# The Near Infrared Imager and Slitless Spectrograph for the James Webb Space
Telescope \- II. Wide Field Slitless Spectroscopy

Chris J. Willott NRC Herzberg, 5071 West Saanich Rd, Victoria, BC V9E 2E7,
Canada  René Doyon Département de Physique and Observatoire du Mont-Mégantic,
Université de Montréal, C.P. 6128, Succ. Centre-ville, Montréal, H3C 3J7,
Québec, Canada  Institut de Recherche sur les exoplanètes, Université de
Montréal, Québec, Canada  Loic Albert Département de Physique and Observatoire
du Mont-Mégantic, Université de Montréal, C.P. 6128, Succ. Centre-ville,
Montréal, H3C 3J7, Québec, Canada  Institut de Recherche sur les exoplanètes,
Université de Montréal, Québec, Canada  Gabriel B. Brammer Cosmic Dawn Center,
Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, DK-2100
Copenhagen, Denmark  William V. Dixon Space Telescope Science Institute, 3700
San Martin Drive, Baltimore, MD 21218, USA  Koraljka Muzic CENTRA, Faculdade
de Ciências, Universidade de Lisboa, Ed. C8, Campo Grande, P-1749-016 Lisboa,
Portugal  Swara Ravindranath Space Telescope Science Institute, 3700 San
Martin Drive, Baltimore, MD 21218, USA  Aleks Scholz SUPA, School of Physics &
Astronomy, University of St Andrews, North Haugh, St Andrews, KY16 9SS, United
Kingdom  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  Étienne Artigau Département de Physique and
Observatoire du Mont-Mégantic, Université de Montréal, C.P. 6128, Succ.
Centre-ville, Montréal, H3C 3J7, Québec, Canada  Institut de Recherche sur les
exoplanètes, Université de Montréal, Québec, Canada  Maruša Bradač Department
of Physics and Astronomy, University of California, Davis, One Shields Ave,
Davis, CA 95616, USA  Paul Goudfrooij Space Telescope Science Institute, 3700
San Martin Drive, Baltimore, MD 21218, USA  John B. Hutchings  NRC Herzberg,
5071 West Saanich Rd, Victoria, BC V9E 2E7, Canada  Kartheik G. Iyer Dunlap
Institute for Astronomy and Astrophysics, University of Toronto, 50 St George
Street, Toronto, ON M5S 3H4, Canada  Ray Jayawardhana Department of Astronomy,
Cornell University, Ithaca, New York 14853, USA  Stephanie LaMassa Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Nicholas Martis NRC Herzberg, 5071 West Saanich Rd, Victoria, BC V9E 2E7,
Canada  Institute for Computational Astrophysics and Department of Astronomy &
Physics, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3,
Canada  Michael R. Meyer Astronomy Department, University of Michigan, Ann
Arbor, MI 48109, USA  Takahiro Morishita Space Telescope Science Institute,
3700 San Martin Drive, Baltimore, MD 21218, USA  Lamiya Mowla Dunlap Institute
for Astronomy and Astrophysics, University of Toronto, 50 St George Street,
Toronto, ON M5S 3H4, Canada  Adam Muzzin Department of Physics and Astronomy,
York University, 4700, Keele Street, Toronto, ON MJ3 1P3, Canada  Gaël Noirot
Institute for Computational Astrophysics and Department of Astronomy &
Physics, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3,
Canada  Camilla Pacifici Space Telescope Science Institute, 3700 San Martin
Drive, Baltimore, MD 21218, USA  Neil Rowlands Honeywell Aerospace, 303 Terry
Fox Dr, Ottawa, ON K2K 3J1, Canada  Ghassan Sarrouh  Department of Physics and
Astronomy, York University, 4700, Keele Street, Toronto, ON MJ3 1P3, Canada
Marcin Sawicki Institute for Computational Astrophysics and Department of
Astronomy & Physics, Saint Mary’s University, 923 Robie Street, Halifax, NS
B3H 3C3, Canada  Joanna M. Taylor Space Telescope Science Institute, 3700 San
Martin Drive, Baltimore, MD 21218, USA  Kevin Volk Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Johannes Zabl
Institute for Computational Astrophysics and Department of Astronomy &
Physics, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3,
Canada

(Received 2021 November 16; Revised 2022 January 17; Accepted 2022 January 31)

###### Abstract

We present the wide field slitless spectroscopy mode of the NIRISS instrument
on the James Webb Space Telescope. This mode employs two orthogonal low-
resolution (resolving power $\approx 150$) grisms in combination with a set of
six blocking filters in the wavelength range 0.8 to $2.3\,\mu$m to provide a
spectrum of almost every source across the field-of-view. When combined with
the low background, high sensitivity and high spatial resolution afforded by
the telescope, this mode will enable unprecedented studies of the structure
and evolution of distant galaxies. We describe the performance of the as-built
hardware relevant to this mode and expected imaging and spectroscopic
sensitivity. We discuss operational and calibration procedures to obtain the
highest quality data. As examples of the observing mode usage, we present
details of two planned Guaranteed Time Observations programs: The Canadian
NIRISS Unbiased Cluster Survey (CANUCS) and The NIRISS Survey for Young Brown
Dwarfs and Rogue Planets.

Astronomical Instrumentation – Infrared Telescopes – Spectrometers

††journal: PASP

##  1 Introduction

The James Webb Space Telescope  (JWST) is a 6.5 m-aperture telescope that was
launched on 25 December 2021 to a halo orbit around the second Sun-Earth
Lagrange point. Equipped with a suite of near-infrared (NIR) to mid-infrared
(MIR) instruments it will be a revolutionary facility for furthering
humanity’s understanding of the cosmos. Some of the key features of JWST are
the large collecting area, high spatial resolution, low background from cooled
telescope and instruments and very stable image quality. The telescope was
designed to be a general purpose observatory with instrumentation offering a
large variety of observing modes to target objects from within our own solar
system to the furthest known galaxies in the early universe.

Within the wavelength range of 0.6 to 5.3 $\mu$m covered by the three NIR
instruments, JWST offers a total of eight spectroscopic modes. The Near
Infrared Imager and Slitless Spectrograph (NIRISS, Doyon et al. in
preparation; hereafter Paper I) has a single object grism spectrum mode (SOSS,
Albert et al. in preparation) for individual bright objects and a wide field
slitless spectroscopy mode (WFSS) utilizing two low-resolution ($R\approx
150$) grisms that is the focus of this paper. The Near Infrared Camera
(NIRCam) has two grisms operating at longer wavelengths than those of NIRISS
for both wide field and single object slitless spectroscopy (Greene et al.,
2017). The Near Infrared Spectrograph (NIRSpec, Jakobsen et al. 2022) has the
first space-based configurable multi-object spectrograph (MOS), a
$3^{\prime\prime}$ IFU, several fixed slits and a $1.6^{\prime\prime}$ square
aperture for time-series observations. For multi-object observations at 0.8 to
2.3 $\mu$m the two options are NIRISS WFSS and NIRSpec MOS. In this article we
describe in detail the capabilities and usage of NIRISS WFSS and where
appropriate contrast this with the alternative capability of NIRSpec MOS. For
a complete description of NIRSpec MOS we refer the reader to Ferruit et al.
(2022).

In slitless spectroscopy, light from the full field passes through a
dispersive element such as a grism and a spectrum of almost every source
within the field-of-view falls onto the detector. It is therefore a mode that
provides a very high multiplex gain with potentially thousands of spectra
recorded simultaneously. In such a slitless mode the full field sky background
emission is also dispersed by the grisms with the result that there is a
dispersed spectral background that is effectively the mean spectrum from many
background points. Hence the spectral background is about the same as the
imaging background and is higher than can be obtained in a configurable MOS.

With slitless spectroscopy there is no flux excluded from a finite slit width,
resulting in no PSF-dependent ‘slit losses’, accurate flux calibration and
time-series observations free of time-variable slit loss. For low spectral-
resolution observations, careful subtraction of the continuum can isolate maps
of line emission, making this mode especially useful for studies requiring
accurate emission line structure and/or ratios (Brammer et al., 2012a). With
the excellent angular resolution of the James Webb Space Telescope, scales of
a few hundred parsecs in galaxies at redshift $z=2$ can be mapped to study
their structural evolution.

Another benefit of slitless spectroscopy is that observations are quite simple
to execute. There is no need for pre-imaging of the field or accurate
astrometry of potential spectroscopic targets. No target acquisition is
required to place the targets within small apertures as the telescope pointing
uncertainty is a thousand times smaller than the field of view. These features
make wide field slitless spectroscopy a powerful mode for parallel
observations. With parallel observations, extra data can be obtained ‘for
free’ whilst another instrument is performing a prime observation. This
enables large survey programs covering an area that would be prohibitive to
perform with prime observations. It is therefore not surprising that NIRISS
WFSS has been allocated the majority of the pure parallel observing time
during the first year of JWST operations111https://www.stsci.edu/jwst/science-
execution/approved-programs/cycle-1-go.

Wide field slitless spectroscopy has been available on the Hubble Space
Telescope  (HST) using the ACS G800L, WFC3/UVIS G280, WFC3/IR G102 and G141
grisms. Scientific highlights of the usage of these HST grisms include
spectral template definition, binarity and variability of the coolest brown
dwarfs (Schneider et al., 2015; Buenzli et al., 2015), detailed
characterization of gravitationally-lensed star-forming galaxies (Brammer et
al., 2012b; Wang et al., 2020), spectroscopic confirmation of $z\sim 2$
clusters (Noirot et al., 2018), age-dating passive galaxies in $z\sim 2$
clusters (Newman et al., 2014) and in the field (Whitaker et al., 2013;
Estrada-Carpenter et al., 2019; Morishita et al., 2019), discovery of extreme
emission line galaxies (Atek et al., 2011), statistical studies of galaxy
evolution (Whitaker et al., 2014; Prusinski et al., 2021), discovering the
prevalence of inside-out star formation (Nelson et al., 2016), measuring
galaxy properties, including Ly$\alpha$ absorption by the intergalactic
medium, in the reionization epoch (Schmidt et al., 2016; Tilvi et al., 2016)
and breaking the record for most distant spectroscopic redshift of a galaxy at
$z=11$ (Oesch et al., 2016). The 3D-HST survey alone measured $\sim$100,000
spectroscopic redshifts with the WFC3/IR grisms (Momcheva et al., 2016),
vastly more than all ground-based near-IR spectroscopy combined. Given the
wealth of science done with HST slitless grisms, it can be expected that
NIRISS on board JWST will prove similarly productive.

