Feuchtgruber et al (2013) report a hydrogen D/H of
$4.4(\pm 0.4)\times 10^{-5}$ for Uranus and $4.1(\pm 0.4)\times 10^{-5}$ for
Neptune. Independent near-infrared and mid-infrared analyses of Neptune by
Irwin et al. (2014) and Fletcher et al (2010) report a CH3D/CH4 ratio of
$3.0(\pm 1.0)\times 10^{-4}$, which must be divided by 4 to give a
(D/H)${}_{CH_{4}}$ = $7.5(\pm 2.5)\times 10^{-5}$ in Neptune’s atmosphere.
Fletcher et al. (2010) assumed an old isotopic fractionation factor
f=(D/H)${}_{CH_{4}}$/(D/H)${}_{H_{2}}$=1.25 (Fegley and Prinn, 1988) to give
an estimate of Neptune’s hydrogen (D/H)${}_{H_{2}}$ of $6(\pm 2)\times
10^{-5}$ in agreement with ISO/SWS analysis by Feuchtgruber et al. (1999).
However, if we use a more suitable Neptunian $f=1.6\pm 0.2$ from Lecluse et
al. (1996), this would give a (D/H)${}_{H_{2}}$ of $4.7(\pm 1.7)\times
10^{-5}$ that is more consistent with the Herschel/PACS analysis of
Feuchtgruber et al. (2013). Comparing the measured (D/H)${}_{CH_{4}}$ and
(D/H)${}_{H_{2}}$ for Neptune, this implies a fractionation factor of $1.8\pm
0.6$, closer to the estimate of $f=1.6\pm 0.2$ (Lecluse et al., 1996). These
values are consistent with the Ice Giants accreting greater quantities of ices
(compared to hydrogen gas) during their formation.

The challenge of measuring the D/H ratio in a planetary atmosphere is
considerable, even with a visiting spacecraft like Cassini. It is notable that
CIRS estimates of Saturn’s D/H in methane, inferred from HD lines in the far-
infrared by Pierel et al. (2017) and measured in CH3D by Fletcher et al.
(2009b), are both in disagreement with each other. Taken at face value, the
D/H in stratospheric methane from Fletcher et al. (2009b) (mid-infrared) would
appear to be smaller than the D/H in hydrogen (far infrared), which is both
unexpected and hard to explain. Furthermore, both estimates are smaller than
the proto-solar value of $2.1(\pm 0.5)\times 10^{-5}$, as well as being
smaller than the estimates for Jupiter (e.g. the directly measured Galileo
probe result of Mahaffy et al., 1998). It is expected that the D/H in methane
should be higher than in hydrogen, further, it should be higher than values at
Jupiter due to deuterium enrichment with heliocentric distance. No explanation
is currently offered for this phenomenon as its existence has always been
disputed (Bézard et al., 2003). These discrepancies call into question the D/H
measurements in Saturn’s atmosphere, and prompt us to revisit them in this
study. Table 1 shows that measurements from higher spectral resolution
spectroscopic instruments such as ISO, which yielded a D/H ratio in methane of
$2.0\times 10^{-5}$ from Lellouch et al. (2001), are more consistent with one
another than lower spectral resolution measurements such as those from Cassini
CIRS. The D/H of Saturn has been noted to be lower than the D/H of Jupiter
(Pierel et al., 2017), even when considering error margins. Motivated by these
discrepancies with the Cassini results, we seek to re-examine Saturn’s D/H
ratio using high-resolution ground-based spectroscopy.

We build upon the previous work using the Texas Echelon cross Echelle
Spectrograph (TEXES) instrument (Lacy et al., 2002) on the NASA Infrared
Telescope Facility (IRTF). TEXES is currently the world-leading instrument for
high-spectral resolution mid-infrared observations (maximum R = 100,000) and
therefore has the capacity to produce the most accurate measurement of D/H
from a ground-based telescope. Prior work on Saturn using this instrument has
included the first ever measurement of propane at Saturn (Greathouse et al.,
2005), analysis of nitrogen isotopologues in both Saturn’s and Jupiter’s
ammonia reservoirs (Fletcher et al., 2014), as well as exploring the
stratospheric aftermath of the 2010 storm observed on Saturn (Fouchet et al.,
2016). This work uses the same archive of TEXES observations collected at the
IRTF to examine the D/H ratio of methane in the 8.6 $\mu$m region.

##  2 Observation and data reduction

We observed Saturn in the N-band 8.6 $\mu$m region for one night in February
2013, using the high spectral resolution of the TEXES instrument and the
3-meter diameter mirror of NASA’s IRTF. On 6 February 2013, Saturn’s angular
diameter was 17.2”, at a distance from Earth of 9.64 AU and $L_{s}$ of 41.86°
(local northern spring). The spectral range of the observation was 1151-1160
cm-1, the spectral resolving power $R=\lambda/\Delta\lambda\simeq 80,000$, and
the spectral pixel scale was R=300,000, equivalent to a pixel width in
wavenumbers of 0.0038 cm-1. Data were retrieved using a slit aligned to
celestial north/south that was initially pointed at the centre of Saturn, then
offset north 25” to a sky position where the flatfield and five blank sky
observations were taken for calibration and sky subtraction. The slit was then
moved just east of Saturn and stepped in 0.7” steps westward across Saturn’s
disk until falling off on the opposite side. The 0.7” step size is half the
1.4” slit width, producing Nyquist-sampled maps of Saturn’s disk. We
integrated for 2 sec for each step of the scan and coadded 64 separate scans
giving a final integration time of 128 secs per pixel in the summed datacube.
Each pixel along the 7” slit length covers 0.36”. The final scan map provides
a spatially resolved spectral data cube of Saturn. Other than the limb of the
planet, no spatial structure is apparent in the image, which is formed by
summing across all the measured wavenumbers (Figure 1). The Doppler shift and
emission angle of every pixel of the data cube were calculated using custom
software written for TEXES planetary observations by Greathouse, which
utilizes the NAIF toolkit (Acton et al., 1996). Only pixels with an emission
angle less than 45° (in order to eliminate regions of limb
brightening/darkening) were used in the following analysis so as to remove the
influence of limb brightening and darkening.

Figure 1: Emission of each mapped pixel shown here with an overlaying wire
frame model of Saturn from the midpoint time of the observations. The latitude
range along the central meridian is from approximately 10° to 50° north. The
background regions (shown in black) do overlap with the rings, however the
emission of the rings is undetectable in the wavelength range of this
observation.

Figure 2: Model spectra showing the influence of varying the abundances of
different species. _Panel_ a: model spectrum (red solid line) with nominal
levels of three methane isotopologues and phosphine, overlaying the observed
spectra shifted down by $-0.21\times 10^{-9}$ W cm-2str-1cm-1 (dark grey solid
line) and its associated uncertainties (grey shaded region). _Panel_ b: effect
of the CH4 population on this spectral range, and similiary for _Panel_ c:
deuterated methane isotope CH3D and _Panel_ d: the tropospheric phosphine
(PH3). For each of these three panels (b-d), the red profile is the forward
model using a nominal level of each species (as per the model of Moses et al.,
2000 and Saturn priors from Fletcher et al., 2009), the green line is a
forward model with twice the respective nominal level of each species
identified in the title, the blue line is the forward model where the volume
mixing ratio (VMR) is set to zero at all altitudes of the identified species.

###  2.1 Spectral Contributions

The emission for each wavenumber was averaged across all planetary longitudes
and latitudes in this region to improve the signal to noise ratio (S/N). The
resulting spectra were then analysed using the non-linear optimal estimator
for multivariate spectral analysis code (NEMESIS, Irwin et al., 2008) to fit
the spectrum (Figure 2); a more detailed description of NEMESIS can be found
in Section 3. Figure 2 compares the averaged TEXES spectrum to three forward-
modelled spectra that demonstrate the effects of the methane isotopologues and
phosphine on the model spectra. There are many different species that
contribute to Saturn’s mid-infrared spectra, either through emission or
absorption. From testing the isotopologue 13CH4 it was evident that its
contribution to this spectral range was negligible compared to the inherent
noise of the data, we therefore can ignore its variation when retrieving the
D/H ratio. Though PH3 does have a significant influence on this spectral range
(Figure 2), retrieval testing showed that its influence could not be
disentangled from that of temperature. Indeed, when PH3 was varied as a free
parameter in our spectral inversions, the best fits had the PH3 volume mixing
ratio (VMR) largely unchanged. Hence, we assume the PH3 VMR can remain
constant for all subsequent retrievals.

###  2.2 Radiometric Calibration Uncertainties

At the outset of the analysis, we discovered that the TEXES spectra contained
a number of regions where the calibrated brightness was systematically
brighter where our emission model suggests the emission should be close to
zero, thus signifying an issue with the radiometric calibration. Indeed, this
prevented NEMESIS from fitting a physically plausible temperature structure.
To counter this, a standard radiance offset was added to the measured spectra.
This value was derived by using the mean difference between the measurement
and the forward model to match the continuum in the spectral regions 1153-1154
cm-1 and 1159-1160 cm-1. A range of values around this number were tested to
find the retrieval that gave the lowest $\chi^{2}$ fit of the model to the
observed spectra as well as constraining the sensitivity of our D/H result to
this radiometric uncertainty. Given that both the methane and deuterated
methane lines are measured within the same spectral setting, this systematic
offset does not significantly influence the result. The final radiance offset
used was $-0.21\times 10^{-9}$ W cm-2str-1cm-1 as this yielded both the
smallest $\chi^{2}$ value and a realistic temperature retrieval.

##  3 Analysis

NEMESIS (Irwin et al. 2008) is a radiative-transfer and spectral-retrieval
code that has been used extensively to measure temperature and composition in
giant planet atmospheres. This tool performs a spectral inversion using an
optimal estimation approach (Rodgers et al. 2000), thereby minimising a two-
term cost function that is comprised of the residual fit to the spectral data
and our prior knowledge of the atmospheric state; a priori estimates of
atmospheric temperature and the volume mixing ratios of different species as a
function of altitude. These a priori estimates are used to constrain the
retrieval process by ensuring that the final solution retains physically
realistic atmospheric profiles. Sources of spectral line data are identical to
those tabulated in Fletcher et al. (2018) and were used to generate
k-distributions on a 0.0144 cm-1 grid. Spectral line data for methane and
deuterated methane came from Brown et al. (2003) and their temperature
dependence from Margolis et al. (1993).

