# CLASSY II: A technical Overview of the COS Legacy Archive Spectroscopic
SurveY111 Based on observations made with the NASA/ESA Hubble Space Telescope,
obtained from the Data Archive at the Space Telescope Science Institute, which
is operated by the Association of Universities for Research in Astronomy,
Inc., under NASA contract NAS 5-26555.

Bethan L. James ESA for AURA
Space Telescope Science Institute
3700 San Martin Drive, Baltimore, MD 21218, USA  Danielle A. Berg Department
of Astronomy
The University of Texas at Austin
2515 Speedway, Stop C1400, Austin, TX 78712, USA  Teagan King Space Telescope
Science Institute
3700 San Martin Drive, Baltimore, MD 21218, USA  David J. Sahnow Space
Telescope Science Institute
3700 San Martin Drive, Baltimore, MD 21218, USA  Matilde Mingozzi Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
John Chisholm Department of Astronomy, The University of Texas at Austin, 2515
Speedway, Stop C1400, Austin, TX 78712, USA  Timothy Heckman Center for
Astrophysical Sciences, Department of Physics & Astronomy, Johns Hopkins
University, Baltimore, MD 21218, USA  Crystal L. Martin Department of Physics,
University of California, Santa Barbara, Santa Barbara, CA 93106, USA  Dan P.
Stark Steward Observatory, The University of Arizona, 933 N Cherry Ave,
Tucson, AZ, 85721, USA  Alessandra Aloisi Space Telescope Science Institute,
3700 San Martin Drive, Baltimore, MD 21218, USA  Ricardo O. Amorín Instituto
de Investigación Multidisciplinar en Ciencia y Tecnología, Universidad de La
Serena, Raul Bitrán 1305, La Serena 2204000, Chile  Departamento de
Astronomía, Universidad de La Serena, Av. Juan Cisternas 1200 Norte, La Serena
1720236, Chile  Karla Z. Arellano-Córdova Department of Astronomy, The
University of Texas at Austin, 2515 Speedway, Stop C1400, Austin, TX 78712,
USA  Matthew Bayliss Department of Physics, University of Cincinnati,
Cincinnati, OH 45221, USA  Rongmon Bordoloi Department of Physics, North
Carolina State University, 421 Riddick Hall, Raleigh, NC 27695-8202, USA
Jarle Brinchmann Instituto de Astrofí]sica e Ciências do Espaço, Universidade
do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal  Stéphane Charlot
Sorbonne Université, UPMC-CNRS, UMR7095, Institut d’Astrophysique de Paris,
F-75014, Paris, France  Zuyi Chen Steward Observatory, The University of
Arizona, 933 N Cherry Ave, Tucson, AZ, 85721, USA  Jacopo Chevallard Sorbonne
Université, UPMC-CNRS, UMR7095, Institut d’Astrophysique de Paris, F-75014,
Paris, France  Ilyse Clark Department of Astronomy, The University of Texas at
Austin, 2515 Speedway, Stop C1400, Austin, TX 78712, USA  Dawn K. Erb Center
for Gravitation, Cosmology and Astrophysics, Department of Physics, University
of Wisconsin Milwaukee, 3135 N Maryland Ave., Milwaukee, WI 53211, USA  Anna
Feltre INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna,
Via P. Gobetti 93/3, 40129 Bologna, Italy  Matthew Hayes Stockholm University,
Department of Astronomy and Oskar Klein Centre for Cosmoparticle Physics,
AlbaNova University Centre, SE-10691, Stockholm, Sweden  Alaina Henry Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Svea Hernandez Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Anne Jaskot Department of Astronomy, Williams
College, USA  Lisa J. Kewley Research School of Astronomy and Astrophysics,
Australian National University, Cotter Road, Weston Creek, ACT 2611,
Australia; ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions
(ASTRO 3D), Canberra, ACT 2611, Australia  Nimisha Kumari Space Telescope
Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Claus
Leitherer Space Telescope Science Institute, 3700 San Martin Drive, Baltimore,
MD 21218, USA  Mario Llerena Instituto de Investigación Multidisciplinar en
Ciencia y Tecnología, Universidad de La Serena, Raul Bitrán 1305, La Serena
2204000, Chile  Michael Maseda Leiden Observatory, Leiden University, PO Box
9513, 2300 RA, Leiden, The Netherlands  Themiya Nanayakkara Swinburne
University of Technology, Melbourne, Victoria, AU  Masami Ouchi Institute for
Cosmic Ray Research, The University of Tokyo, Kashiwa-no-ha, Kashiwa 277-8582,
Japan  Adele Plat Steward Observatory, The University of Arizona, 933 N Cherry
Ave, Tucson, AZ, 85721, USA  Richard W. Pogge Department of Astronomy, The
Ohio State University, 140 W 18th Avenue, Columbus, OH 43210, USA  Center for
Cosmology & AstroParticle Physics, The Ohio State University, 191 W Woodruff
Avenue, Columbus, OH 43210  Swara Ravindranath Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Jane R. Rigby
Observational Cosmology Lab, Code 665, NASA Goddard Space Flight Center, 8800
Greenbelt Rd, Greenbelt, MD 20771, USA  Claudia Scarlata Minnesota Institute
for Astrophysics, University of Minnesota, 116 Church Street SE, Minneapolis,
MN 55455, USA  Peter Senchyna Carnegie Observatories, 813 Santa Barbara
Street, Pasadena, CA 91101, USA  Evan D. Skillman Minnesota Institute for
Astrophysics, University of Minnesota, 116 Church Street SE, Minneapolis, MN
55455, USA  Charles C. Steidel Cahill Center for Astronomy and Astrophysics,
California Institute of Technology, MC249-17, Pasadena, CA 91125, USA  Allison
L. Strom The Observatories of the Carnegie Institution for Science, 813 Santa
Barbara Street, Pasadena, CA 91101, USA  Yuma Sugahara National Astronomical
Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Waseda
Research Institute for Science and Engineering, Faculty of Science and
Engineering, Waseda University, 3-4-1, Okubo, Shinjuku, Tokyo 169-8555, Japan
Stephen M. Wilkins Astronomy Centre, University of Sussex, Falmer, Brighton
BN1 9QH, UK  Aida Wofford Instituto de Astronomía, Universidad Nacional
Autónoma de México, Unidad Académica en Ensenada, Km 103 Carr. Tijuana-
Ensenada, Ensenada 22860, México  Xinfeng Xu Center for Astrophysical
Sciences, Department of Physics & Astronomy, Johns Hopkins University,
Baltimore, MD 21218, USA Bethan L. James<EMAIL_ADDRESS>
###### Abstract