Figure 1: Optical transmission of the NIRISS pick-off mirror. The black border
shows part of the region beyond the mirror edges. The outline of the region of
the POM that images onto the detector is displayed with a green box. The four
coronagraphic spots lie within the blue parallelogram. Numerous small features
are visible, particularly in the upper region that is in focus. However, most
of these features decrease transmission by only a few percent.

This paper is one of a series of five describing the NIRISS instrument, its
three main observing modes, and the Fine Guidance Sensor. In Section 2 we
describe aspects of the as-built hardware important for wide field slitless
spectroscopy and in Section 3 the expected on-sky performance based on ground-
test data. In Section 4 we discuss the operations concept including
calibration. Section 5 details the data processing and analysis steps. In
Section 6 we present two Guaranteed Time Observations (GTO) programs that
illustrate the science use of wide field slitless spectroscopy. Section 7
provides a summary. All figures of the NIRISS detector in this paper use the
Data Management System (DMS) orientation, such that the spacecraft +V3 axis is
up and the +V2 axis to the left. There is a $0.6^{\circ}$ rotation between the
V3-V2 axis and the NIRISS detector axis. Further information on NIRISS is
available on the JWST User Documentation222https://jwst-docs.stsci.edu site
hosted by the Space Telescope Science Institute.

##  2 NIRISS Hardware

###  2.1 Overview

NIRISS is a fully reflective instrument containing gold-coated mirrors for
high infrared throughput. It takes light from the pick-off mirror, passes it
through a collimator three-mirror anastigmat, then through a pupil wheel and
filter wheel. Next a camera three-mirror anastigmat focuses the light on to
the detector. The different imaging or spectroscopic observing modes are
selected only by rotating the pupil wheel and/or filter wheel to pass the
light through filters, grisms, the non-redundant mask and clear apertures.

Originally, the instrument was named the Tunable Filter Imager (TFI) and was
based around a Fabry-Perot etalon, enabling tunable narrow-band imaging.
However, to avoid schedule risk associated with this new technology, TFI was
converted into NIRISS in 2011 (Haley et al., 2012). The etalon and its order-
limiting filters were removed from the design and three grisms and a range of
wide- and medium-band filters were added to the pupil and filter wheels to
enable the NIRISS observing modes. NIRISS was tested in a series of cryo-
vacuum campaigns first at the David Florida Laboratory in Canada and later at
the Goddard Space Flight Center and the Johnson Space Center in the USA
between 2011 and 2017. Most results shown in this paper are from the third and
last Goddard cryo-vacuum test known as CV3 (Kimble et al., 2016).

###  2.2 Pick-off Mirror

The Pick-off Mirror (POM) feeds the light from the telescope into the
instrument. The POM transmission mask derived from ground test data is shown
in Figure 1. There are a few characteristics of the POM that are particularly
relevant for direct imaging and wide field slitless spectroscopy. The POM is
oversized compared to the detector to ensure that the detector area is fully
utilized. Therefore when a grism is inserted into the beam it is possible for
spectra from sources outside the imaging field of view to fall on the
detector. Additionally, the astrophysical background (see Section 4.2)
illuminating the POM will be dispersed onto the detector leading to a distinct
series of bands in the structure of the background corresponding to the filter
cutoffs of various spectral orders.

The original TFI design of the instrument incorporated a coronagraphic imaging
mode for high-contrast imaging. Four occulting spots with diameters
$0.58^{\prime\prime},0.75^{\prime\prime},1.5^{\prime\prime},2.0^{\prime\prime}$
were engraved into the POM. The spots reduce the flux for any object and the
background in those field locations. The effect of the coronagraphic occulting
spots will be clearly visible in NIRISS WFSS direct images and spectra.
Because of the relatively small dither patterns used in NIRISS WFSS there will
usually remain missing data at the locations of the larger spots, even after
dithering and combining exposures.

Several small spots and linear or curved features that decrease transmission
have been identified using ratios of dispersed and direct imaging flats that
eliminate quantum efficiency and detector defects from the resulting ratio
image (Figure 1). Most of these features decrease transmission by up to 10%,
although the largest feature to the lower-right of center blocks approximately
50%. The transmission is wavelength dependent with higher transmission at
longer wavelengths. Because of a designed tilt in NIRISS, only the upper part
of the POM is at focus on the detector, so such features are more readily
identified in the upper half of the image and more diffuse in the lower half.
These features are either surface defects in the gold coating or
contamination. They have been stable across multiple cryo-cycles, however an
expected shift in their position on the detector for changes in focus has been
observed. After NIRISS achieves best focus during commissioning, their
location and transmission are not expected to change significantly during the
JWST mission.

###  2.3 Detector

NIRISS employs a single Teledyne HAWAII-2RG chip (H2RG; Loose et al. 2003) as
its detector. It is sensitive to light with wavelengths from 0.6 to 5.3
$\mu$m, with quantum efficiency greater than 80% over most of that range. The
detector has an array of $2048\times 2048$ pixels of which an outer perimeter
of 4 pixels are reference pixels, not sensitive to light, leaving $2040\times
2040$ active pixels. With a scale of $0.065^{\prime\prime}$ per pixel, the
imaging and WFSS field-of-view is $2.2^{\prime}\times 2.2^{\prime}$. There is
a rotation of 1.2∘ between the alignment of the POM and the detector. We
briefly describe relevant aspects of the detector performance here while
further detail can be found in Paper I.

The H2RG detector is read out using non-destructive up-the-ramp sampling, with
one frame every 10.7 seconds in full-frame mode. There are two readout modes
available: NISRAPID and NIS. For NISRAPID, each frame is transmitted to the
ground, whereas for NIS, the onboard computer averages every 4 frames together
into a single group and only each group is transmitted to the ground. The
noise in data collected with NIS readout is expected to perform similarly to
NISRAPID, with the main difference being slightly worse performance in cosmic
ray identification, particularly when a cosmic ray impact occurs in the middle
of a group.

The selected detector has low dark signal of 0.39 electrons per frame
(equivalent to full-frame dark current of 0.036 e-/s) and low total noise in
full-frame-readout ramps (8.4 e- for a 1000 s ramp). For short ramps the $1/f$
noise is a major noise component and leaves significant stripes in the
calibrated (reference-pixel corrected) data. In imaging mode it is possible to
correct for some of this striping by masking objects and subtracting the
median of each column. The striping is more concerning for spectroscopy
because the spectral traces are approximately aligned with the detector rows
and columns when using the GR150C and GR150R grisms, respectively. However,
for long spectroscopic integrations that approach 1000 s and contain a typical
zodiacal and scattered light background, the striping will be almost
unnoticeable because the noise is dominated by the background. The high cosmic
ray rate at L2 will be an additional source of noise that affects the overall
sensitivity.

Figure 2: Layout of dispersed spectra across the full width of the NIRISS
detector compared to the direct imaging source position (top panel, also
marked by vertical dashed line in all panels). These ground-test observations
used an artificial star source at CV3. Locations of each order are labeled for
two of the six blocking filters. The NIRISS first-order spectra in each filter
are relatively short at $\sim 100$ pixels in length, resulting in much lower
source overlap than with other instruments, such as WFC3/IR and NIRCam. The
spectra of the diffuse object to the lower-left of the artificial star is the
result of the optical ghosts described in Section 3.

The NIRISS detector has good cosmetics with a low number of bad pixels (0.9%).
In addition to these, a further 2% of pixels show some excess noise in dark
exposures that may or may not be significant depending on the science case.
Ground testing showed that the bad pixel population changes with detector
temperature. However, it is planned that the NIRISS detector will operate at a
constant temperature for all of its lifetime, negating any such temperature-
dependent bad pixel changes. Bad pixel maps are available based on ground test
data and will be updated regularly after NIRISS reaches its final operating
temperature.

###  2.4 Grisms

Grism spectra from different objects often overlap, particularly in crowded
fields. The excellent sensitivity of NIRISS on JWST means that most fields
will be crowded for moderate to long exposure times. To alleviate this
problem, NIRISS employs two identical grisms, GR150C and GR150R, oriented with
orthogonal dispersion directions approximately aligned with the detector rows
and columns, respectively. Note that the ‘C’ and ‘R’ are reversed because the
grisms were named using the original detector orientation, before the switch
to the DMS orientation. Having two orthogonal grisms also allows one to
triangulate a position and wavelength when a source is only identified by a
single emission line.

The two GR150 grisms are blazed at a wavelength of 1.3 $\mu$m. The grisms have
a peak first-order transmission of 80% and first-order throughput above 50%
between 1.0 and 2.1 $\mu$m. The second order peaks at 0.68 $\mu$m. Further
details on the measured grism transmission versus wavelength are given in
Paper I.

Each grism provides a mean resolving power $(\lambda/\Delta\lambda)$ of 150
(in first order) over the wavelength range 0.8–2.3 $\mu$m. The dispersion is
$\sim$ 47 Å pixel-1 and two pixels sample one resolution element. The first
order spectrum at a wavelength of 1.047 $\mu$m is not deviated by the grism,
and falls on approximately the same pixel as in the direct images.