A reference atmosphere was used to create the a priori estimates for the
atmospheric temperature as well as mole fractions of methane and its
isotopologues. The a priori temperature estimate was taken from a model based
on the time-series of Cassini Composite Infrared Spectrometer (CIRS)
measurements produced by Fletcher et al. (2017). The time series was
interpolated to the February 2013 date of the TEXES observations to find a
prior for the T(p). The temperatures reported by Fletcher et al. (2017) did
not cover the entire pressure range that we require for our NEMESIS analysis
(1 microbar to 10 bar), hence we blended this with the temperature model of
Moses et al. (2000). A smoothing function was applied to this temperature
profile to reduce any discontinuities between the CIRS model and the Moses
model temperatures.

The vertical shape of the a priori volume mixing ratio profile (VMR) of CH4
was acquired from the Moses et al. (2000) model of Saturn’s atmosphere with
the base level scaled to the CIRS-derived mole fraction of $4.7\pm 0.2\times
10^{-3}$ from Fletcher et al. (2009). CH4 is predicted to be well mixed
throughout the atmosphere of Saturn (Flasar et al., 2005) and therefore serves
as an accurate sensor for measuring atmospheric temperature. CH3D is expected
to be a consistent fraction of the CH4 population as there are no production
or loss mechanisms within Saturn’s atmosphere that are currently known. For
the CH3D vertical VMR profile, the a priori estimate was set to be a fraction
of the CH4 profile in the same manner as Fletcher et al. (2009b) using
equation 1.

$CH_{3}D(VMR)=4\times CH_{4}(VMR)\times 1.7\times 10^{-5}.$ (1)

The factor of $1.7\times 10^{-5}$ was the D/H ratio in hydrogen used in the a
priori set up of Fletcher et al. (2009b), it has been multiplied by 4 here to
convert to the CH3D abundance (see equation 2 in Section 4) assuming a
fractionation factor of 1. During retrievals, the VMR profile of CH3D was
allowed to scale by a constant factor to preserve the shape of the
distribution, given that the CH3D population must be scaled at each level as a
uniform proportion of the CH4 population.

Using these a priori estimates, we simultaneously retrieved the temperature
and CH3D VMR over a selection of CH4 emission lines (1152.5-1152.73 cm-1,
1153.2-1153.6cm-1 and 1154.0-1155.0 cm-1) and CH3D emission lines (between
1155.8 and 1156.15 cm-1). Figure 3 shows the resultant spectral fit (in red)
of the CH4 and CH3D emission lines as well as their surrounding continuum. The
uncertainty in the spectral radiance was drawn from the standard deviation of
background noise sampled from the off-planet regions of the TEXES spectral
cube. This uncertainty was increased for regions of low telluric transmission
so that the retrievals would be weighted towards clearer sky spectral windows,
thereby reducing the effects of terrestrial contamination. Figure 4 shows the
retrieved temperature and CH3D VMR altitudinal profiles. Both the troposphere
and stratosphere are cooler than the CIRS model would suggest for this time
period. The VMR of CH3D is a factor of approximately 0.94 of the a priori
estimate (Figure 4 (b)).

Figure 3: (top) spectral retrieval of the CH4 emission lines and the CH3D
emission lines (red line), the spectra from the observation (black solid
line), the shaded grey region is the uncertainty range associated with the
observed spectra, the telluric absorption (blue dot-dashed line) in this
spectral range. The spectral ranges used in the retrieval are highlighted for
CH4 (green) and CH3D (orange). (bottom) residual difference between the
spectral retrieval and the spectra from the observation (black), the
observation uncertainty (red solid line) is shown for each wavelength as well
as the telluric absorption (blue dot-dashed line).

Figure 4: _Left_ : retrieved vertical temperature profile T(p) is shown in red
and the shaded grey region is the associated uncertainty, the a priori T(p)
estimate is shown in black. _Right_ : retrieved vertical VMR profile of CH3D
is shown in red alongside it’s a priori estimate, which is shown in black.

We retrieve a CH3D abundance at the 1-millibar level of $0.30_{-0.05}^{+0.04}$
ppm, which is close to the a priori estimate. Taking the ratio of the
retrieved CH3D vertical VMR and the CH4 VMR profile, we estimate Saturn’s
(D/H)${}_{CH_{4}}$ $=1.65\times 10^{-5}$, with a full uncertainty analysis in
the following Sections. We note that this value is considered to be
representative of Saturn’s stratosphere, because this is where we have an
independent constraint on the atmospheric temperatures from the nearby CH4
emission lines. While we do have sensitivity to tropospheric CH3D, we do not
have a way of independently constraining tropospheric temperatures (not to
mention the influence of tropospheric phosphine). Hence, we assume that the
value derived in the stratosphere is relevant over the entire atmospheric
column. In the following sections we conduct analysis for the degree of
uncertainty in this D/H ratio. We test the sensitivity of our fit to various
input parameters; the uncertainty in the temperature fit (Section 3.1) and the
uncertainty caused by the radiance offset (Section 3.2).

###  3.1 Uncertainty in the temperature fit

To assess the sensitivity of the spectral fits to the derived D/H ratio, we
fixed the CH3D abundance to values that are between 50-150% of the value
derived above, and reran the temperature retrieval, comparing the goodness-of-
fit to the TEXES spectrum in each case. The $\Delta\chi^{2}$ (the difference
of the non-reduced $\chi^{2}$ from the minimum value) for each fit is shown in
Figure 6, along with a selected range of retrieved temperature profiles,
indicating that a different D/H ratio can yield a slightly different (but
still physically plausible) temperature structure. The first confidence
interval $\sigma_{1}$ can be measured by interpolating between these points
with a quadratic polynomial and examining the point at which these spectral
fits deviate from the measurements by a $\Delta\chi^{2}$ of 1. Similarly, the
confidence intervals $\sigma_{2}$ and $\sigma_{3}$ were estimated from
deviations with a $\Delta\chi^{2}$ of 4 and 9 respectively. It is apparent
from Figure 5 that the change to the D/H ratio does not affect the $\chi^{2}$
value in a symmetrical fashion about the minimum. As such, two different
quadratic fits were used for the D/H values less than (shown in blue) and
greater than (shown in red) the initial retrieval. The confidence intervals
for this fit are $\sigma_{1}=-0.20/+0.27\times 10^{-5}$ ,
$\sigma_{2}=-0.38/+0.51\times 10^{-5}$ and $\sigma_{3}=-0.57/+0.93\times
10^{-5}$. The first confidence interval corresponds to an uncertainty on the
D/H ratio of approximately 12%, suggesting that the temperature retrieval is a
significant source of uncertainty in this analysis of D/H. Using the first
confidence interval, the D/H in methane with uncertainties is therefore given
as $1.65_{-0.20}^{+0.27}\times 10^{-5}$. However, this does not capture the
additional systematic uncertainties related to the TEXES calibration, as
explained below.

Figure 5: _Left_ : $\chi^{2}$ values (compared to the best fit) of each
temperature retrieval for the range of fixed D/H ratios. Measured values are
marked by the black asterisks, the blue dotted line marks the quadratic fit to
the D/H ratios less than $1.65\times 10^{-5}$ and the dashed red line marks
the quadratic fit of the D/H ratios that are greater than $1.65\times
10^{-5}$. The minimum $\chi^{2}$ of 84.487 has been subtracted from all values
so that this plot can show $\Delta\chi^{2}$. The horizontal black lines
delineate the $\Delta\chi^{2}$ of 1, 4 and 9 which correspond to $\sigma_{1}$,
$\sigma_{2}$ and $\sigma_{3}$ respectively. The vertical dotted line marks the
inferred value of methane D/H $2.9\times 10^{-5}$ converted from the hydrogen
proto-solar D/H of Geiss and Gloeckler (1998) using a fractionation factor of
1.34, demonstrating that the proto-solar quantity of deuterated methane
provides a significantly worse fit using our data and retrieval. _Right_ : the
temperature profiles associated with CH3D abundances set to 50% (blue dashed
line), 60% (blue dotted line), nominal levels (solid black line), 140% (red
dotted line) and 150% (red dashed line). We note that the nominal level is in
the centre of these adjusted temperature retrievals, this indicates that the
black profile is the best temperature fit for these data.

###  3.2 Uncertainty esimation of radiance offset

To test the sensitivity of the retrieval to the systematic radiance offset
described in Section 2.2 a series of NEMESIS retrievals were performed for a
range of radiance offsets applied to the spectra. A range of radiance offsets
from $-1.3$ to $-3.0\times 10^{-9}$ W cm-2str-1cm-1 were each tested with a
retrieval of both the temperature and CH3D VMR. Using a least-squares fitting
technique, the $\chi^{2}$ values of each of these retrievals were assessed, as
well as the D/H ratio. The minimum $\chi^{2}$ was subtracted from all measured
$\chi^{2}$ values, such that the resultant contour plot shows $\Delta\chi^{2}$
(Figure 6). The first three confidence intervals are marked respectively,
$\sigma_{1}$, $\sigma_{2}$ and $\sigma_{3}$, which correspond to a
$\Delta\chi^{2}$ of 1, 4 and 9. The confidence intervals for this fit are
$\sigma_{1}=-0.39/+0.18\times 10^{-9}$, $\sigma_{2}=-0.89/+0.35\times 10^{-9}$
and $\sigma_{3}=-1.37/+0.55\texttimes 10^{-9}$ W cm-2str-1cm-1. The first
confidence interval amounts to a change in the D/H of $-0.047/+0.013\times
10^{-5}$ (Fig. 6) which corresponds to an uncertainty of approximately 1-3% in
the D/H ratio. This highlights the benefit of measuring both the methane and
deuterated methane lines together in the same TEXES spectral setting,
rendering them largely immune to systematic offsets. This indicates that the
radiance offset has a smaller influence on the measurement of D/H and the
quality of the spectral fits than the temperature-fitting process of Section
3.1.

Figure 6: _Left_ Non-reduced $\Delta\chi^{2}$ values obtained from NEMESIS
retrievals of (D/H)${}_{CH_{4}}$ and temperature as a function of radiance
offset. The three horizontal lines represent a $\Delta\chi^{2}$ of 1, 4 and 9,
which are equivalent to the $\sigma_{1}$, $\sigma_{2}$ and $\sigma_{3}$
levels, respectively. The dotted blue and dashed red lines mark the quadratic
fits to the relevant regions. _Right_ Change in the D/H ratio as a function of
radiance offset. Derived values are marked by black crosses for each radiance
offset. Quadratic fits to the radiance are marked for the same radiance
offsets as in the left panel. For each of the confidence intervals, the
radiance shift and its corresponding change in D/H is marked by $\sigma_{1}$
dotted (red and blue), $\sigma_{2}$ dashed (red and blue) and $\sigma_{3}$
dot-dashed (red only).