The COS Legacy Archive Spectroscopic SurveY (CLASSY) is designed to provide
the community with a spectral atlas of 45 nearby star-forming galaxies which
were chosen to cover similar properties as those seen at high-$z$ ($z>6$). The
prime high level science product of CLASSY is accurately coadded UV spectra,
ranging from $\sim$1000–2000 Å, derived from a combination of archival and new
data obtained with HST’s Cosmic Origins Spectrograph (COS). This paper details
the multi-stage technical processes of creating this prime data product, and
the methodologies involved in extracting, reducing, aligning, and coadding
far-ultraviolet (FUV) and near-ultraviolet (NUV) spectra. We provide
guidelines on how to successfully utilize COS observations of extended
sources, despite COS being optimized for point sources, and best-practice
recommendations for the coaddition of UV spectra in general. Moreover, we
discuss the effects of our reduction and coaddition techniques in the
scientific application of the CLASSY data. In particular, we find that
accurately accounting for flux calibration offsets can affect the derived
properties of the stellar populations, while customized extractions of NUV
spectra for extended sources are essential for correctly diagnosing the
metallicity of galaxies via C iii] nebular emission. Despite changes in
spectral resolution of up to $\sim$25% between individual datasets (due to
changes in the COS line spread function), no adverse affects were observed on
the difference in velocity width and outflow velocities of isolated absorption
lines when measured in the final combined data products, owing in-part to our
signal-to-noise regime of S/N$<20$.