Ground tests have revealed that the filter-wheel mechanism can place either of
the grisms within $\pm 0.15^{\circ}$ of its nominal position. Offsets of the
grism element from the default location in the wheel impart a rotation to the
entire field of view. A trace solution has been developed that depends on the
position of the filter wheel determined from an encoder, and can be read from
the header keyword FWCPOS. In ground test data there is a good correlation
between FWCPOS and source trace rotation angle measured on the detector.
During commissioning the trace angles for many observations will be correlated
with FWCPOS to determine the relationship and its uncertainty. The data
processing software will use FWCPOS to determine the predicted trace location
and orientation.

###  2.5 Filters

The WFSS grisms in the NIRISS filter wheel must be crossed with one of the
band-limiting filters located in the pupil wheel. These filters restrict the
wavelength range of spectra and are one of the keys to mitigating source
overlap in very deep NIRISS spectroscopy. The first-order NIRISS WFSS spectra
are much shorter than those of the WFC3/IR grisms that do not utilize blocking
filters. There are six filters available for WFSS, four are broad-band (F090W,
F115W, F150W and F200W) and two are medium-band (F140M and F158M). Five of the
filters have the same specifications as filters in NIRCam, the exception being
F158M. Paper I tabulates the central wavelength, bandpass edges and average
transmission of all NIRISS filters. The “W” filter set is based roughly on
ground-based near-IR filters and not designed specifically for spectroscopic
band-limiting, so there are small gaps in wavelength between the edges of the
F115W and F150W filters and the F150W and F200W filters. Thus when using
several filters to obtain a spectrum with broad wavelength range there will be
small gaps in wavelength coverage and consequently certain lines will be
unobservable at certain redshifts (see Section 3.2). The layout of a dispersed
point source using each blocking filter is shown in Figure 2. The shortest
wavelength filter, F090W, has a bandpass from 0.8 to 1.0 $\mu$m. In this
filter the second order throughput is higher than the first order between 0.8
and 0.9 $\mu$m, so at these wavelengths both orders should be utilized for
optimum sensitivity.

##  3 Performance

###  3.1 Direct Imaging

To provide accurate wavelength calibration and spectral trace normalization
and location, it is important to have a direct image through the same filter,
without the grism in the optical path. In Cycle 1 it will be mandatory to
obtain NIRISS direct imaging in WFSS mode to ensure all data can be
calibrated. It may be possible to use other methods to provide a direct image
location such as (i) the zeroth order location, (ii) the intersection of the
two grism traces, (iii) imaging obtained with a different filter, (iv) imaging
obtained at a different time with NIRISS or NIRCam. However, the quality of
the calibration using other methods like these will need to be verified after
launch.

In slitless spectroscopy the spatial resolution of the imaging and
spectroscopic data are identical. In the six WFSS blocking filters the FWHM of
a point source ranges from 42 (F090W) to 65 (F200W) milli-arcseconds (mas).
However, the images will be under-sampled by the detector pixel scale of 65
mas per pixel. The telescope optics are not diffraction-limited at wavelengths
less than 2$\mu$m, such that there is a significant halo around the PSF core.
The expected 80% encircled energy radius ranges from $0.229^{\prime\prime}$
(F090W) to $0.168^{\prime\prime}$ (F200W), according to the WebbPSF simulation
tool (Perrin et al., 2014). The PSF will be regularly monitored for changes,
but is expected to be very stable. The WebbPSF tool allows users to generate
oversampled (sub-pixel) PSFs.

The imaging sensitivity of NIRISS is described in detail in Paper I. Here we
are interested in the imaging sensitivity for direct imaging to enable source
identification for spectroscopy. A general rule-of-thumb for low-resolution
grism spectroscopy is that the total direct imaging exposure time when
background-limited should be $\approx 10$% of the total spectroscopic exposure
time. For a spectrum of a continuum-dominated source that extends $\approx
100$ pixels in the spectral direction (compared to $\approx 2$ pixels for a
direct image), in the background-limited noise regime, this gives an imaging
S/N a factor of $\approx 15$ higher than the grism S/N per spectral pixel. If
the targets have photometry dominated by emission lines then the fraction of
time for direct imaging may need to be greater than $10$% to securely detect
such objects. Alternatively, emission lines may be identified solely in the
spectra by triangulation from their positions with the two orthogonal grisms.

For the case of shallow grism spectroscopy with few dithers and short exposure
times, the 10% rule for direct imaging may become extremely inefficient due to
a high fraction of time for exposure overheads and read-noise dominance at
short exposure times. In Figure 3 we show the F150W filter $10\sigma$ point-
source limiting magnitude for the case of four direct image dither positions
as a function of the number of groups per exposure and the total exposure
time. It can be seen that very short exposures should be avoided because of
the imaging depth achieved.

Figure 3: Point-source limiting magnitude in imaging mode with the F150W
filter as a function of number of groups per exposure in NISRAPID readout mode
for a 4 point dither pattern of direct imaging (blue). The upper axis shows
the total exposure time of the 4 exposures. The dashed black line shows the
scaling to shorter times if the exposures were background limited (setting
detector read-noise to zero). The dominant contribution of detector read-noise
for short exposures ($\rm{N_{group}}<20$) leads to a relatively bright
magnitude limit.

For fields containing bright objects, the user should be careful to avoid or
mitigate the effects of saturation in direct imaging. For standard imaging of
a single target the usual method to avoid saturation is to utilize a sub-array
on the detector that is read out in a fraction of the regular frame time of
10.7 s. Sub-array imaging is only available for NIRISS calibration
observations. For regular science all WFSS imaging and spectroscopy must use
the full detector readout. Saturation has two important consequences:
increased uncertainty on the centroid of an object and therefore the
astrometric/wavelength solution for this object on the corresponding grism
exposure and detector persistence that will be seen on the subsequent
exposure(s). The full-frame direct image point-source saturation limits for
the shortest possible integration, two groups in NISRAPID read-out, range from
AB magnitude 17.6 for F140M to 18.4 for F115W, F150W and F200W. For NIS read-
out the limits are 1.5 magnitudes fainter. Sources fainter than these limits
can have a flux measurement and accurate centroiding can be done for them.
Such sources may saturate in later reads in the ramp depending on the exposure
length, which can produce persistence in subsequent ramps. However this does
not need affect the flux and centroid measurements as these can be extracted
only from read-out groups recorded prior to saturation.

The band-limiting filters in NIRISS produce ghosting at the 0.1 to 2% level.
When using NIRISS in imaging mode it will be important to understand where in
the field the ghost of each bright source is located, so they are not
misidentified as independent, faint objects. Analysis of ground test data has
shown that the ghosts appear at predictable positions in the field. However,
ghost images of point sources do not appear simply as scaled versions of the
PSF. They are distorted, with field- and filter-dependent morphologies that
include multiple separate components. Therefore, modeling and subtraction of
ghosts will be imperfect, so mitigation by masking is likely to be the
preferred option, at least for early operations. During commissioning there
will be observations of a very large number of stars across the field that
will be used to characterize the ghosts in much more detail than was possible
in ground tests, potentially enabling modeling and subtraction in the future.

The NIRISS filters induce a small offset in the location of sources on the
detector. For all filters except F200W the offset is less than 1 pixel. For
the F200W filter the offset is 2.5 pixels ($0.16^{\prime\prime}$). The
astrometric distortion will be measured during commissioning using
observations of the JWST astrometric field in the Large Magellanic Cloud
(LMC). A separate World Coordinate System (WCS) solution for each filter will
be determined.

###  3.2 Spectroscopy

Figure 4: Point-source continuum limiting AB magnitude for S/N=10 per pixel in
WFSS mode with the GR150R or GR150C grism in combination with each of the six
blocking filters. The total exposure time per filter is 10,000 s. The spectrum
is extracted in a box with cross-dispersion aperture of $0.30^{\prime\prime}$.
For a resolved source, $n=1$ circular Sersic profile with effective radius of
$0.2^{\prime\prime}$ or $0.4^{\prime\prime}$, a similar S/N is achieved for a
source 0.4 or 0.8 magnitudes brighter, respectively. In these cases the
optimum extraction aperture is correspondingly larger. The limiting magnitudes
here only consider the first order spectrum. The second order has higher
transmission below 0.9 $\mu$m.  Figure 5: Point-source limiting emission line
flux for S/N=10 per pixel in WFSS mode with the GR150R or GR150C grism in
combination with each of the six blocking filters. The total exposure time per
filter is 10,000 s. The emission line is extracted in a $0.3^{\prime\prime}$
diameter circular aperture.

Figure 4 shows the $10\sigma$ per spectral pixel point-source limiting AB
magnitude in 10,000 s of background-limited grism spectroscopy for all six
blocking filters. The limiting magnitude ranges from 24 to 25 across most of
the wavelength range. Figure 5 shows a similar plot for emission line
sensitivity. The two Medium filters, F140M and F158M, have the highest
sensitivity due to reduced background. Each of these Medium filters requires
only half as long an integration time as the Wide F150W filter to reach the
same sensitivity. Therefore in a fixed observing time, the same sensitivity is
reached using either both Medium or one Wide filter. The Wide filter has
continuous coverage without potential bandpass edge effects, whereas the
Medium filters have shorter spectra giving less spectral overlap.

The full-frame GR150 point-source saturation limits for NIRISS in the six
filters used for WFSS range from AB magnitude 13.3 for F200W to 14.1 for
F115W. Brighter sources will saturate a large number of pixels in the first-
order spectrum, potentially leading to persistence in subsequent integrations.
It is advisable to avoid placing bright sources within the field, and just
outside the field but within the POM area where spectra can be dispersed onto
the detector.