##  4 Discussion

We measure Saturn’s D/H ratio for methane to be $1.65_{-0.21}^{+0.27}\times
10^{-5}$, with the stated 1-sigma uncertainty representing the contribution of
the NEMESIS fit, the radiance offset and the uncertainty in the CH4 abundance
(adding these uncertainties in quadrature). The uncertainty due to the
temperature retrieval is by far the most significant influence on the D/H
ratio at 12%. In contrast, systematic errors in the TEXES radiance calibration
appear to only have a minimal influence (1-3%). These uncertainties could be
further constrained with new observations of Saturn at the same resolution but
with a broader wavelength coverage, thereby improving the S/N ratio and
sensitivity to stratospheric methane lines, which in turn would provide a more
constrained temperature retrieval.

Our D/H measurement is in agreement with the methane D/H of $1.6(\pm
0.2)\times 10^{-5}$ derived from the Cassini/CIRS observations of CH3D by
Fletcher et al. (2009b), but not with the estimate of $2.85(\pm 0.2)\times
10^{-5}$ that was estimated by converting the D/H derived from measurements of
HD by Pierel et al (2017). Our D/H in methane is within the uncertainties of
the result of $1.7(\pm 1.1)\times 10^{-5}$ determined from high-resolution 5
$\mu$m spectroscopy by Noll and Larson (1991), and is at the lower end of the
ISO/SWS determination of $2.0_{-0.7}^{+1.4}\times 10^{-5}$ by Lellouch et al.
(2001). It appears to be too low to be consistent with the estimate of 2.4
(±0.5) $\times 10^{-5}$ in methane from Bézard et al. (2003), who used the
same TEXES instrument and spectral region in 2000, but we have been unable to
reproduce their result.

###  4.1 Deuterium fractionation

The D/H ratio of methane and hydrogen are related to each other as shown in
equation 2. Prior measurements of Saturn’s D/H ratio have utilised the
isotopic enrichment factor f which relates the distribution of the deuterium
isotope in hydrogen-containing molecular species.

$f=\frac{(D/H)_{CH_{4}}}{(D/H)_{CH_{3}D}}.$ (2)

Lellouch et al. (2001) used a fractionation factor of $1.34\pm 0.19$ taken
from the average of prior assessments using both laboratory analysis (Lécluse
et al., 1996) and model analysis (Smith et al., 1996, 1998). This
fractionation factor was most recently used in the work of Pierel et al.
(2017) to convert their (D/H)${}_{H_{2}}$ to a D/H for methane of $2.85\times
10^{-5}$. Working in the opposite direction, if we apply this fractionation
factor to our (D/H)${}_{CH_{4}}$ we estimate a hydrogen D/H ratio of
$1.23_{-0.23}^{+0.27}\times 10^{-5}$ which is significantly below the
uncertainty ranges of the estimated proto-solar values $2.1(\pm 0.5)\times
10^{-5}$ from Geiss and Gloeckler (1998).

Surprisingly, our (D/H)${}_{H_{2}}$ estimated from our methane analysis is not
within the uncertainty range of the CIRS analysis from Pierel et al. (2017)
which yielded a hydrogen-derived D/H value of $2.10(\pm 0.13)\times 10^{-5}$.
Indeed, if we compare our D/H in methane with the CIRS-derived D/H in
hydrogen, we would need an entirely different fractionation factor of
approximately 0.79. Conversely, our value is within the uncertainty range of
the ISO-SWS measurement (D/H)${}_{H_{2}}$ of $1.85_{-0.60}^{+0.85}\times
10^{-5}$ for Saturn from Lellouch et al (2001). NEMESIS re-analysis of the
same ISO-SWS results gave a (D/H)${}_{H_{2}}$ value of $1.62(\pm 0.61)\times
10^{-5}$ (see Table 5 of Pierel et al., 2017), which is even closer to our
estimated (D/H)${}_{H_{2}}$ from the TEXES measurements. Note that taking this
latter (D/H)${}_{H_{2}}$ and comparing it to our refined value of
(D/H)${}_{CH_{4}}$ would imply a fractionation factor close to unity.
Laboratory work to reassess the fractionation factor under Saturn’s
stratospheric conditions would be welcome.

We initially suspected that the significant scatter in the D/H results might
be a consequence of the differing spectral resolutions used in different
studies. However, Griffin et al. (1996) found a higher hydrogen D/H of
$2.3_{-0.8}^{+1.2}\times 10^{-5}$ when using the ISO – LWS which has a maximum
resolving power of R=9,000. From this we infer that the variation in D/H
measurements is unlikely to be caused by variation in spectral resolution, as
the high-resolution spectra of TEXES appears to confirm lower-resolution
measurements from CIRS (Fletcher et al., 2009b). An alternative source of this
variation could be the spectroscopic parameters used. In this work we use the
HITRAN line database, however Lellouch et al. (2001) used the GEISA 1997 data
bank. Further testing is required to see if this would make an appreciable
difference. In summary, our new methane-derived stratospheric D/H value is
consistent with one derived from Cassini CIRS data, but both results are
generally lower than what would be expected from the D/H measured
independently in hydrogen at tropospheric depths. This raises the intriguing
possibility that the deuterium fractionation might vary between the
troposphere and stratosphere reservoirs. However, this inconsistency between
different spectral regions might also be a consequence of discrepancies in the
spectroscopic line data for HD, and a re-analysis of CIRS and ISO HD
observations might be required with a range of different scale factors applied
to the rotational line intensities, to see if there is some way to make the
two spectral ranges consistent. It may also be that the CH3D measurements
suffer from fewer systematic effects given the spectral close nature of the
CH3D and CH4 emissions compared to the much more spectrally separated H2 and
HD lines.

###  4.2 Comparisons to Jupiter and Neptune

Direct comparisons of our D/H in Saturnian stratospheric methane to published
D/H in Jovian stratospheric methane, $2.2(\pm 0.7)\times 10^{-5}$ (Lellouch et
al., 2001) and $2.4(\pm 0.7)\times 10^{-5}$ (Bjoraker et al., 1986), show that
our result for Saturn is significantly lower, however still marginally within
uncertainty values. We also note that the Saturnian (D/H)${}_{CH_{4}}$ is a
factor of 3-6 times smaller than that found in Neptune’s methane ($7.5(\pm
2.5)\times 10^{-5}$ as deduced in Section 1, based on Fletcher et al., 2010;
Irwin et al., 2014).

Our inferred D/H in hydrogen of $1.23_{-0.23}^{+0.27}\times 10^{-5}$ is
substantially lower and outside of the uncertainty margins of Jupiter’s
hydrogen D/H from ISO of $2.4(\pm 0.4)\times 10^{-5}$ (Lellouch et al., 2001),
and the in situ Galileo-probe measurement $2.6(\pm 0.7)\times 10^{-5}$
(Mahaffy et al., 1998). Given the margin of uncertainty on the Jupiter and
Saturn measurements, as well as the proposed values for the proto-solar value
of D/H, this comparison indicates that the methane D/H ratio in Saturn’s
stratosphere is definitively lower than that of Jupiter and the proto-solar
value. It remains a possibility that some sort of fractionation process,
depleting deuterium in the observable atmosphere of Saturn but not Jupiter,
may be playing a role. For example, Dobrijevic and Loison (2018) have
previously found photochemical fractionation of N isotope-bearing molecules in
Titan’s atmosphere. Contamination of Saturn’s stratosphere via exogenic
materials with lower D/H ratios could also be a possibility, although this is
unlikely given the high D/H observed in cometary ices. Whilst we cannot
definitively say the same processes are responsible for our measured D/H of
Saturn, perhaps the D/H measured in stratospheric methane emission is not
wholly representative of Saturn’s bulk composition due to some unidentified
chemical process similar to Dobrijevic and Loison (2018). The tropospheric D/H
measured in hydrogen by, for example, Pierel et al. (2017) might therefore be
the better estimate pertinent to the study of planetary origins.

A possible method to reconcile this would be to measure the D/H of Jupiter and
Saturn using the same observational and analytical techniques, thereby
allowing a direct comparison to be made – the James Webb Space Telescope
(Norwood et al., 2016) might offer such an opportunity in the CH3D and CH4
emission regions, but the rotational HD features will be out of reach.

##  5 Conclusions

Infrared spectra from the TEXES instrument have been analysed over the range
of 8.62 to 8.68 $\mu$m ($1151-1160$cm-1) in order to determine the D/H ratio
in Saturn’s stratospheric methane. This spectral range features emission lines
from both methane and its deuterated isotopologue and was used to retrieve the
atmospheric temperature and deuterated methane volume mixing ratio
simultaneously as a function of altitude. We find a methane D/H of
$1.65_{-0.21}^{+0.27}\pm 10^{-5}$, which is in good agreement with previous
D/H measurements in methane in the same spectral range from Cassini CIRS
(Fletcher et al., 2009b). However, this stratospheric value is considerably
smaller than that found for Jupiter and, if we employ a factor to account for
fractionation in different molecules, it would predict a D/H in hydrogen that
is significantly smaller than previous ISO and Cassini measurements of far-
infrared HD lines.

We note that estimations of the hydrogen D/H on Saturn are consistently lower
than the protosolar value of D/H (estimated at $2.1(\pm 0.5)\times 10^{-5}$ –
Geiss and Gloeckler, 1998) and are actually closer to those values found today
in the Local Interstellar Medium - D/H in hydrogen of $1.51(\pm 0.1)\times
10^{-5}$ (Linsky, 1998, Sahu et al., 1999). No explanation for this disparity
has been found so far and we recognise that it is difficult to imagine a
mechanism which would deplete deuterium at Saturn. It is possible that the
stratospheric value is not representative of Saturn’s bulk, either via some
unidentified chemical fractionation process, or maybe by contamination from
unidentified exogenic material with a D/H value lower than proto-solar levels.
Spatially resolved mapping of variations in the D/H ratio might help elucidate
these possibilities, but further progress may require in situ sampling of the
D/H ratio via atmospheric entry probes for Saturn such as the HERA Saturn
entry probe proposed in Mousis et al. (2016).