Dwarf galaxies (416), Ultraviolet astronomy (1736), Galaxy chemical evolution
(580), Galaxy spectroscopy (2171), High-redshift galaxies (734), Emission line
galaxies (459)

††journal: AASJournal ApJ††facilities: HST (COS)††software:  astropy (The
Astropy Collaboration 2013, 2018) CalCOS (STScI), dustmaps (Green 2018),
jupyter (Kluyver 2016), Photutils (Bradley 2021), PYNEB (Luridiana 2012;
2015), python, pysynphot (STScI Development Team)

##  1 Introduction

The COS Legacy Archive Spectroscopic SurveY (CLASSY) is a Hubble Space
Telescope (HST) treasury program with the Cosmic Origins Spectrograph (COS)
dedicated to creating the first high-resolution FUV spectral catalog of star-
forming galaxies at $z\sim 0$, with a sample of 45 targets selected to span
properties seen at high-$z$ ($z>6$) i.e., low-metallicity, low-mass, high star
formation rate (SFR), and high ionization parameter ($U$-parameter) systems
(Berg et al. 2022, submitted, berg22 hereafter). The spectra detect a suite of
emission and absorption lines over 1200–2000 Å that fully characterise the
stellar populations, outflows (in absorption), and nebular conditions present
in the bright star-forming knots that are within the 2$\farcs$5 diameter COS
aperture. Using a total of 312 orbits of COS FUV and NUV spectroscopy (135
new, 177 archival), CLASSY will be responsible for delivering an atlas of high
level science products to the community. In addition to an atlas of coadded
FUV-to-NUV high signal-to-noise (an average S/N$\gtrsim 5$ at 1500Å with
S/N${}_{1500}\gtrsim 5$ for 40% of the overall sample) spectra for each
target, covering the G130M+G160M+G185M/G225M gratings of HST/COS, CLASSY is
designed to provide the properties of the stellar continuum, absorption line
profiles, and emission line profiles, for each spectrum. Moreover, the survey
will release all scientific measurements and the properties derived from them
to the community, including stellar ages, outflow velocities, chemical and
physical properties of the neutral and ionized gas, along with UV-based
emission line diagnostics. Each of these products will be detailed in
forthcoming publications from the CLASSY collaboration.

The main scientific aim of CLASSY is to provide the community with the
necessary toolkit to navigate the upcoming JWST era, when spectral properties
of galaxies in the distant Universe finally come to light. As such, each of
the targets covered by CLASSY are star-forming galaxies akin to those at
high-z ($z<7$, e.g., steidel14; lefevre15; shapley15; mclure18), including
stellar mass (log(M⋆/)$\sim$6 to 10), gas phase metallicity (Z/Z⊙ $\sim$0.03
to 1.2), star formation rate (log(SFR/yr-1)$\sim-3$ to 2), ionization
(O${}_{32}\sim-0.3$222log([O iii]/[O ii]) to 1.4), and electron density
($10<$$N_{\rm e}$(cm-3)$<1120$). The targets have redshifts between $z$=0.002–
0.182, whose UV sources have Galaxy Evolution Explorer (GALEX) FUV magnitudes
between $m_{FUV}=15.3-19.2$, ranging from 0$\farcs$11–1$\farcs$6 in diameter
(as measured from the COS NUV acquisition images).

The Cosmic Origins Spectrograph (COS; green12) onboard the Hubble Space
Telescope (HST) is the ideal (and only) instrument capable of providing the
high-S/N UV spectra required for CLASSY. Installed during Servicing Mission 4
in May 2009, COS delivers high-sensitivity, medium- and low-resolution
spectroscopy in the $\sim 815-3200$ Å wavelength range. COS is comprised of
two separate detectors: a cross delay line (XDL) detector sensitive to FUV
wavelengths (815–2050Å) and a multi-anode microchannel array (MAMA) optimized
for the NUV wavelengths (1700–3200Å). Both detectors offer the use of low-
dispersion gratings (e.g., G140L for the FUV or G230L for the NUV) or medium-
dispersion gratings (e.g., G130M and G160M in the FUV or G185M and G225M in
the NUV). The instrument was designed to study the origins of large scale
structure in the Universe, the formation and evolution of galaxies, the origin
of stellar and planetary systems, and the cold interstellar medium
(ISM).333https://www.stsci.edu/hst/instrumentation/cos. One drawback of COS,
in relation to the needs of CLASSY, is that it is an instrument optimized for
point-source spectroscopy. For example, (1) the standard reduction pipeline
adopts an optimized extraction routine that assumes a point-source profile,
and (2) the light entering the COS aperture is vignetted beyond the central
0$\farcs$4 radius - both of which affect spectroscopy of extended sources
(defined in the COS Instrument Handbook, IHB444https://hst-
docs.stsci.edu/cosihb, COSIHB, as targets with light profiles whose
GFWHM555Gaussian full width at half maximum$>$0$\farcs$6). Moreover, COS is
typically only used for single-grating science and while its data reduction
pipeline produces accurate data summed over a single ‘visit’, it does not
coadd data between different visits, central wavelength settings of the
grating (CENWAVEs), or gratings. Due to the non-point-source nature of
$\sim$60% of the CLASSY targets, and in order to produce the highest quality
high level science products (HLSPs) spanning the full FUV-NUV wavelength
range, the CLASSY data went through significant levels of ad-hoc data
reduction and analysis.