The ghost of each bright source due to the NIRISS filters described in Section
3.1 also cause dispersed ghosts in the grism data (the spectral orders of the
ghost are visible below the main traces in Figure 2). Further in-flight data
is required to determine whether the ghost spectra are achromatic within each
filter. Since the flux in ghosts is a small percentage of the flux in sources,
the ghosts should not cause a significant increase in spectral contamination,
but may be somewhat more complex to model and subtract.

Figure 6: Spectral features of galaxies in NIRISS WFSS ‘W’ filters as a
function of redshift. For each of the four filters the range of redshift where
each feature is in the bandpass is plotted as a rectangle. NIRISS observations
with all four filters will contain at least one strong line at most redshifts.
The gray bands show redshift ranges where NIRISS WFSS covers all the
metallicity-sensitive lines [O iii], H$\beta$, [Ne iii] and [O ii].

Figure 6 shows the emission lines and spectral breaks that lie in the NIRISS
“W” filters for redshifted galaxies. At redshifts up to $z=3.5$ there are
multiple strong emission lines visible such as H$\alpha$, [O iii], H$\beta$,
or [O ii]. However the effect of the gaps between the F115W/F150W and
F150W/F200W combinations are evident as narrow redshift ranges where certain
lines are not observable with NIRISS. Unfortunately, the gaps for the two
strongest lines in high-$z$ galaxies (H$\alpha$ and [O iii]) line up for a
small range in redshift around $z=1.6$. Ly$\alpha$ enters the WFSS wavelength
range at $z>5.6$ and would be visible at most redshifts up to $z=17$, whenever
the line is transmitted through the intergalactic medium. The gray bands show
redshift ranges where all of the lines [O iii], H$\beta$, [Ne iii] and [O ii]
are visible within NIRISS WFSS filters. These strong lines, particularly the
flux ratios [O iii]/[O ii] and [Ne iii]/[O ii], can be used to measure high-
redshift gas-phase metallicities (Sanders et al., 2020). The H$\alpha$ and
H$\beta$ lines can both be observed between $z=0.7$ and $z=2.4$ to determine
the spatial distribution of star formation and nebular dust extinction.

##  4 Operations concept and calibration

Figure 7: Normalized NIRISS flat-field images from CV3 testing. The left side
is an imaging flat with F200W+CLEAR and the right side is a dispersed flat
with F200W+GR150R. The decreased flux regions from the four coronagraphic
spots lie within the blue parallelograms. These regions are seen as dark
circles in the imaging flat and negative first order spectra in the dispersed
flat. The horizontal bands in the dispersed flat are due to various spectral
orders being cut-off by the pick-off mirror, with the two strongest being the
first-order at the top of the detector and the zeroth-order at the bottom. The
slight tilt of the bands is due to the small rotation between the detector and
the pick-off mirror.

###  4.1 Observing Sequence and Dithers

As mentioned in Section 3.1, direct images are taken along with the grism
exposures to enable proper identification of the sources in the dispersed
images, and also to determine the absolute wavelength zeropoint for the
spectra. The NIRISS WFSS observing sequence in any desired filter consists of
a direct image (pre-imaging), followed by dithered grism exposures and then a
direct image (post-imaging) at the end of the sequence. Direct images are
obtained at both the start and the end of the sequence to enable generation of
a cleaner combined image by masking of bad pixels. The grism exposures can use
either one of the grisms or use the two grisms in succession by selecting the
BOTH option in the Astronomer’s Proposal Tool (APT). If multiple filters are
required for wavelength coverage, then a new filter is selected in the pupil
wheel and the entire sequence is repeated. The WFSS observing sequence is
designed to avoid any mechanism moves across dithers, which is important when
this mode is used for parallel observations.

NIRISS observing modes use dithers to mitigate detector artifacts, which
include bad pixels, persistence, flat-field errors, and pixel sensitivity
variations. Most importantly, dithering is necessary for the WFSS mode to
improve the sampling of the Point Spread Function (PSF). The NIRISS detector
has pixel sizes that critically sample the PSF at $\sim$ 4 $\mu$m, and the PSF
is heavily under-sampled at the wavelengths (0.8$-$2.3 $\mu$m) where the WFSS
mode operates. The WFSS observations are dithered by selecting from a set of
pre-defined dither patterns available in the APT. The dither patterns are
defined such that the large pixel offsets allow to mitigate the detector
artifacts, and the fractional pixel offsets are used to recover the PSF
sampling and structural information in the direct and dispersed images. The
WFSS dithers are specified by the number of dither positions (2, 3, 4, 6, 8,
12 or 16 steps) and the amplitude of the dither step (Small, Medium, and
Large). The Small pattern uses a dither step size of $\sim$ 0.3 arcsec, and
offers a higher conservation of pixel phase sampling in the presence of
varying geometric distortion. This option with small step size is useful for
sparse fields of point (or compact) sources. The Medium and Large dither
patterns use step sizes of $\sim$ 0.6 and $\sim$1.2 arcsec respectively, and
allow to step over larger features. The Medium pattern is appropriate for
extragalactic sources at moderate and high redshifts, while the Large pattern
is better-suited for large extended sources in an astronomical scene, such as
galaxies at low redshifts.

The choice of the number of WFSS dithers to use depends on the science program
and total observation time. As noted in Section 3, short exposures are
inefficient due to higher read noise and overheads. Individual exposures
should normally be in the range 400 to 1300 seconds long, with the upper limit
due to the cosmic ray hit rate to be confirmed during commissioning. If
astrometric and photometric precision are important for the science, more
dithers sampling different locations at the sub-pixel scale and different
pixels should be considered.

As illustrated in Figure 2, spectra obtained in filters F140M, F150W, F158M
and F200W are significantly offset from their direct image locations. The
consequence is that the spectra of some sources in the direct image will fall
off the detector and the spectra of some sources outside the direct image but
within the POM region will fall on the detector. This would result in only 76%
of sources in the direct image having complete spectra with both grisms and
all four W filters on the detector. To improve the overlap between imaged
sources and spectra, a grism- and filter-dependent offset observing sequence
is used for the case of the BOTH grisms option with the F140M, F150W, F158M or
F200W filters in the APT. By offsetting the field between the GR150R and
GR150C sequences a larger field is imaged, albeit at lower depth at the edges
with half coverage. This larger imaged field provides a greater overlap so 89%
of sources within a NIRISS-size field have full spectra and images. If the
user does not wish to use these offsets, then each grism should be selected
separately rather than selecting the BOTH grisms option.

Figure 8: Measurements of multiple orders of the spectral traces taken during
CV3. The five direct image positions of the calibration source per grism are
indicated with black filled squares. They are connected to their associated
trace by a small line segment. The cross dispersion scales of the traces and
offsets from the direct images have been increased by a factor of 50 for this
figure to amplify the small curvature. The grey square in each panel is the
detector outline. Although it may appear on this figure that some measured
traces fall off the detector, this is just an artifact of the factor of 50
increase in cross-dispersion scale. $\lambda^{+1}$ indicates the first order
with the arrow in the direction of increasing wavelength.

###  4.2 Background Subtraction

The major sources of NIR background for JWST are in-field zodiacal emission
and stray light. Stray light consists of both zodiacal light and galactic
starlight scattered into the field through rogue paths (Lightsey, 2016). It is
unknown whether there will be significant spatial structure in the background
across the NIRISS field (apart from that caused by effects that can be
corrected by flat-fielding) and whether this structure will vary with pointing
location. When a grism is inserted the background is dispersed across the
field and will show similar banding structure to the flat-field images in
Figure 7.

The high source density in NIRISS WFSS mode results in a high probability that
pixels near a science target trace will be contaminated by flux from other
sources. Therefore, one cannot use dedicated regions of the image for each
target to model the background and subtract it from the target. Instead the
process will be to generate a master background image from the combination of
many science images, masking out individual sources. The background level for
each exposure will be determined by measurement in source-free regions and the
master background scaled to this value before subtraction. This process has
been shown to be effective for WFSS with the WFC3/IR instrument. Its accuracy
for NIRISS will depend on the variation in background structure at different
pointings.

###  4.3 Flux Calibration

Due to the slitless nature of the WFSS mode, there are no slit losses and flux
calibration is relatively simple. Observations will be obtained during
commissioning and regular science operations of standard stars in imaging mode
and WFSS mode using all six filters to determine the efficiency of the various
orders of the grisms as a function of wavelength. Any field-dependence of the
spectral flux calibration (beyond that taken care of by flat-fielding) will be
checked for during commissioning by observing a field of stars.

###  4.4 Trace Calibration

During CV3 testing, trace calibration exposures of a bright continuum source
in all six filters and both grisms were obtained. The target was placed at
five locations within the field of view in order to investigate the position
dependence of the shape of the trace. In each individual exposure for the
unique combination of grism, filter, and target location, we measured the
centroid along the trace by fitting a Moffat (1969) function along the cross
dispersion axis. The traces in the four “W” filters are shown in Figure 8.
Each trace can be well reproduced by a single quadratic function across all
spectral orders and blocking filters. Residuals of the quadratic fit to the
measured trace are $\lesssim 0.1$ pix across the full width of the detector.
The direct image location was found to be offset by a few pixels from the
trace.

The shapes of the traces in the two grisms are distinct, with GR150C having
more curvature. The traces vary significantly across the field of view, with a
larger effect for GR150R. Note the general misalignment of the GR150R spectra
with the detector axes is accounted for by the filter-wheel mechanism offset
from its nominal position described in Section 2.3. During instrument
commissioning, the trace calibration (shape and field dependence) will be
verified and refined at a larger number of field points using stars
distributed throughout the NIRISS field.