## Acknowledgments

The Leicester authors were supported by a European Research Council
Consolidator Grant, under the European Union’s Horizon 2020 research and
innovation framework, grant number 723890. LNF was also supported by a Royal
Society Research Fellowship at the University of Leicester. The TEXES
observations were acquired under programme ID 2013A-045 at NASA’s Infrared
Telescope Facility, where Greathouse and Fletcher were visiting astronomers.
Greathouse and Orton were supported by NASA with funds distributed,
respectively, to the Southwest Research Institute and to the Jet Propulsion
Laboratory, California Institute of Technology. The Infrared Telescope
Facility is operated by the University of Hawaii under contract NNH14CK55B
with the National Aeronautics and Space Administration. We recognise the
cultural and religious significance of Maunakea to the Hawaiian people and are
privileged to have been able to observe from this mountain.

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# The Near Infrared Imager and Slitless Spectrograph for the James Webb Space
Telescope \- I. Instrument Overview and in-Flight Performance

Doyon, René  Institut Trottier de recherche sur les exoplanètes, Département
de physique, Université de Montréal  Observatoire du Mont-Mégantic, Université
de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, H3C 3J7, Québec, Canada.
Chris J. Willott NRC Herzberg, 5071 West Saanich Rd, Victoria, BC V9E 2E7,
Canada  John B. Hutchings  NRC Herzberg, 5071 West Saanich Rd, Victoria, BC
V9E 2E7, Canada  Anand Sivaramakrishnan Space Telescope Science Institute,
3700 San Martin Drive, Baltimore, MD 21218, USA  Astrophysics Department,
American Museum of Natural History, 79th Street at Central Park West, New
York, NY 10024  Department of Physics and Astronomy, Johns Hopkins University,
3701 San Martin Drive, Baltimore, MD 21218, USA  Loïc Albert Institut Trottier
de recherche sur les exoplanètes, Département de physique, Université de
Montréal  Observatoire du Mont-Mégantic, Université de Montréal, C.P. 6128,
Succ. Centre-ville, Montréal, H3C 3J7, Québec, Canada.  David Lafrenière
Institut Trottier de recherche sur les exoplanètes, Département de physique,
Université de Montréal  Neil Rowlands Honeywell Aerospace #100, 303 Terry Fox
Drive, Ottawa, ON K2K 3J1, Canada  M. Begoña Vila NASA Goddard Space Flight
Center, 8800 Greenbelt Rd, Greenbelt, MD 20771  KBR Space Engineering
Division, 8120 Maple Lawn Blvd, Fulton, MD 20759  André R. Martel  Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Stephanie LaMassa Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  David Aldridge  Honeywell Aerospace #100, 303 Terry
Fox Drive, Ottawa, ON K2K 3J1, Canada  Étienne Artigau Institut Trottier de
recherche sur les exoplanètes, Département de physique, Université de Montréal
Observatoire du Mont-Mégantic, Université de Montréal, C.P. 6128, Succ.
Centre-ville, Montréal, H3C 3J7, Québec, Canada.  Peter Cameron  Honeywell
Aerospace #100, 303 Terry Fox Drive, Ottawa, ON K2K 3J1, Canada  Pierre Chayer
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218,
USA  Neil J. Cook Institut Trottier de recherche sur les exoplanètes,
Département de physique, Université de Montréal  Rachel A. Cooper Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Antoine Darveau-Bernier Institut Trottier de recherche sur les exoplanètes,
Département de physique, Université de Montréal  Jean Dupuis  Canadian Space
Agency, 6767 Route de l’Aéroport, Saint-Hubert, QC J3Y 8Y9, Canada  Colin
Earnshaw  Honeywell Aerospace #100, 303 Terry Fox Drive, Ottawa, ON K2K 3J1,
Canada  Néstor Espinoza Space Telescope Science Institute, 3700 San Martin
Drive, Baltimore, MD 21218, USA  Joseph C. Filippazzo Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Alexander W.
Fullerton Space Telescope Science Institute, 3700 San Martin Drive, Baltimore,
MD 21218, USA  Daniel Gaudreau  Canadian Space Agency, 6767 Route de
l’Aéroport, Saint-Hubert, QC J3Y 8Y9, Canada  Roman Gawlik  Honeywell
Aerospace #100, 303 Terry Fox Drive, Ottawa, ON K2K 3J1, Canada  Paul
Goudfrooij Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Craig Haley  Honeywell Aerospace #100, 303 Terry Fox
Drive, Ottawa, ON K2K 3J1, Canada  Jens Kammerer Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  David Kendall
Canadian Space Agency, 6767 Route de l’Aéroport, Saint-Hubert, QC J3Y 8Y9,
Canada  Scott D. Lambros  NASA Goddard Space Flight Center, 8800 Greenbelt Rd,
Greenbelt, MD 20771  Luminita Ilinca Ignat  Canadian Space Agency, 6767 Route
de l’Aéroport, Saint-Hubert, QC J3Y 8Y9, Canada  Michael Maszkiewicz  Canadian
Space Agency, 6767 Route de l’Aéroport, Saint-Hubert, QC J3Y 8Y9, Canada
Ashley McColgan  Honeywell Aerospace #100, 303 Terry Fox Drive, Ottawa, ON K2K
3J1, Canada  Takahiro Morishita Space Telescope Science Institute, 3700 San
Martin Drive, Baltimore, MD 21218, USA  Nathalie N.-Q. Ouellette Institut
Trottier de recherche sur les exoplanètes, Département de physique, Université
de Montréal  Observatoire du Mont-Mégantic, Université de Montréal, C.P. 6128,
Succ. Centre-ville, Montréal, H3C 3J7, Québec, Canada.  Camilla Pacifici Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Natasha Philippi  Honeywell Aerospace #100, 303 Terry Fox Drive, Ottawa, ON
K2K 3J1, Canada  Michael Radica Institut Trottier de recherche sur les
exoplanètes, Département de physique, Université de Montréal  Swara
Ravindranath Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Jason Rowe Department of Physics & Astronomy,
Bishop’s University, Sherbrooke, QC J1M 1Z7, Canada.  Arpita Roy Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Department of Physics and Astronomy, Johns Hopkins University, 3701 San Martin
Drive, Baltimore, MD 21218, USA  Karl Saad  Canadian Space Agency, 6767 Route
de l’Aéroport, Saint-Hubert, QC J3Y 8Y9, Canada  Sangmo Tony Sohn Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Geert Jan Talens Department of Astrophysical Sciences, Princeton University, 4
Ivy Lane, Princeton, NJ 08544, USA  Deepashri Thatte  Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Joanna M. Taylor
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218,
USA  Thomas Vandal Institut Trottier de recherche sur les exoplanètes,
Département de physique, Université de Montréal  Kevin Volk Space Telescope
Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Michel
Wander  Canadian Space Agency, 6767 Route de l’Aéroport, Saint-Hubert, QC J3Y
8Y9, Canada  Gerald Warner  Honeywell Aerospace #100, 303 Terry Fox Drive,
Ottawa, ON K2K 3J1, Canada  Sheng-Hai Zheng  Honeywell Aerospace #100, 303
Terry Fox Drive, Ottawa, ON K2K 3J1, Canada  Julia Zhou  Honeywell Aerospace
#100, 303 Terry Fox Drive, Ottawa, ON K2K 3J1, Canada  Roberto Abraham
Department of Astronomy & Astrophysics, University of Toronto, 50 St. George
Street, Toronto, ON M5S 3H4, Canada  Dunlap Institute for Astronomy and
Astrophysics, University of Toronto, 50 St George Street, Toronto, ON M5S 3H4,
Canada  Mathilde Beaulieu  Université Côte d’Azur, Observatoire de la Côte
d’Azur, CNRS, Laboratoire Lagrange, F-06108 Nice, France.  Björn Benneke
Institut Trottier de recherche sur les exoplanètes, Département de physique,
Université de Montréal  Laura Ferrarese NRC Herzberg, 5071 West Saanich Rd,
Victoria, BC V9E 2E7, Canada  Ray Jayawardhana Department of Astronomy,
Cornell University, Ithaca, New York 14853, USA  Doug Johnstone NRC Herzberg,
5071 West Saanich Rd, Victoria, BC V9E 2E7, Canada  Department of Physics and
Astronomy, University of Victoria, 3800 Finnerty Road, Elliot Building,
Victoria, BC, V8P 5C2, Canada  Lisa Kaltenegger Carl Sagan Institute and
Department of Astronomy, Cornell University, Ithaca, NY 14853, USA  Michael R.
Meyer Astronomy Department, University of Michigan, Ann Arbor, MI 48109, USA
Judy L. Pipher Department of Physics and Astronomy, University of Rochester,
Rochester NY 14627, USA  Julien Rameau Institut Trottier de recherche sur les
exoplanètes, Département de physique, Université de Montréal  Univ. Grenoble
Alpes, CNRS, IPAG, F-38000 Grenoble, France.  Marcia Rieke Steward
Observatory, University of Arizona, 933 N. Cherry Ave, Tucson, AZ 85721, USA.
Salma Salhi Department of Physics and Astronomy, University of Calgary, 2500
University Dr NW, Calgary, AB T2N 1N4, Canada.  Institut Trottier de recherche
sur les exoplanètes, Département de physique, Université de Montréal  Marcin
Sawicki Institute for Computational Astrophysics and Department of Astronomy &
Physics, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3,
Canada

(Received 21 December 2022; Revised 13 February; Accepted 10 May 2023)

###### Abstract

The Near-Infrared Imager and Slitless Spectrograph (NIRISS) is the science
module of the Canadian-built Fine Guidance Sensor (FGS) onboard the James Webb
Space Telescope (JWST). NIRISS has four observing modes: 1) broadband imaging
featuring seven of the eight NIRCam broadband filters, 2) wide-field slitless
spectroscopy (WFSS) at a resolving power of $\sim$150 between 0.8 and 2.2
$\mu$m, 3) single-object cross-dispersed slitless spectroscopy (SOSS) enabling
simultaneous wavelength coverage between 0.6 and 2.8 $\mu$m at R$\sim$700, a
mode optimized for exoplanet spectroscopy of relatively bright ($J<6.3$) stars
and 4) aperture masking interferometry (AMI) between 2.8 and 4.8 $\mu$m
enabling high-contrast ($\sim 10^{-3}-10^{-4}$) imaging at angular separations
between 70 and 400 milliarcsec for relatively bright ($M<8$) sources. This
paper presents an overview of the NIRISS instrument, its design, its
scientific capabilities, and a summary of in-flight performance. NIRISS shows
significantly better response shortward of $\sim 2.5\,\mu$m resulting in
10-40% sensitivity improvement for broadband and low-resolution spectroscopy
compared to pre-flight predictions. Two time-series observations performed
during instrument commissioning in the SOSS mode yield very stable spectro-
photometry performance within $\sim$10% of the expected noise. The first
space-based companion detection of the tight binary star AB Dor AC through AMI
was demonstrated.

instrumentation, JWST

††journal: PASP

##  1 Introduction

One of the four science instruments onboard the James Webb Space Telescope
(JWST; Gardner et al. (2006), Mather et al. 2022) is the Near-Infrared Imager
and Slitless spectrograph (NIRISS) provided by the Canadian Space Agency
(CSA). The original Canadian contribution to JWST was the Fine Guidance Sensor
(FGS; Rowlands et al. (2003)) and technical contributions of the NIRCam
instrument (Horner et al., 2002), more specifically the filter wheels and the
tunable filter modules, used to be part of NIRCam in the early days of the
Project and specifically designed to detect Lyman alpha emitters. In
2002-2003, as part of the replan exercise of the Project that reduced the 8 m
telescope aperture into 6.5 m, the tunable filter modules were displaced out
of NIRCam to become a standalone instrument on the back side of FGS: the
Tunable Filter Imager (TFI; Rowlands et al. (2004)). However the original two
channel TFI architecture was mass inefficient and was quickly re-optimized in
2005-2006 to a more compact single channel version which remained the baseline
into Phase D (Doyon et al., 2010). The heart of TFI was a Fabry-Perot etalon
whose development turned out to be challenging, in particular, their
susceptibility to vibration and shocks during launch (Haley et al., 2012).
Those technical issues led to a reconfiguration of TFI into a simpler, more
robust instrument with imaging and spectroscopic capabilities enabling science
programs complementary to the other three science instruments (Doyon et al.,
2012).

This paper presents an overview of the NIRISS instrument, its observing modes,
and a summary of in-flight performance based on commissioning data. This paper
is part of a series describing the JWST mission (Gardner et al., 2023), the
telescope performance (McElwain et al., 2023), the science performance (Rigby
et al., 2023) and that of the other three science instruments: NIRCam (Rieke
et al., 2023), NIRSpec (Böker et al., 2023) and MIRI (Wright et al., 2023).
The reader is invited to consult the NIRISS section of the JDox
documentation111https://jwst-docs.stsci.edu for further information.

##  2 Science requirements

NIRISS was conceived to enable similar science themes as TFI which was
designed for studying distant galaxies (Lyman $\alpha$ emitters) and for
detecting/characterizing young gas giant exoplanets and brown dwarfs through
narrow-band coronagraphy and aperture masking interferometry (AMI), the latter
with a 7-hole non-redundant mask (Doyon et al., 2010). Since the
reconfiguration had to occur over a relatively short schedule, approximately
one year, this imposed minimal design changes that were restricted to keep the
same optics, remove the Fabry-Pérot etalon and populate the dual (filter and
pupil) wheel with new optical components (filters and dispersion elements).
While the main intent of this reconfiguration was to provide new observing
modes, they were also developed with the spirit of providing mode redundancy
for broadband imaging to NIRCam, multi-object spectroscopy to NIRSpec and
white light guiding to FGS.

##  3 Observing Modes

NIRISS features four observing modes: 1) broadband imaging, 2) low-resolution
wide-field slitless spectroscopy (WFSS), 3) medium-resolution single-object
spectroscopy (SOSS), and 4) aperture masking interferometry (AMI) and kernel
phase interferometry (KPI). A given mode is configured by combining two
optical elements from the filter and pupil wheels (see Figure 1). Here we
present a brief description of each mode with highlights of the main science
programs that they enable. More details on WFSS, SOSS, AMI and KPI are given
in Willott et al. (2022), Albert et al. (2023), Sivaramakrishnan et al. (2023)
and Kammerer et al. (2023), respectively. Table 1 provides an overview of the
NIRISS specifications along with a high-level description of each mode.

###  3.1 Imaging

NIRISS imaging is primarily used for pre-imaging observations needed for the
WFSS mode or for parallel observations but will be available as prime in Cycle
2. F070W excluded, NIRISS carries the same broadband “W” filters as NIRCam
with resolving power $R\sim 4$. They were manufactured with the same coating
design222Since the NIRCam short-wavelength channels use a 2.5 $\mu$m cut-off
H2RG detector, all filters shortward of 2.77 $\mu$m are not blocked to
radiation beyond 2.5 $\mu$m. For NIRISS, which uses a 5 $\mu$m cut-off
detector, each short-wavelength filter is mounted with an appropriate blocking
filter. with inband transmission varying between 86 and 95% (see Figure 2).

Figure 1: Schematic diagram showing the location of all optical elements on
the filter and pupil wheels. Both wheels feature a clear aperture; CLEARP
includes pupil alignment references in the central obstruction that was used
during integration and tests. Figure from the JDox documentation. Figure 2:
Transmission profile of all NIRISS filters. Figure from the JDox
documentation. Table 1: NIRISS Specifications and Observing Mode Overview

Instrument Parameter |  | Comment/Description
---|---|---
Detector | Hawaii-2RG | 5 $\mu$m cut-off, 2048$\times$2048 pixels with 18 $\mu$m pitch
Read noise (CDS) | 17.1 e- | Total noise is 10 e- for a 1000 s exposure
Image scale | 65.57$\pm$0.04 mas | Distortion solution known to $<$3 mas RMS
Field of view | 2.2${}^{\prime}\times 2.2^{\prime}$ |
Imaging
Wavelength range | 0.8-5.0 $\mu$m | 7 broad- and 5 medium-band filters
WFSS
Wavelength range | 0.8-2.2 $\mu$m | 6 blocking filters: F090W, F115W, F140M, F150W, F158M, F200W
Number of grisms | 2 | Two orthogonal orientations available
Spectral resolution | 139 | Measured 2-pixel resolution at 1.3 $\mu$m
SOSS
Wavelength range | 0.6-2.8 $\mu$m | Two cross-dispersed orders, no blocking filter
Spectral resolution | 654 | Measured 2-pixel resolution at 1.25 $\mu$m
Brightness limit (J magnitude) | 6.3 | Order 2 in smallest subarray 96$\times$2048
AMI
Wavelength range | 2.8-4.8 $\mu$m | Four filters: F277W, F380M, F430M, F480M
Contrasta | 10-3-10-4 | One sigma contrast$\approx 10/\sqrt{N_{p}}$ where $N_{p}$ is the total number of photons
Inner working angle |  $70-400$ mas | Total field of view is a subarray of 80$\times$80 pixels

Note. — a Preliminary in-flight performance are $0.5-1$ mag worse than
predicted with 108 photons (Sivaramakrishnan et al., 2023)).

###  3.2 Wide Field Slitless Spectroscopy

The WFSS mode is optimized for the detection of faint, high-redshift galaxies
over the full NIRISS field of view (FOV). It is implemented through two grisms
(GR150R & GR150C) operated in slitless mode enabling low-resolution $R\sim
150$ multi-object spectroscopy between 0.8 and 2.2 $\mu$m in first order.

Both grisms are identical except that they are mounted so as to disperse light
90∘ from one another, one along the detector rows (GR150R) and the other along
the columns (GR150C). Observing the same scene with both grisms lifts the
ambiguity between spatial location and wavelength, especially for emitting
sources with strong equivalent-width such as Lyman $\alpha$ emitters or
extreme emission-line galaxies. The two orientations also mitigate the problem
of overlapping spectra for crowded regions for which the nominal operating
scenario is to divide the observing time equally between GR150C and GR150R
observations. The data are acquired similarly as in imaging mode by dithering
the scene around on the detector.

###  3.3 Single-Object Slitless Spectroscopy

SOSS is specifically designed to perform medium-resolution ($R\sim 700$)
transit and eclipse spectroscopy of exoplanets orbiting relatively bright
($J>6.3$) host stars. The wide wavelength coverage is achieved through the
GR700XD grism assembly featuring a cross-dispersing ZnS prism combined with a
directly-ruled ZnSe grism (c.f., figure 1 of Albert et al. (2023)). The
entrance face of the ZnS prism has a built-in cylindrical weak lens that
defocuses the spectrum over $\sim$25 pixels along the spatial direction
without degrading the spectral resolution, effectively increasing the
brightness limit by $\sim$3 magnitudes. Because of mechanical constraints,
i.e. clearance between the dual wheel and the optomechanical bench, the
GR700XD could not be made thick enough to separate all orders completely. As a
result, order 2 is slightly overlapping with order 1 around 2.2 $\mu$m. The
SOSS data pipeline features special algorithms for locating all spectral
traces (Radica et al., 2022) and to correct the inter-order contamination that
turns out to be of minimal scientific impact even uncorrected (Darveau-Bernier
et al., 2022).

###  3.4 Aperture Masking Interferometry

AMI enables high-contrast (10${}^{-3}-10^{-4}$) imaging between 2.8 and 4.8
$\mu$m at inner working angles (IWA) between 70 and 400 mas. Such high-
contrast sub- $\lambda/D$ imaging capability (Sivaramakrishnan2022 and
references therein) is unique and very complementary to the NIRCam coronagraph
that includes occulting spots with IWA between 300 and 880 mas over the same
wavelength range (Rieke et al., 2023). AMI probes a parameter space (IWA$<$200
mas at 4-5 $\mu$m) hardly accessible from ground-based 8-10 m class
instruments like the Gemini Planet Imager (Macintosh et al., 2014), SPHERE
(Beuzit et al., 2019) and NIRC2.

AMI is implemented through a 7-hole non-redundant mask (see Figure 1)
optimized for detecting point sources, but it can also be used for extended
sources such as transition disks and active galactic nuclei; examples of such
applications are given in Sivaramakrishnan et al. (2023). AMI can be used with
F277W and three medium-band ($R=16-20$) filters (F380M, F430M, F480M) whose
wavelengths were specifically chosen to provide good constraints on both the
effective temperature and surface gravity of relatively cold exoplanets and
brown dwarfs with clear atmospheres (see Figure 13 from Doyon et al. (2012)).

KPI (Martinache (2010)) is the full aperture version of AMI, i.e. without an
NRM mask that also provides sub-$\lambda/D$ imaging but for fainter targets
albeit with lower contrasts. This mode is particularly appealing for detecting
close binary systems like brown dwarfs. This mode is not specific to NIRISS
and can be applied to NIRCam and MIRI but only NIRISS was used for testing KPI
during commissioning. Detailed results from NIRISS KPI observations are
presented in Kammerer et al. (2023).