###  4.5 Wavelength Calibration

In CV3 a monochromatic source was observed at a wavelength appropriate for
each filter and at the same positions across the field of view used for the
trace continuum source shown in Figure 8. With knowledge of the intrinsic
wavelength of the source and the geometry of the trace, the wavelength
calibration is defined by the offset along the trace between the position of
the monochromatic line image in the dispersed image and the position of the
source in the undispersed direct image. The CV3 data are consistent with a
linear dispersion of the first order spectra of 46.7 Å/pix and 46.8 Å/pix for
the GR150C and GR150R grisms, respectively. During instrument commissioning
the compact planetary nebula LMC SMP 58 (Sanduleak et al., 1978; Stanghellini
et al., 2002) will be observed to verify the wavelength calibration.

##  5 Data Processing and Analysis

###  5.1 JWST Pipeline Processing

The JWST Science Calibration Pipeline processes JWST data to remove and
calibrate instrumental signatures, combine relevant exposures, and perform
basic spectral and imaging source extraction. FITS file headers include
information from the instrument, observatory, and spacecraft in addition to
the relevant proposal details. The pipeline is modular and can be run by users
with options to specify their own reference files and configuration
parameters.

Direct images are processed as standard imaging files. They pass through the
first stage of the pipeline, calwebb_detector1, which processes the data from
raw non-destructively read ramps to uncalibrated slope images. First, dead,
hot, noisy and saturated pixels are flagged and a superbias frame is
subtracted. A reference pixel correction is applied using the border of non
light-sensitive pixels at the edge of the detector. A non-linearity correction
and optional persistence correction/flagging are applied. The dark signal is
then subtracted and cosmic rays are flagged. The final step in
calwebb_detector1 is to fit a slope to the reads of each pixel. If an exposure
contains multiple integrations, a file with the integrations averaged is also
output. The second pipeline stage, calwebb_image2, adds WCS information to the
file, applies a flat-field correction and adds flux calibration information to
the file. The third pipeline stage, calwebb_image3, combines multiple
exposures (from all dither positions) for each filter into a single, drizzled
(Fruchter & Hook, 1997) image. A catalog and segmentation image constructed
from this combined image are used to identify the position and extent of each
detected object.

Dispersed images pass through the same pipeline first stage as direct images,
but then move on to a different second stage, calwebb_spec2. WCS information,
including the computed position of the spectrum for each object in the direct
image source catalog, is written to the file. calwebb_spec2 masks regions of
the dispersed image that contain spectra, scales a master sky background image
(provided as a reference file) to the unmasked regions, and subtracts the
master sky background image from the entire field. A flat-field correction is
then applied. The flat-field reference file for each filter is based on the
imaging flat-field, but with the POM transmission features removed. It is
assumed the flat field is independent of wavelength within each filter
bandpass. The flat-fielded two-dimensional image can be optionally output here
in case the user wants to do further analysis on the full field, rather than
individual object spectra. The two-dimensional spectrum of each object in the
field is then extracted and written to a separate image extension in the
output FITS file, along with its WCS information. A simple model for spectral
contamination from overlapping sources is constructed for each extracted two-
dimensional spectrum and subtracted to get the clean, uncontaminated two-
dimensional spectrum of each object. The spectra are then flux calibrated and
a one-dimensional spectrum extracted.

For dispersed images, the third stage of the pipeline is calwebb_spec3. For
each object, the two-dimensional spectra from multiple exposures are combined
into a single rectified image (with dimensions of space and wavelength) using
a modified drizzle algorithm. Spectral combination is performed in a single
step to avoid noise propagation through multiple interpolations. The algorithm
employs an iterative sigma-clipping technique to reject any cosmic-ray
affected pixels that were not detected by calwebb_detector1. Products are
produced for each source with individual exposures in the extensions. From the
drizzled two-dimensional spectrum, a one-dimensional spectrum is extracted.

###  5.2 Post-Pipeline Processing and Analysis

Whilst the JWST pipeline performs a simple contamination correction and
outputs extracted one- and two-dimensional spectra, many users will require
more specialized software to analyze grism spectroscopy. This is particularly
going to be the case in crowded fields. Given the depth achieved by NIRISS
grism spectroscopy in moderate to long observations, even ‘blank’ fields may
become crowded and require more advanced treatment.

Table 1: CANUCS Prime Target Fields Cluster | RA | DEC | Redshift |  HST Survey
---|---|---|---|---
Abell 370 | 02:39:54.1 | -01:34:34 | 0.375 | Frontier Fields
MACS J0416.1-2403 | 04:16:09.4 | -24:04:21 | 0.395 | Frontier Fields
MACS J0417.5-1154 | 04:17:35.1 | -11:54:38 | 0.443 | RELICS
MACS J1149.6+2223 | 11:49:36.7 | +22:23:53 | 0.543 | Frontier Fields
MACS J1423.8+2404 | 14:23:47.8 | +24:04:40 | 0.545 | CLASH

The most effective method to simultaneously disentangle overlapping spectra
and determine the true nature of the source spectra is by forward modelling of
the scene (Brammer et al., 2012a). The direct image mosaic for a particular
filter provides an excellent prior on the locations and flux-density
distributions of the sources responsible for each spectrum. Unlike WFC3/IR on
HST, NIRISS utilizes the same blocking filters in spectroscopy as in direct
imaging, removing the need to interpolate between several imaging filters.

The shape of the spectra can be modelled using stellar or galaxy templates and
the best fit determined by iteratively minimizing the residuals. For galaxies
this can also include determining the redshift, potentially by including prior
information from photometry over a wider wavelength range than the grism
spectroscopy. After subtraction of the continua of the target of interest and
overlapping sources, emission lines may be identifiable to give more precise
redshifts. All 2D exposures using both grisms, potentially also observed at a
range of telescope orientations, can be fit simultaneously, in order to
minimize degeneracy between position and wavelength (e.g. Ryan et al. 2018;
Pirzkal et al. 2018), and enable efficient use of all data even when some is
contaminated. This process can result in maps of each emission line that are
particularly useful for subsequent science analysis.

Several software packages that perform analyses similar to described above are
available such as grizli333https://grizli.readthedocs.io/en/latest/ and
pyLINEAR444https://github.com/Russell-Ryan/pyLINEAR.

##  6 Planned Guaranteed Time Observations

The NIRISS Instrument Science Team will use part of their guaranteed JWST
observing time to highlight two of the many applications of NIRISS WFSS mode
observations. In this section we provide a high-level overview of these two
programs focusing on how WFSS observations enable unique science.

###  6.1 CANUCS: The Canadian NIRISS Unbiased Cluster Survey

As described in Section 1, wide-field grism observations with HST have proved
to be remarkably successful for addressing a number of science questions in
galaxy formation and evolution. With NIRISS on JWST the improved performance
in wavelength range, sensitivity and spatial resolution described in Section 3
will lead to new revelations.

The CAnadian NIRISS Unbiased Cluster Survey, or CANUCS555http://canucs-
jwst.com, will use about 200 hours of observatory time with the NIRISS, NIRCam
and NIRSpec instruments. The NIRISS and NIRCam observations are performed in
parallel at the same observatory orientation, so each target has a central
field and two flanking fields, one observed with NIRISS and one with NIRCam,
on either side. An example field layout for the CANUCS NIRISS and NIRCam prime
and parallel observations is shown in Figure 9. The central target fields for
CANUCS (Table 1) are massive galaxy clusters chosen for their excellent
gravitational lensing properties and extensive existing multi-wavelength data.
Lensing cluster fields were chosen over non-cluster fields in order to take
advantage of the lensing magnification, enabling the study of fainter and
smaller galaxies. A lower limit of $z>0.35$ for target clusters was adopted to
limit contamination from cluster galaxies and intracluster light, while also
ensuring that most of the highly-magnified image plane is located within a
single NIRISS field. Three of the clusters have extensive Hubble optical and
NIR imaging from the Hubble Frontier Fields Project (Lotz et al., 2017) and
the wider area BUFFALO survey (Steinhardt et al., 2020). The other two
clusters will be observed with 28 orbits each of ACS imaging in Hubble Cycle
29 program ID:16667 (PI: M. Bradač), expected to be completed before the
CANUCS JWST observations of these fields.

Each cluster field in CANUCS will be observed with NIRISS with both orthogonal
grisms (GR150C and GR150R) in the three filters; F115W, F150W and F200W. Total
spectroscopic exposure times will be 9.7 ks per configuration, resulting in
sensitivities similar to those shown in Figs. 4 and 5. Galaxy spectra
uncontaminated in both grisms will have sensitivity of a factor of $\sqrt{2}$
better.

Grism spectroscopy in the NIRISS flanking fields will cover a larger volume
than the cluster core fields (due to lower magnification) and therefore
increase the statistics for more luminous galaxies. The NIRCam flanking fields
will be imaged in 14 wide and medium filters: F090W, F115W, F140M, F150W,
F162M, F182M, F210M, F250M, F277W, F300M, F335M, F360M, F410M, F444W (10
filters with 2.9 hours total exposure and 4 filters with 1.6 hours). With
these data one can determine accurate photometric redshifts, strong emission
line equivalent width distributions, and emission line, star formation rate
and stellar mass maps (Sorba & Sawicki, 2018) up to redshift 9.

Figure 9: Field layout for the CANUCS observations of Abell 370. The
background greyscale is the BUFFALO ACS F814W image. NIRISS and NIRCam module
B are centered on the cluster core. The NIRCam observations are made with a
slightly larger dither pattern (INTRAMODULEX 6) and a small position offset so
that NIRCam images the region outside the NIRISS imaging field where spectra
are dispersed onto the detector. The NIRISS dispersion directions for the two
gratings are shown with arrows. The NIRCam prime dither pattern is clearly
visible (note that only the NIRCam Short Wave detectors, not Long Wave, are
shown for clarity). When NIRISS is prime the dithers are smaller and barely
perceptible on this plot. The full NIRCam Flanking Field is covered by BUFFALO
optical imaging. The three NIRSpec MSA configurations in the field are not
shown as their exact positions and orient angle will be defined later.