##  4 Instrument Design

NIRISS was designed, manufactured, assembled and tested by Honeywell Aerospace
(Ottawa ON, Canada) under contract to CSA, with contributions from CSA, the
Université de Montréal, and the Herzberg Astronomy & Astrophysics Research
Centre of the National Research Council Canada (Victoria, BC).

###  4.1 Optical Design

NIRISS features an all-reflective optical design based on aspheric diamond-
turned Aluminium mirrors manufactured by Corning NetOptix (Keene NH, USA). A
layout of the optical design is shown in Figure 3. The NIRISS FOV is captured
by a pick-off (POM) mirror located at the telescope focus; the POM is mounted
on a linear stage to allow for potential focus adjustment of up to $\pm$5.2 mm
in the OTE focal plane. After the highly successful OTE commissioning campaign
the telescope focal position was found to provide excellent image quality and
no focus adjustment of the NIRISS POM was needed (McElwain et al., 2023). The
light then passes through a three-mirror assembly (TMA) whose output is a
39-mm diameter collimated beam that feeds the dual wheel (pupil then filter
wheel) followed by a TMA camera that refocuses light onto the detector at
F/8.8 to yield the nominal 65 mas image scale. The wavefront error (WFE) of
the whole optical train was measured at cryogenic temperature and varies
between 50 and 70 nm depending on the FOV point.

Figure 3: Schematic of the NIRISS optical design.

###  4.2 Mechanical Design

Both FGS and NIRISS share a common optomechanical bench made of Aluminium to
ease optical alignment from room to cryogenic temperature. Three titanium
kinematic mounts provide the interface to the carbon fibre structure of JWST’s
Integrated Science Instrument Module (ISIM). A mechanical layout of the NIRISS
side (without baffles) is shown in Figure 4 along with a flight hardware
photograph. Figure 5 shows photographs of some of the instrument’s key
subsystems: the detector focal plane assembly (FPA) and the dual wheel.

###  4.3 Electrical/Software Design

In addition to the FGS/NIRISS optical assembly (Figure 4), the other major
hardware component of NIRISS is the FGS/NIRISS electronics box located in the
ISIM Electrical Compartment (IEC). The IEC is located beside the cryogenic
ISIM structure supporting all the Science Instruments and is maintained at a
temperature of $\sim$280 K. Special radiators ensure that the thermal load on
the $\sim$ 40 K ISIM structure is minimized. The FGS/NIRISS Electronics Box is
organized as three independent sets of control electronics, one for each
guider and one for the (single string) NIRISS instrument. Each guider and
NIRISS has four identical printed circuit assemblies (a power control card, a
Spacewire interface card, a SIDECAR control card and a Focus Mechanism &
Housekeeping card. In addition, the NIRISS electronics box has a Dual Wheel
control card and a ‘blank’ card to terminate the control signals originally
intended for the Fabry-Perot etalon in the former TFI configuration.

Instrument control functions are performed by NIRISS Flight Software (FSW)
which is hosted in the ISIM Command & Data Handling Unit. Communication with
the FGS/NIRISS electronics is solely over a redundant Spacewire interface,
unlike the other JWST science instruments that make use of a 1553 interface
for command & telemetry and a Spacewire interface for image data flow. All
FGS/NIRISS mechanisms make use of stepper motors with resolvers to read back
their current positions. The gearing of the filter and pupil wheels is such
that the resolver completes one rotation as the wheel moves through each
filter/pupil position. The wheels also have a variable reluctance (VR) sensor
which triggers at the home positions of the wheels, in order for NIRISS FSW to
confirm filter/pupil position.

NIRISS FSW also provides temperature control for the focal plane array and, if
required, the SIDECAR ASIC. The FPA is maintained at $\sim$39 K, with $\sim$20
mK of drift depending on the thermal state of the observatory. The FPA
temperature can also drop by $\sim$30 mK during sub-array readouts.

NIRISS FSW also controls four redundant miniature incandescent lamps which
project into the NIRISS optical path upstream of the filter and pupil optical
elements. The illumination of the focal plane is non-uniform and the resulting
calibration image data is used to monitor relative changes. Two of the lamps
project through a comb filter (intended for the TFI configuration) and two are
unfiltered.

Figure 4: Left: solid rendering of the NIRISS optomechanical bench shown
without the baffle between the pick-off mirror and the collimator TMA. Right:
flight hardware photograph.  Figure 5: Left: focal plane assembly unit,
center: pupil side of the dual wheel and the filter wheel on the right.

##  5 In-flight NIRISS Performance

This section provides a high-level summary of the instrument performance
solely based on commissioning data that are compared to pre-flight
measurements. We will show that NIRISS performs significantly better than
predictions in general.

###  5.1 Detector

The NIRISS detector is controlled by an Application-Specific Integrated
Circuit (ASIC) called SIDECAR (Loose et al., 2006) which provides biases,
clocking and output digitization. The SIDECAR bias voltages affect the read
noise, dark current and dynamic range of the detector, thus best performance
is obtained when the detector and SIDECAR are optimized together. The flight
detector for NIRISS was characterized at Teledyne in their standard test set,
then more fully characterized at Honeywell with a representative SIDECAR ASIC.
The final optimization was done through an ASIC tuning procedure during the
CV3 test campaign in 2015. This procedure was not repeated on orbit since the
performance was very similar to during ground tests. The last pre-flight
detector tests were obtained during the OTIS test campaign in 2017 when the
whole observatory was tested at cryogenic temperature. We take the OTIS data
as our reference for pre-flight detector performance.

The NIRISS detector has a distinctive feature associated with an epoxy void.
While most of the Teledyne H2RG arrays on JWST have voids, the NIRISS void is
the largest, occupying 8% of the detector area (see Figure 2 of Albert et al.
(2023)). This void region has a dark current about a factor of two lower than
the rest of the detector. The detector performance is summarized in Table 2.
The in-flight correlated double sampling (CDS) read noise is marginally lower
compared to that at OTIS but the dark current is significantly higher by 0.005
e-/s. This increase is most likely associated with cosmic ray residuals not
properly handled by the jump detection algorithm during the up-the-ramp
sampling. This higher dark current probably explains the slightly higher in-
flight total noise (see Figure 6). This higher dark current has a marginal
scientific impact since relatively long integrations from WFSS programs are
background-limited.

Table 2: In-flight and Pre-flight (OTIS) Detector Performancea

Median stats | In-flight (39.0 K) | OTIS (38.0 K)
---|---|---
Dark current outside void e-/sb | 0.048 | 0.043
Dark current inside void e-/sb | 0.026 | 0.021
CDS Noise e-/sb | 17.06 | 17.25
Amplifier Crosstalk | 0.00028 | 0.00021
Inter-Pixel Capacitance (IPC) | 0.00464 | 0.00432
Number of hot ($>$0.5 e-/s) pixels | 6257 | 5874
Number of warm (0.1-0.5 e-/s) pixels | 8494 | 7071
Number of noisy ($>$30 ADU) pixels | 3072 | 3306

Note. — a Full-frame measurements with the NISRAPID read out mode.

b Assumes the measured conversion gain of 1.62 e-/ADU (analog to digital
unit).

Figure 6: Total noise vs exposure time. The on-orbit performance is slightly
worse than pre-flight. The most likely cause is uncorrected cosmic rays.

###  5.2 Image Quality

NIRISS image quality was characterized by measuring the encircled energy (EE),
ellipticity and full width at half maximum (FWHM) of the point spread function
(PSF) of bright stars at several FOV locations. As shown in Figure 7, the
measured EE exceeds pre-flight predictions from WebbPSF with hardly measurable
PSF variations across the FOV. Figure 8 is a qualitative illustration of the
excellent image quality for both imaging and WFSS. Such an exquisite image
quality performance is due to the excellent telescope wavefront error measured
to be diffraction-limited at 1.1 $\mu$m instead of the 2 $\mu$m requirement
(Menzel et al., 2023).

Figure 7: Left panel: encircled energy (EE) as a function of measurement
radius for filter F115W. Plus signs in black and blue represent measurements
for two different dither positions. For comparison, the red curve represents
the run of EE for the pre-launch WebbPSF model for F115W. The radii for EE
levels of 80% and 90% are indicated. Right panel: ellipticity measurements of
stars across the field of view for exposures with the F480M filter in
commissioning program 1464. The symbol size indicates the ellipticity, ranging
from 0.01 to 0.11. The mean ellipticity in this image is 0.03 $\pm$ 0.01. Note
the lack of any trend in ellipticity across the FOV. Figure 8: Left: F200W
image of a crowded star field, illustrative of the very uniform PSF across the
whole FOV. Middle and right: Same field with the GR150C/R grisms.

###  5.3 Throughput

The system throughput was measured for all filters, the GR150C/R and GR700XD
grisms. As presented below, all modes show a throughput improvement between 10
and 60% compared to the pre-flight Exposure Time Calculator (ETC). This
improved transmission is likely attributed to several factors: a combination
of better throughput from the telescope optics, the NIRISS TMA, the quantum
efficiency of the detector and the accumulation of margins in the throughput
budget.

####  5.3.1 Imaging

Table 3 reports the measured count rates relative to the pre-flight ETC and
the instrument team predictions. Overall, the measured throughput is 10-20%
better than predicted. Both the pre-flight ETC and the instrument team
predictions are in good agreement in general except for filters longward of
$\sim 2\,\mu$m, a discrepancy that has now been routed to an aperture
correction issue within the pre-flight ETC that has been fixed for the version
available from December 2022 onwards.

Table 3: Measured count rates relative to predictionsa

Filter | $\tau_{ETC}(\lambda)^{\rm b}$ | $\tau_{NIS}(\lambda)^{\rm c}$
---|---|---
F090W | 1.25 | 1.21
F115W | 1.26 | 1.21
F140M | 1.31 | 1.25
F150W | 1.29 | 1.23
F158M | 1.29 | 1.2
F200W | 1.14 | 1.07
F277W | 1.22 | 1.11
F356W | 1.24 | 1.10
F380M | 1.25 | 1.07
F430M | 1.23 | 1.07
F444M | 1.20 | 1.13
F480M | 1.27 | 1.08

Note. — a Measured from the flux standard LDS749B.

b Measured count rate relative to pre-flight ETC predictions.

c Measured count rate relative to NIRISS instrument team pre-flight
predictions.

####  5.3.2 WFSS

The throughput of both GR150 grisms was characterized over all short
wavelength filters by observing the spectroscopic standard WD 1657+343. As
shown in Figure 9, the throughput of both grisms is significantly better than
the pre-flight ETC predictions by 10-25%, as high as 50% near $0.8\,\mu$m, in
excellent agreement with the imaging data. GR150C has a slightly better
transmission at $<1.3\,\mu$m than GR150R, most likely due to a better coating.
GR150C should thus be preferred for observations requiring only one
orientation.

The absolute transmission of both grisms was measured by comparing the
relative signal with/without blocking filters on low background fields (see
section 5.6). The resulting transmission is $85\pm 3$%, in good agreement with
pre-flight measurements.

Figure 9: Measured count rates of both GR150 grisms relative to pre-flight ETC
predictions. Black squares are measurements inferred from imaging data.

####  5.3.3 SOSS

The GR700XD grism was characterized by observing the A1V star BD+60 1753
($J=9.61$) with and without the F277W filter, the latter allowing to eliminate
order 2 in the overlap region. As shown in Figure 10a, order 1 shows a
throughput 25% better than the pre-flight ETC predictions around the blaze
peak (1.3 $\mu$m). Compared to the ETC, order 2 shows an improved transmission
of $\sim$60% now understood to be a slight mismatch of 0.026 $\mu$m between
the in-flight and pre-flight measurements of the blaze wavelength. Figure 10b
shows the resulting photon conversion efficiency of the GR700XD for both
orders. Order 2 shows a significant transmission of $\sim$24% near H$\alpha$
(0.656 $\mu$m) which is a very good stellar activity indicator for low-mass
stars.

Figure 10: a) Measured divided by the predicted count rate of the SOSS grism
in both order 1 (black) and 2 (red), the latter shifted by 0.026 $\mu$m to
match the pre-flight blaze function with in-flight data. b) Resulting photon
conversion efficiency.

###  5.4 Wavelength Calibration

####  5.4.1 WFSS

The wavelength calibration of both GR150 grisms was achieved by observing the
compact planetary nebula SMP-LMC-58. Figure 11 presents a representative
spectrum with the GR150R grism, showing an excellent agreement with the model.
The inferred linear dispersions are $46.888\pm 0.009\,\AA/{\rm pix}$ and
$46.959\pm 0.017\,\AA/{\rm pix}$ for the GR150R and GR150C grims,
respectively, in excellent agreement with the optical model predictions. This
dispersion corresponds to a 2-pixel resolving power of 139 at 1.3 $\mu$m.

Figure 11: GR150R spectrum of a planetary nebula observed with all short-
wavelength filters.

####  5.4.2 SOSS

The SOSS wavelength calibration was achieved through spectral correlation with
the following stars: BD+601753 (A1V) and TWA33 (M5.5V). Hydrogen lines were
used to anchor the wavelength solution from the A star while a BT-Settl
atmosphere model (Allard et al., 2012) was used to derive the one from the M
dwarf. The dispersion shows some non-linearity (see Figure 12) due to optical
distortion within the SOSS FOV. The inferred median dispersions are $9.8\pm
0.3$ Å/pixel and $4.6\pm 0.2$ Å/pixel for orders 1 and 2, respectively, with
corresponding median 2-pixel resolving powers of $\sim$700 at blaze peak (1.3
$\mu$m for order 1 and 0.7 $\mu$m for order 2). The current wavelength
solution is accurate to $\sim$1/3 pixel over most of the SOSS spectral range
with some systematic mismatch at the level of $\sim$2 pixels around 2.3
$\mu$m. The wavelength solution should improve with more data during Cycle 1.

Figure 12: Linear dispersion of the SOSS order 1.

###  5.5 Astrometric calibration

The plate scale and optical distortion of the NIRISS FOV were characterized
through observations within an astrometric field located in the Large
Magellanic Cloud, a field extensively used and characterized for the Hubble
Space Telescope (see the Astrometric Calibration section of the JDox
documentation.). The resulting astrometric solution is accurate to 3
milliarcsec and repeatable to better than 1 milliarcsec. The measured plate
scale is $65.57\pm 0.04$ mas/pixel.

###  5.6 Background

Background measurements were obtained in five different low-background fields
with F115W and F200W in both imaging and GR150C/R spectroscopy. Each
measurement was compared with the prediction from the nominal background model
at the date of the observation, all yielding an average background of $0.80\pm
0.02$ relative to the nominal pre-flight background model. More details on
background measurements for all instruments are reported in Rigby et al.
(2023).

###  5.7 Scattered Light Issues

####  5.7.1 “Light saber”

Imaging observations during commissioning revealed a nearly horizontal
scattered light feature dubbed the “light saber” whose root cause was
identified, and very well modeled, as a rogue path originating from a
susceptibility region 3.5°$\times$0.5° located approximately 22° off-axis from
the telescope FOV. The rogue path beam enters directly through the telescope
entrance baffle, grazes off the wall following the NIRISS pickoff mirror, then
makes a double reflection on two of the camera TMA mirrors before hitting the
detector. The light saber is prominent when a very bright ($H_{vega}\sim 1$)
star is located anywhere within the susceptibility region but it is also
detected with zodiacal light only, albeit at a much fainter level (see Figure
13). The light saber feature from integrated zodiacal light is well
approximated by a two-dimensional, nearly Gaussian, function, hence can be
easily subtracted out through a simple model (see right panel of Figure 13).
The Astronomer Proposal Tool will also include a tool to warn users of a
potentially bright lightsaber signal. NIRCam is affected by similar scattered
light features of different shapes, also originating from a rogue path (Rieke
et al., 2023).

Figure 13: Left: F150W image stack showing a bright light saber horizontal
feature due to a bright ($H\sim 1$) star located within the out-of-field
susceptibility region. Middle: F200W image of a typical lightsaber feature due
to zodiacal background. Right: The same image as the middle panel but
corrected by a two-dimensional model of the lightsaber. The circular features
near the top left of the images are due to conical dimples directly engraved
on the pick-off mirror acting as occulting spots for the coronagraphic mode of
TFI (Beaulieu et al., 2008); those are permanent features of the NIRISS
flatfield.

####  5.7.2 Ghosts

NIRISS is affected by some image ghosts originating from internal reflections
within the optical components of the dual wheel. Their relative intensity is
field-dependent and varies between 0.1 and 4%. A given filter ghost is
characterized by a ghost axis point (GAP) corresponding to the intersection
point of all lines between the source and the ghost for different field
positions (see Figure 14). GAPs allow the user to predict ghost locations.
Ghosts intensity and their GAPs were characterized during commissioning and
previous ground-based tests campaigns. They can be partly mitigated through
dithering and most efficiently with the MEDIUM and LARGE dither patterns. AMI
is not affected by ghosts since those fall outside its small FOV (80$\times$80
pixelx). No significant ghost affects the SOSS mode. More details on NIRISS
ghosts are provided in the NIRISS Ghost section of the JDox documentation.

Figure 14: Left: F150W on-sky image of a field with several stars at different
field positions (cyan circles) and their corresponding ghosts (magenta
circles) connected by dashed white lines. The intersection of all lines
(magenta cross) corresponds to the Ghost Axis Point. Right: Zoomed-in view to
show the double nature of the ghosts for this particular filter. Figure from
the NIRISS Ghost section of the JDox documentation.

####  5.7.3 Extra Diffraction spike

Images of bright stars show a diffraction-like spike that rotates with field
position, mostly along the $X$ (V2) direction of the detector (see Figure 15).
Its intensity, brightest in F090W and decreasing towards longer wavelength
filters, is fainter ($\sim 70$%) than the regular diffraction spikes. In
practice, this feature has a very small, hardly measurable, impact on the
image quality.

A similar field-dependent feature is also observed in the two FGS guiders. The
most likely root cause of this effect for both instruments is small polishing
errors within the TMAs associated with the diamond-turning process for
manufacturing the FGS and NIRISS aluminum mirrors.

Figure 15: Top: Image of a bright star in F090W featuring an additional
diffraction spike at a position angle (PA) of +16° (11/5 o’clock). Bottom:
same image but at a different $X\sim 1400$ detector position, showing a
similar diffraction spike but at a different PA of $-8$° (1/7 o’clock)

##  6 NIRISS Science Performance

###  6.1 Imaging and WFSS Sensitivity

As described in previous sections, the measured throughput and image quality
of NIRISS are significantly (10-30%) better than pre-flight predictions,
particularly at short wavelengths, translating into improved sensitivity.
While the notional 10$\sigma$ 104 s sensitivity was not directly measured
during commissioning with 10 individual 1000 s exposures, one can make new
predictions based on in-flight measurements.

Table 4 compares the pre-flight and in-flight continuum sensitivity
predictions for all NIRISS filters. The new sensitivity assumes a zodiacal
background model flux at a date of June 19, 2023 at RA=17h26m50.96s and Dec =
-73°20′03.43″. The sensitivity improvement is typically $\sim$15% at long
wavelengths and between 24% (F200W) and 44% (F090W) in short-wavelength
filters. As shown in Figure 16, a similar improvement is observed for the WFSS
line flux sensitivity, varying between 20% in F200W and 38% for F090W.

Table 4: NIRISS Imaging sensitivity (10$\sigma\,10^{4}\rm s$)

Filter | ETC 1.0b | ETC 2.0b | $\Delta_{Sens}^{\rm c}$
---|---|---|---
| (nJy) | (nJy) | (%)
F090W | 15.3 | 10.6 | +44
F115W | 13.0 | 9.5 | +37
F140M | 17.6 | 12.9 | +36
F150W | 11.8 | 8.6 | +37
F158M | 16.1 | 12.1 | +33
F200W | 10.2 | 8.2 | +24
F277W | 13.8 | 11.9 | +16
F356W | 14.5 | 12.6 | +15
F380M | 37.3 | 32.9 | +13
F430M | 51.4 | 46.0 | +12
F444M | 22.8 | 19.9 | +15
F480M | 63.4 | 56.5 | +12

Note. — a Pre-flight ETC predictions.

b In-flight ETC predictions.

c Sensitivity improvement from pre-flight predictions
($\Delta_{Sens}=(ETC1.0-ETC2.0)/ETC1.0)$

Figure 16: Comparison of pre- and in-flight line-flux sensitivity of the WFSS
mode. The sensitivity improvement from pre-flight predictions varies between
20% for F200W to 38% for F090W.