The main science goal of CANUCS is to understand the evolution of low mass
($10^{7}\raisebox{-2.58334pt}{$\,\stackrel{{\scriptstyle\raisebox{-0.60275pt}{$\textstyle<$}}}{{\sim}}\,$}M_{*}\raisebox{-2.58334pt}{$\,\stackrel{{\scriptstyle\raisebox{-0.60275pt}{$\textstyle<$}}}{{\sim}}\,$}10^{9}M_{\sun}$)
galaxies across cosmic time. Low mass galaxies typically reside in dark matter
halos with mass
$\raisebox{-2.58334pt}{$\,\stackrel{{\scriptstyle\raisebox{-0.60275pt}{$\textstyle<$}}}{{\sim}}\,$}10^{11}M_{\sun}$
that have relatively low global star formation efficiency (Moster et al.,
2013). Possible reasons for the inability of gas to cool and form stars in
these halos include feedback and outflows from supernovae, heating by the UV
background and/or cosmic rays and heating and evaporation during reionization.
By studying the physical properties of a large sample of such galaxies across
cosmic epochs we aim to determine their evolutionary pathways.

Figure 10: Simulated CANUCS observations of a strongly lensed $z=2$ galaxy in
the MACS J0416.1-2403 field. All panels are $8^{\prime\prime}\times
8^{\prime\prime}$ with North up and East to the left. The two left panels show
NIRISS (upper) and NIRCam (lower) 3-color images. The NIRISS filters include
strong rest-frame optical emission lines, whereas the NIRCam filters shown are
dominated by the stellar continuum. The inset image inside the yellow square
is the unlensed galaxy. The six spectral panels show the NIRISS spectra of the
two grisms combined with the three filters, all on the same intensity scale.
For the orientation of this galaxy, the emission lines are more easily seen
with the GR150R grism, however emission line maps can be generated for both
grisms after continuum subtraction. The galaxy-shaped objects visible in some
panels, particularly in GR150R with F150W and F200W, are zeroth order of other
galaxies in the field.

In crowded fields such as strong-lensing clusters, grism spectroscopy is an
efficient method to capture a spectrum of every galaxy in the field. By
avoiding the biases of pre-selection of certain types of galaxies (as required
for example with NIRSpec MOS) one obtains a sample of galaxies selected by
their emission line properties. This is particularly important in the JWST era
as previous work has shown that the equivalent width of emission lines in
star-forming galaxies increases significantly both with increasing redshift
and decreasing mass (van der Wel et al., 2011; Fumagalli et al., 2012; Stark
et al., 2013; Smit et al., 2014). Extreme emission line galaxies (rest-frame
equivalent width
$\,\stackrel{{\scriptstyle\raisebox{-0.60275pt}{$\textstyle>$}}}{{\sim}}\,$1000Å)
can be so dominated by lines that their continuum is undetectable. Such
objects are easily revealed by grism spectroscopy (e.g. Atek et al. 2011).

The small FWHM of NIRISS ($0.065^{\prime\prime}$ at 2 $\mu$m) enables high-
resolution studies of distant galaxies, particularly when combined with the
power of gravitational lensing. For a lensing magnification of a factor of
five, this FWHM corresponds to a physical scale of 100 pc for a $z=2$ galaxy.
Therefore with CANUCS we will be able to generate resolved maps of physical
properties (stellar mass, star formation rate, dust attenuation, nebular gas
metallicity, etc.) for small, low-mass galaxies to understand their evolution
including gas cycling with the circumgalactic medium. Individual star-forming
regions the size of globular clusters will be measurable in some favorable
lensing configurations (c.f. Vanzella et al. 2019).

The GR150 grism blaze wavelength of 1.3 $\mu$m was chosen to ensure the
highest sensitivity for the Lyman-$\alpha$ transition redshifted to $z\approx
10$. This redshift is prior to the majority of cosmic hydrogen reionization,
but late enough that the luminosity and space density of galaxies are both
high enough to enable a comprehensive study with JWST. NIRISS grism
spectroscopy is sensitive to faint Lyman-$\alpha$ emission lines, but also to
the Lyman-$\alpha$ continuum break in galaxies that do not show Lyman-$\alpha$
lines due to absorption in the IGM. With CANUCS we will search for ‘blind’
Lyman-$\alpha$ emitters such as have been seen up to $z=6$ by MUSE (Maseda et
al., 2018), and determine the frequency of spatial offsets between line and
continuum due to resonant scattering and obscuration (c.f. Hoag et al. 2019;
Lemaux et al. 2021) that may complicate ground-based slit and NIRSpec micro-
shutter-based Lyman-$\alpha$ spectroscopy.

In Figure 10 we show a simulated CANUCS observation of a strongly-lensed
galaxy at $z=2$. Mock JWST images of galaxies were created by combining the
state-of-the-art, publicly available simba cosmological simulation (Davé et
al., 2019) with the open-source dust radiative transfer package powderday
(Narayanan et al., 2021). We employed the $(25/h)^{3}$ Mpc3 simba box with
mass resolution of $\sim 10^{6}\,M_{\odot}$, meaning we can reasonably resolve
galaxies with masses $>5\times 10^{7}\,M_{\odot}$. The minimum smoothing
length for this simulation is $\sim 100$ pc, sufficient for a strongly-lensed
simulated galaxy. Spectra were assigned to each pixel of the full resolution
simulation by matching the specific star formation rate to $z=2$ galaxies from
the JAGUAR mock catalog (Williams et al., 2018).

The selected galaxy from the simulation has a star formation rate of
$13\,M_{\sun}\,{\rm yr}^{-1}$ and lies on the average $z=2$ star-forming main
sequence (Santini et al., 2017). The simulated galaxy is inserted twice into
the scene of the MACS J0416.1-2403 cluster, once at a position where it is
subject to strong lensing (total magnification = 8.7) and once at the corner
of the field with no lensing applied. Lensing is performed using the
Lenstronomy package (Birrer et al., 2021) with the Zitrin NFW version 3
lensing model for this cluster (Zitrin et al., 2013). The MACS J0416.1-2403
scene including these two galaxies was simulated using MIRAGE (Hilbert et al.,
2019) with the CANUCS observational parameters for NIRCam imaging and NIRISS
direct imaging and WFSS. The lensed galaxy has an observed F200W AB magnitude
of 20.3. The raw data were passed through the CANUCS data processing pipeline
to produce final data products. The dispersed spectra of the lensed galaxy in
Figure 10 illustrate how well NIRISS users will be able to make emission line
ratio maps of such galaxies (after continuum subtraction) for resolved studies
of the ionization and metallicity of nebular gas.

###  6.2 The NIRISS Survey for Young Brown Dwarfs and Rogue Planets

Brown dwarfs are a natural byproduct of star formation. About a quarter of the
objects found so far in nearby star forming regions are below the substellar
threshold with estimated masses $<0.08\,M_{\odot}$ (e.g., Andersen et al.
2008; Mužić et al. 2019). Some of them should have planetary masses below the
Deuterium burning limit at $M<0.013\,M_{\odot}$, corresponding to about
13$\,M_{\mathrm{Jup}}$ (Peña Ramírez et al. 2012, see review by Luhman 2012).
Some of these objects will have been formed like stars, from the collapse of
cloud cores, but others could be ejected (or rogue) planets. Based on deep
imaging of star forming regions, followed by spectroscopy of candidates, the
number of free-floating objects with planetary masses has been estimated as
2-5 per 100 stars (Scholz et al., 2012a). In young, dispersed associations,
the number of such objects could be significantly higher (Gagné et al., 2017).
So far, however, none of the direct detection surveys are sensitive below a
limit of $\sim 5\,M_{\mathrm{Jup}}$.

Figure 11: Simulated spectral data for young ultracool objects. Model spectra
from the BT-Settl series were smoothed to the WFSS resolution of $R=150$. For
the input spectrum with a temperature of 1000 K noise has been added to
achieve the expected minimum S/N of 10.  Figure 12: (a) Subaru $K_{s}$-band
image of the central part of NGC 1333, with the NIRISS WFSS mosaic overlaid.
The yellow circles mark the spectroscopically confirmed cluster members from
Luhman et al. (2016). (b) The grizli simulation with grism GR150C and filter
F150W based on the original image to which faint sources expected to be
detected by JWST were added. (c) Zoom into the two simulated WFSS fields
marked by the red rectangle in panel (b).

In extinction-free nearby regions, brown dwarfs and free-floating planets
separate sufficiently from background stars in broadband colors (typically red
optical/NIR bands) and in proper motion to reliably select them and derive
mass functions from imaging alone (e.g. Pinfield et al. 2000; Béjar et al.
1999; Lodieu et al. 2007). In younger regions with significant reddening,
however, young substellar objects mix in color-magnitude space with background
stars and embedded young stars. Therefore, extensive spectroscopic follow-up
is required (Luhman et al., 2003; Weights et al., 2009; Wilking et al., 2004).

Young brown dwarfs and giant planets have a characteristic NIR spectrum with
broad, gravity-dependent absorption bands due to water and methane, which are
easily distinguished from hotter photospheres with low-resolution ($R<1000$)
and moderate signal-to-noise ratio (S/N$\approx$10). The WFSS mode of NIRISS
is an attractive and very efficient option for discovering an unbiased sample
of these objects. WFSS has the obvious advantage of providing spectral
information with sufficient resolution for the entire population in the
targeted field, short-cutting the typical two-step strategy of photometry
first, followed by spectroscopy for color-selected candidates. A spectroscopic
survey with WFSS should be much less affected by incompleteness biases than
color-selected samples.