###  6.2 Time Series Observations

The SOSS mode was tested and qualified through two time series observations
(TSO): one 4-hr long sequence on an early-type star (BD+601753, A1V $J=9.6$)
and a 6-hr long sequence on Hat-P-14, an F5V ($J=9.1$) star hosting a gas
giant transiting exoplanet (Torres et al., 2010) expected to yield a
relatively small atmospheric signal at the level of 20-30 ppm. The latter was
also observed by NIRCam (Rieke et al., 2023; Schlawin et al., 2023) and
NIRSpec (Böker et al., 2023; Espinoza et al., 2023) to qualify their
respective TSO modes. A detailed description of the NIRISS SOSS mode and
flight performance is presented in Albert et al. (2023). Here we present a
high-level summary of the in-flight performance.

####  6.2.1 BD+601753

The TSO sequence on BD+601753 was used to qualify the stability and noise
performance of the SOSS mode. The sequence comprised 876 consecutive 22-sec
integrations (3 reads per integration) with maximal signal levels
approximately halfway to saturation. Except for a jump flux of $\sim$370 ppm
halfway through the sequence (integration 413), the signal was very constant
with a dispersion of 155 ppm within 5-10% of the expected noise for such white
light observations. The jump flux is thought to be attributed to a so-called
“tilt event” whereby one of the primary segments of the telescope suddenly
experiences a change in its tip/tilt configuration. A similar tilt event was
also observed during the NIRCam TSO of Hat-P-14 (Schlawin et al., 2023).
Thanks to the built-in weak lens in the GR700XD grism, a tilt event, or any
other significant wavefront error variation of the primary mirror segments can
be easily detected and accurately quantified with SOSS, either through the
second derivative of the trace profile along the spatial direction or with
principal component analysis (more details in Albert et al. (2023)). A
detailed analysis of the TSO has shown that the jump flux is slightly
chromatic due to the wavelength dependence of the diffraction-limited PSF in
the spectral direction.

Aside from the tilt event, the power spectrum of the TSO (see Figure 17) shows
some clear coherent variability most likely of astrophysical origin (stellar
pulsations) since a different power spectrum was observed on another star
(Hat-P-14). Correcting for the tilt event and binning with time yields a noise
varying as expected, i.e. as $1/\sqrt{N_{bin}}$ where $N_{bin}$ is the number
integrations, down to $\sim$15 ppm with $N_{bin}$=200.

Another evidence of the stellar origin of the observed variability is the fact
that the continuum slope was measured to be different in two stars. Indeed,
the first hour on BD+601753 showed a significant slope of $\sim$-2 ppm/min
while the whole 5-hr sequence was consistent with no variation (0.04$\pm$0.05
ppm/min). On the other hand, for HAT-P-14b, the first hour showed a continuum
slope of -0.4 ppm/min and 0.75$\pm$0.16 ppm/min for the whole sequence.

Figure 17: Power spectra of the TSOs of BD+6017653 and Hat-P-14 (corrected for
the transit depth) overlaid with the one sigma noise envelope. The two
different power spectra is suggestive of the stellar origin for these
variations.

####  6.2.2 Hat-P-14

A transit event of HAT-P-14b was captured by SOSS in June 2022 during a 6-hr
sequence consisting of 572 consecutive integrations with Ngroup$=6$. The
detailed analysis of this sequence in described elsewhere (Albert et al.,
2023). Like BD+601753, a tilt event was detected at the beginning of the
sequence (integration 60, $\sim\,-3$ hours before mid-transit) but a much
smaller one with a very minor impact on the white light curve (WLC). Similarly
to BD+601753, the power spectrum of the TSO of Hat-P-14, corrected for the
transit event, also show some coherent, albeit different, frequencies,
suggesting that Hat-P-14 is inherently variable. Overall, the best WLC fit
model is obtained with a Gaussian process regression. The resulting transit
spectra for both orders is shown in Figure 27 of Albert et al. (2023). Unlike
the flat spectrum observed between 2.8 and 4.8 $\mu$m with the NIRSpec G395
mode (Espinoza et al., 2023), the SOSS spectrum binned to R=50 shows some
significant deviations from flatness at the level of $\sim$100 ppm with median
transit depth uncertainties of 50 ppm.

The integration time of this TSO was deliberately chosen to reach near
saturation around the blaze peak (1.3 $\mu$m) to investigate the noise
behavior, i.e. the flattening of the signal-to-noise (SNR) for signal levels
close to saturation. It was found that the SNR is flattening for signals
higher than $\sim$35000 ADUs (56000 e-) i.e. 3/4 of the detector well dept
($\sim$72000 e-). While this requires further investigation with more data, it
is recommended to set an appropriate number of reads (Ngroups) to remain below
$\sim$50000 e- at the end of the integration to avoid potential SNR
saturation.

In summary, aside from tilt events, inherent stellar variability and SNR
saturation beyond a signal of $\sim$50000 e-, no significant source of
systematic noise of instrumental origin could be detected in both TSOs. This
demonstrates the power of SOSS not only to deliver high-accuracy
spectrophotometry for its main intended application, transit/eclipse
spectroscopy of relatively bright stars, but also for other applications like
asteroseismology. SOSS observations allows disentangling genuine variable
signals of astrophysical origin from systematic flux variations associated
with wavefront error changes of the telescope.

###  6.3 Companion Detection with AMI and KPI

The point source detection capability of AMI was tested on the young quadruple
system AB Dor featuring two binary pairs: AB Dor A/C and AB Dor Ba/Bb
separated by 9″(Close et al., 2005; Guirado et al., 2006; Azulay et al.,
2017). AB Dor A/C is a low-mass companion near the hydrogen burning limit at a
current separation of $\sim$0.3 arcsec with a contrast of 4.5 mag, well within
the AMI detection sensitivity.

AB Dor A was observed at two epochs during commissioning (PID 1093) in all
three AMI filters (F380M, F430M, F480M) along with the star HD37093 of similar
spectral type (K5III) used as a calibrator. Both targets were positioned on
the 80$\times$80 AMI subarray with the standard target acquisition procedure
accurate to $\sim 1/20^{th}$ of a pixel. Figure 18 (left panel) shows the
$\chi^{2}$ map of the point source model fitting both the closure phases and
visibility amplitudes computed with
fouriever333https://github.com/kammerje/fouriever(Sivaramakrishnan et al.,
2023). AB Dor C was unambiguously detected with a contrast of 4.51$\pm$0.05, a
separation $\rho$ and position angle $\phi$ of 326.8$\pm$0.5 mas and
-80.1$\pm$.4°, respectively, in reasonable agreement with the measured
$L$-band contrast of 4.2$\pm$0.3 mag (Azulay et al., 2017) and separation
$\rho=369.4\pm$15 mas and position angle $\phi=$-78.3$\pm$20° predicted from
the dynamical model (private communication). The depth of the AB Dor
observations ($\sim 10^{8}$ photons) enables a 3$\sigma$ companion detection
limit of $\sim$7.5 mag near $\sim$200 mas (Sivaramakrishnan et al., 2023),
between $0.5-1$ mag worse than theoretical predictions.

Figure 18: Left: $\chi^{2}$ map of the best fit model of the observed closure
phase and visibility for the star AB Dor, known to host a companion $\sim$350
mas and 4.5 magnitudes fainter. The companion is unambiguously detected with
the expected contrast $f$, position angle $\phi$ and a separation $\rho$
within 3$\sigma$ of the prediction. Right: Similar $\chi^{2}$ map with the KPI
point source detection algorithm applied to the star CPD-66-562 showing a
$\sim$1:5 contrast companion detected at $\sim$150 mas.

The KPI point detection capability was also tested using four stars with
unknown binary statuses, all acting as calibrators with one another. As shown
in Figure 18 (right panel), one target, CPD-66-562, was serendipitously
discovered to have a companion at 150 mas with a contrast of 1.8 mag. Another
companion with a contrast of $\sim$5.6 mag was also detected around
2MASSJ062802.01-663738 at a separation of $\sim$240 mas. From these
observations, one infers a 5-$\sigma$ companion detection limit of $\sim$6.5
mag at $\sim$200 mas and $\sim$7 mag at $\sim$400 mas (Kammerer et al., 2023).
Both KPI and AMI are complementary with one another, the former better
optimized for faint targets with companions beyond $\sim$325 mas while AMI
should be preferred to search for relatively faint companions inward of
$\sim$325 mas around bright targets.

##  7 Summary

We have presented an overview of the NIRISS instrument, a description of its
design, and four observing modes: imaging, wide-field slitless spectroscopy,
single-object slitless spectroscopy, and aperture masking interferometry. In-
flight data have qualified all four modes with performance exceeding pre-
flight predictions. NIRISS shows a significantly better response at short
wavelengths where most of its science programs are concentrated. More
specifically, the imaging and WFSS modes show improved sensitivities between
10 and 40% and the SOSS mode has an improved throughput of $\sim$60% in the
second order. Time series observations with SOSS show very stable spectro-
photometric performance within 10-20% of the expected noise. Finally, the
first companion detection through space-based aperture masking interferometry
was demonstrated with NIRISS.

We thank the anonymous referee for constructive comments and suggestions that
improved the quality of the paper. NIRISS and FGS are the fruit of more than
two decades of teamwork by hundreds of persons of all origins and diversity
across Canada at the CSA, industry (in particular Honeywell Aerospace),
universities, the National Research Council Canada (NRC) in collaboration on
the JWST project with thousands of others from NASA, ESA and US/European
institutions. Special thanks to Paul Kuzmenko and Erich Bach for their
dedication in the manufacture and timely delivery of the NIRISS grisms. The
Canadian contribution to JWST would not have been possible without the
scientific vision and leadership of Simon Lilly. We are grateful to the late
Phil Sabelhaus, former Project Manager of JWST, Bill Ochs and the JWST Science
Working Group for their enabling decisions at critical times during the
Project that contributed to the successful and timely transition between TFI
and NIRISS. The Canadian contribution to JWST results from an unprecedented,
and several times renewed, financial contribution from CSA. We also
acknowledge financial support from the Université de Montréal, NRC, the
Natural Science and Engineering Research Council of Canada, Développement
Économique Canada, the Fonds de Recherche Québécois - Nature, Technologie et
Santé and the Trottier Family Foundation. D.J. is supported by NRC Canada and
by an NSERC Discovery Grant. Space Telescope Science Institute is operated by
the Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-03127 for JWST. Finally, we thank the general public for their
general support and communicative enthusiasm for this historic space mission.

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