The NIRISS Survey for Young Brown Dwarfs and Rogue
Planets666https://www.stsci.edu/jwst/observing-programs/program-
information?id=1202 targets NGC 1333, a very compact young cluster. The
cluster has a population of $>100$ known young stars within a diameter of 12
arcmin (Scholz et al., 2012b; Luhman et al., 2016). The intra-cluster
extinction is moderate, with a typical $A_{v}$ of 5 magnitudes towards the
known stars and brown dwarfs. Contamination by scattered light from bright
stars is not a severe issue.

A Jupiter-mass object at age 1 Myr has an effective temperature of $\approx
1000$ K, using the AMES-Cond models (Baraffe et al., 2003). At the distance of
NGC 1333 of $\approx 300$ pc such an object should have a brightness of $H\sim
22$ and $K\sim 21$ mag777All magnitudes quoted in this subsection are in the
Vega system.. Using the JWST ETC (version 1.6), the total on-source exposure
times of $\sim 2900$ and 3100 s in the F150W (approximately $H$ band) and
F200W (approximately $K$ band), respectively, with the GR150C grism, will
achieve S/N=10 per spectral pixel. These two filters cover the most pronounced
broadband absorption features of ultracool dwarfs. In Figure 11 we show a BT-
Settl model spectrum for a young 1000 K source (Baraffe et al., 2015), plus
the expected noise, smoothed to the WFSS resolution $R=150$. For comparison,
model spectra for a slightly warmer and a slightly cooler object are plotted,
demonstrating the remarkable change in spectral shape as a function of
temperature.

The survey will cover a $3\times 3$ pointing NIRISS mosaic of NGC 1333 (with 2
tiles removed, one affected by bright stars, the other with very few known
cluster members), a total of about 34 square arcmin. These observations were
simulated using grizli, with the MOIRCS/Subaru $K_{s}$-band image of the
cluster (Scholz et al., 2009) as an input (Figure 12, left panel). This image
reaches $K_{s}\sim 21\,$, and is complete down to $K_{s}\sim 19$. JWST will
detect many more fainter objects, both in the cluster and in the field, and
this was accounted for in the simulations, to verify that the resulting image
would not be over-crowded. From the histogram of brightness of the sources
detected in Subaru images, the number of fainter sources expected to appear in
the WFSS observations was estimated by extrapolation, and artificially added
to the original image, evenly distributed across the field, assuming for
simplicity a flat spectral template. In total, the simulated frames contain
350 real and 500 artificial sources. The simulations (Figure 12, middle and
right panels) provide reassurance that crowding and overlapping spectra would
not be a significant issue for this particular setup.

The spectra will distinguish between reddened background stars or embedded
young stars and ultracool brown dwarfs - while the former have NIR spectra
that are consistent with reddened blackbodies, the latter will feature strong
absorption bands, as shown in Figure 11. Fitting the spectra with templates of
young planetary mass objects or model spectra will determine the effective
temperature and thus, in comparison with model isochrones, the mass. This
project will determine the frequency of young free-floating objects with
masses below the Deuterium burning limit down to a mass comparable to that of
Jupiter.

Star formation is expected to produce objects down to the opacity limit of
fragmentation. Simulations suggest that this limit is in the range of a few
Jupiter masses (Bate, 2012), but it has not been found yet empirically.
Dynamical simulations of planet ejections (Parker & Quanz, 2012; van Elteren
et al., 2019) suggest that significant numbers of ejected giant free-floating
planets should exist (10 to 20 per 100 stars), but these model estimates are
strongly dependent on assumptions about the architecture of the planetary
systems and the clustered environment. The WFSS observations in NGC1333 can be
used to compare with the existing simulations of star and planet formation, to
establish the low-mass limit for objects to form like stars, and to account
for the impact of ejections on the evolution of planetary systems.

##  7 Summary

The wide field slitless spectroscopy mode of the NIRISS instrument provides a
unique low-resolution spectroscopic option for users of the James Webb Space
Telescope. Key highlights include the ease of use with no target acquisition,
high multiplex factor and short spectral traces to minimize overlap. NIRISS
WFSS has the potential to address science ranging from the solar neighborhood
to the early Universe.

The filters covering the wavelength range $0.8\,-\,2.3\,\mu$m sample many
important rest-frame NIR, optical and UV emission lines for galaxies at a
range of redshifts plus diagnostic molecular features in ultracool dwarf
atmospheres.

In this paper we have detailed the pre-launch expected performance of the
observing mode and some of the most important operational and calibration
considerations for users. It is hoped that this information will encourage
prospective users to be less daunted by this observing mode and consider how
the mode can advance their science goals.

Special thanks to all our colleagues who have worked so hard over the last 20
years to design, build and test NIRISS. Thanks to Guido Roberts-Borsani for
assistance with the gravitational lensing simulation. We acknowledge support
from the Natural Sciences and Engineering Research Council of Canada (NSERC)
through a variety of its funding programs. This work is in part supported by
Canadian Space Agency grant 18JWSTGTO1. CP, SR and KV are supported by the
Canadian Space Agency under a contract with NRC Herzberg Astronomy and
Astrophysics. KM acknowledges funding by the Science and Technology Foundation
of Portugal (FCT), grants No. IF/00194/2015, PTDC/FIS-AST/28731/2017,
UIDB/00099/2020. JWST (NIRISS)

## References

  * Andersen et al. (2008) Andersen, M., Meyer, M. R., Greissl, J., & Aversa, A. 2008, ApJ, 683, L183, doi: 10.1086/591473
  * Atek et al. (2011) Atek, H., Siana, B., Scarlata, C., et al. 2011, ApJ, 743, 121, doi: 10.1088/0004-637X/743/2/121
  * Baraffe et al. (2003) Baraffe, I., Chabrier, G., Barman, T. S., Allard, F., & Hauschildt, P. H. 2003, A&A, 402, 701, doi: 10.1051/0004-6361:20030252
  * Baraffe et al. (2015) Baraffe, I., Homeier, D., Allard, F., & Chabrier, G. 2015, A&A, 577, A42, doi: 10.1051/0004-6361/201425481
  * Bate (2012) Bate, M. R. 2012, MNRAS, 419, 3115, doi: 10.1111/j.1365-2966.2011.19955.x
  * Béjar et al. (1999) Béjar, V. J. S., Zapatero Osorio, M. R., & Rebolo, R. 1999, ApJ, 521, 671, doi: 10.1086/307583
  * Birrer et al. (2021) Birrer, S., Shajib, A., Gilman, D., et al. 2021, The Journal of Open Source Software, 6, 3283, doi: 10.21105/joss.03283
  * Brammer et al. (2012a) Brammer, G. B., van Dokkum, P. G., Franx, M., et al. 2012a, ApJS, 200, 13, doi: 10.1088/0067-0049/200/2/13
  * Brammer et al. (2012b) Brammer, G. B., Sánchez-Janssen, R., Labbé, I., et al. 2012b, ApJ, 758, L17, doi: 10.1088/2041-8205/758/1/L17
  * Buenzli et al. (2015) Buenzli, E., Marley, M. S., Apai, D., et al. 2015, ApJ, 812, 163, doi: 10.1088/0004-637X/812/2/163
  * Davé et al. (2019) Davé, R., Anglés-Alcázar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827, doi: 10.1093/mnras/stz937
  * Estrada-Carpenter et al. (2019) Estrada-Carpenter, V., Papovich, C., Momcheva, I., et al. 2019, ApJ, 870, 133, doi: 10.3847/1538-4357/aaf22e
  * Ferruit et al. (2022) Ferruit, P., Jakobsen, P., Giardino, G., et al. 2022, A&A, in press
  * Fruchter & Hook (1997) Fruchter, A., & Hook, R. N. 1997, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 3164, Applications of Digital Image Processing XX, ed. A. G. Tescher, 120–125, doi: 10.1117/12.292751
  * Fumagalli et al. (2012) Fumagalli, M., Patel, S. G., Franx, M., et al. 2012, ApJ, 757, L22, doi: 10.1088/2041-8205/757/2/L22
  * Gagné et al. (2017) Gagné, J., Faherty, J. K., Mamajek, E. E., et al. 2017, ApJS, 228, 18, doi: 10.3847/1538-4365/228/2/18
  * Greene et al. (2017) Greene, T. P., Kelly, D. M., Stansberry, J., et al. 2017, Journal of Astronomical Telescopes, Instruments, and Systems, 3, 035001, doi: 10.1117/1.JATIS.3.3.035001
  * Haley et al. (2012) Haley, C., Roy, N., Osman, Z., Rowlands, N., & Scott, A. 2012, in Space Telescopes and Instrumentation 2012: Optical, Infrared, and Millimeter Wave, ed. M. C. Clampin, G. G. Fazio, H. A. MacEwen, & J. M. O. Jr., Vol. 8442, International Society for Optics and Photonics (SPIE), 1266 – 1274, doi: 10.1117/12.927026
  * Hilbert et al. (2019) Hilbert, B., Sahlmann, J., Volk, K., et al. 2019, spacetelescope/mirage: First github release, v1.1.1, Zenodo, doi: 10.5281/zenodo.3519262
  * Hoag et al. (2019) Hoag, A., Treu, T., Pentericci, L., et al. 2019, MNRAS, 488, 706, doi: 10.1093/mnras/stz1768
  * Jakobsen et al. (2022) Jakobsen, P., Ferruit, P., Alves de Oliveira, C., et al. 2022, A&A, in press
  * Kimble et al. (2016) Kimble, R. A., Vila, M. B., Van Campen, J. M., et al. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9904, Space Telescopes and Instrumentation 2016: Optical, Infrared, and Millimeter Wave, ed. H. A. MacEwen, G. G. Fazio, M. Lystrup, N. Batalha, N. Siegler, & E. C. Tong, 990408, doi: 10.1117/12.2231554
  * Lemaux et al. (2021) Lemaux, B. C., Fuller, S., Bradač, M., et al. 2021, MNRAS, 504, 3662, doi: 10.1093/mnras/stab924
  * Lightsey (2016) Lightsey, P. A. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9904, Space Telescopes and Instrumentation 2016: Optical, Infrared, and Millimeter Wave, ed. H. A. MacEwen, G. G. Fazio, M. Lystrup, N. Batalha, N. Siegler, & E. C. Tong, 99040A, doi: 10.1117/12.2233062
  * Lodieu et al. (2007) Lodieu, N., Hambly, N. C., Jameson, R. F., et al. 2007, MNRAS, 374, 372, doi: 10.1111/j.1365-2966.2006.11151.x
  * Loose et al. (2003) Loose, M., Farris, M. C., Garnett, J. D., Hall, D. N. B., & Kozlowski, L. J. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4850, IR Space Telescopes and Instruments, ed. J. C. Mather, 867–879, doi: 10.1117/12.461796
  * Lotz et al. (2017) Lotz, J. M., Koekemoer, A., Coe, D., et al. 2017, ApJ, 837, 97, doi: 10.3847/1538-4357/837/1/97
  * Luhman (2012) Luhman, K. L. 2012, ARA&A, 50, 65, doi: 10.1146/annurev-astro-081811-125528
  * Luhman et al. (2016) Luhman, K. L., Esplin, T. L., & Loutrel, N. P. 2016, ApJ, 827, 52, doi: 10.3847/0004-637X/827/1/52
  * Luhman et al. (2003) Luhman, M. L., Satyapal, S., Fischer, J., et al. 2003, ApJ, 594, 758, doi: 10.1086/376965
  * Maseda et al. (2018) Maseda, M. V., Bacon, R., Franx, M., et al. 2018, ApJ, 865, L1, doi: 10.3847/2041-8213/aade4b
  * Moffat (1969) Moffat, A. F. J. 1969, A&A, 3, 455
  * Momcheva et al. (2016) Momcheva, I. G., Brammer, G. B., van Dokkum, P. G., et al. 2016, ApJS, 225, 27, doi: 10.3847/0067-0049/225/2/27
  * Morishita et al. (2019) Morishita, T., Abramson, L. E., Treu, T., et al. 2019, ApJ, 877, 141, doi: 10.3847/1538-4357/ab1d53
  * Moster et al. (2013) Moster, B. P., Naab, T., & White, S. D. M. 2013, MNRAS, 428, 3121, doi: 10.1093/mnras/sts261
  * Mužić et al. (2019) Mužić, K., Scholz, A., Peña Ramírez, K., et al. 2019, ApJ, 881, 79, doi: 10.3847/1538-4357/ab2da4
  * Narayanan et al. (2021) Narayanan, D., Turk, M. J., Robitaille, T., et al. 2021, ApJS, 252, 12, doi: 10.3847/1538-4365/abc487
  * Nelson et al. (2016) Nelson, E. J., van Dokkum, P. G., Förster Schreiber, N. M., et al. 2016, ApJ, 828, 27, doi: 10.3847/0004-637X/828/1/27
  * Newman et al. (2014) Newman, A. B., Ellis, R. S., Andreon, S., et al. 2014, ApJ, 788, 51, doi: 10.1088/0004-637X/788/1/51
  * Noirot et al. (2018) Noirot, G., Stern, D., Mei, S., et al. 2018, ApJ, 859, 38, doi: 10.3847/1538-4357/aabadb
  * Oesch et al. (2016) Oesch, P. A., Brammer, G., van Dokkum, P. G., et al. 2016, ApJ, 819, 129, doi: 10.3847/0004-637X/819/2/129
  * Parker & Quanz (2012) Parker, R. J., & Quanz, S. P. 2012, MNRAS, 419, 2448, doi: 10.1111/j.1365-2966.2011.19911.x
  * Peña Ramírez et al. (2012) Peña Ramírez, K., Béjar, V. J. S., Zapatero Osorio, M. R., Petr-Gotzens, M. G., & Martín, E. L. 2012, ApJ, 754, 30, doi: 10.1088/0004-637X/754/1/30
  * Perrin et al. (2014) Perrin, M. D., Sivaramakrishnan, A., Lajoie, C.-P., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9143, Space Telescopes and Instrumentation 2014: Optical, Infrared, and Millimeter Wave, ed. J. Oschmann, Jacobus M., M. Clampin, G. G. Fazio, & H. A. MacEwen, 91433X, doi: 10.1117/12.2056689
  * Pinfield et al. (2000) Pinfield, D. J., Hodgkin, S. T., Jameson, R. F., et al. 2000, MNRAS, 313, 347, doi: 10.1046/j.1365-8711.2000.03238.x
  * Pirzkal et al. (2018) Pirzkal, N., Rothberg, B., Ryan, R. E., et al. 2018, ApJ, 868, 61, doi: 10.3847/1538-4357/aae585
  * Prusinski et al. (2021) Prusinski, N. Z., Erb, D. K., & Martin, C. L. 2021, AJ, 161, 212, doi: 10.3847/1538-3881/abe85b
  * Ryan et al. (2018) Ryan, R. E., J., Casertano, S., & Pirzkal, N. 2018, PASP, 130, 034501, doi: 10.1088/1538-3873/aaa53e
  * Sanders et al. (2020) Sanders, R. L., Shapley, A. E., Reddy, N. A., et al. 2020, MNRAS, 491, 1427, doi: 10.1093/mnras/stz3032
  * Sanduleak et al. (1978) Sanduleak, N., MacConnell, D. J., & Philip, A. G. D. 1978, PASP, 90, 621, doi: 10.1086/130397
  * Santini et al. (2017) Santini, P., Fontana, A., Castellano, M., et al. 2017, ApJ, 847, 76, doi: 10.3847/1538-4357/aa8874
  * Schmidt et al. (2016) Schmidt, K. B., Treu, T., Bradač, M., et al. 2016, ApJ, 818, 38, doi: 10.3847/0004-637X/818/1/38
  * Schneider et al. (2015) Schneider, A. C., Cushing, M. C., Kirkpatrick, J. D., et al. 2015, ApJ, 804, 92, doi: 10.1088/0004-637X/804/2/92
  * Scholz et al. (2009) Scholz, A., Geers, V., Jayawardhana, R., et al. 2009, ApJ, 702, 805, doi: 10.1088/0004-637X/702/1/805
  * Scholz et al. (2012a) Scholz, A., Jayawardhana, R., Muzic, K., et al. 2012a, ApJ, 756, 24, doi: 10.1088/0004-637X/756/1/24
  * Scholz et al. (2012b) Scholz, A., Muzic, K., Geers, V., et al. 2012b, ApJ, 744, 6, doi: 10.1088/0004-637X/744/1/6
  * Smit et al. (2014) Smit, R., Bouwens, R. J., Labbé, I., et al. 2014, ApJ, 784, 58, doi: 10.1088/0004-637X/784/1/58
  * Sorba & Sawicki (2018) Sorba, R., & Sawicki, M. 2018, MNRAS, 476, 1532, doi: 10.1093/mnras/sty186
  * Stanghellini et al. (2002) Stanghellini, L., Shaw, R. A., Mutchler, M., et al. 2002, ApJ, 575, 178, doi: 10.1086/341146
  * Stark et al. (2013) Stark, D. P., Schenker, M. A., Ellis, R., et al. 2013, ApJ, 763, 129, doi: 10.1088/0004-637X/763/2/129
  * Steinhardt et al. (2020) Steinhardt, C. L., Jauzac, M., Acebron, A., et al. 2020, ApJS, 247, 64, doi: 10.3847/1538-4365/ab75ed
  * Tilvi et al. (2016) Tilvi, V., Pirzkal, N., Malhotra, S., et al. 2016, ApJ, 827, L14, doi: 10.3847/2041-8205/827/1/L14
  * van der Wel et al. (2011) van der Wel, A., Straughn, A. N., Rix, H. W., et al. 2011, ApJ, 742, 111, doi: 10.1088/0004-637X/742/2/111
  * van Elteren et al. (2019) van Elteren, A., Portegies Zwart, S., Pelupessy, I., Cai, M. X., & McMillan, S. L. W. 2019, A&A, 624, A120, doi: 10.1051/0004-6361/201834641
  * Vanzella et al. (2019) Vanzella, E., Calura, F., Meneghetti, M., et al. 2019, MNRAS, 483, 3618, doi: 10.1093/mnras/sty3311
  * Wang et al. (2020) Wang, X., Jones, T. A., Treu, T., et al. 2020, ApJ, 900, 183, doi: 10.3847/1538-4357/abacce
  * Weights et al. (2009) Weights, D. J., Lucas, P. W., Roche, P. F., Pinfield, D. J., & Riddick, F. 2009, MNRAS, 392, 817, doi: 10.1111/j.1365-2966.2008.14096.x
  * Whitaker et al. (2013) Whitaker, K. E., van Dokkum, P. G., Brammer, G., et al. 2013, ApJ, 770, L39, doi: 10.1088/2041-8205/770/2/L39
  * Whitaker et al. (2014) Whitaker, K. E., Franx, M., Leja, J., et al. 2014, ApJ, 795, 104, doi: 10.1088/0004-637X/795/2/104
  * Wilking et al. (2004) Wilking, B. A., Meyer, M. R., Greene, T. P., Mikhail, A., & Carlson, G. 2004, AJ, 127, 1131, doi: 10.1086/381482
  * Williams et al. (2018) Williams, C. C., Curtis-Lake, E., Hainline, K. N., et al. 2018, ApJS, 236, 33, doi: 10.3847/1538-4365/aabcbb
  * Zitrin et al. (2013) Zitrin, A., Meneghetti, M., Umetsu, K., et al. 2013, ApJ, 762, L30, doi: 10.1088/2041-8205/762/2/L30