As such, this paper is dedicated to describing the processes involved in
producing the CLASSY coadded spectra and is formatted as follows: Section 2
describes the initial reduction techniques including spectral extraction in
the FUV (Section 2.1) and NUV (Section 2.2). The details of the spectral
coaddition are given in Section 3, including wavelength calibration, spectral
resolution, flux calibration, coaddition techniques, and the effects of
vignetting and aberrations. Finally, in Section 4, we demonstrate and discuss
the effects of the reduction and coaddition processes on the scientific
results derived from our spectra. A detailed description of the CLASSY survey,
including coverage of the sample selection, observations, and sample
properties, is provided in berg22. Any use of the CLASSY spectral atlas should
cite this work (james21) and berg22. Here we provide an in-depth overview of
the data reduction strategies utilized in CLASSY and consequently, a technical
guide to UV-spectroscopic science in general.

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Figure 1: CLASSY acquisition images with compact-source light profiles. For
each galaxy, the NUV target acquisition image from the G130M dataset in shown
(left panels), overlaid with the COS 2$\farcs$5 aperture (gold circle) and
labelled according to its MIRRORA or MIRRORB target acquisition configuration.
The solid-blue and dashed-red lines correspond to the cross-dispersion (PA)
and dispersion axis of the observations, respectively. The collapsed light
profiles along the dispersion and cross-dispersion axes are show as spectral
and spatial traces, respectively (right panels). The Gaussian FWHM fit to the
spectral and spatial traces is given in the legend in comparison to a profile
with FWHM$=$0$\farcs$6 (gold) which we use for classification purposes. For
objects that have been acquired using MIRRORB, the secondary image formed
along the spectral axis is marked by a grey opaque box in both panels.

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Figure 1: *

Figure 1 - continued.

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Figure 2: Same as Figure 1, here showing targets with extended
(GFWHM$>$0$\farcs$6) light profiles.

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Figure 2: *

Figure 2 - continued.

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Figure 3: Same as Figure 1, here showing targets with multi-component profiles
(defined as having additional NUV light components beyond 0$\farcs$4 from the
center).

##  2 Data Reduction

Observation details of all the datasets included within CLASSY can be found in
berg22, including dataset IDs, gratings, CENWAVEs, position angles, exposure
times and rest wavelength coverage. All targets covered by CLASSY are
presented in Figures 1–3, which also show the placement of the COS aperture
and the position angle used.

Upon retrieval from the archive, all raw data were reduced locally using the
COS data-reduction package CalCOS
v.3.3.11666https://github.com/spacetelescope/calcos/releases. This includes
both new data attributable to the CLASSY survey itself (PID: 15840, PI: Berg)
and all archival data included in CLASSY, as detailed in berg22. It is the
combination of both data from PID:15840 and archival data that we define as
CLASSY data hereon and it should be noted here that all CLASSY datasets were
processed in a self-consistent way, in that they were all reduced via the most
recent version of the CalCOS pipeline. With regards to the homogeneity of the
data reduction procedures employed, it may also be useful to note that CLASSY
contains data observed at different Lifetime Positions on the COS FUV
detector. As detailed in table 2 of berg22, 15 galaxies were observed at LP
1/2 for G130M or G160M, 28 galaxies occur at LPs 3/4 for G130M and G160M and 2
galaxies contain coadded data with a mix of LPs 1/2 or 3/4. All new CLASSY
data was executed before the commissioning of LP5.

The CalCOS pipeline consists of three main components that calibrate COS data
by (1) correcting for instrumental effects such as thermal drifts, geometric
distortion corrections, Doppler corrections, and pixel-to-pixel variations in
sensitivity, (2) generating an exposure-specific wavelength-calibrated scale,
and (3) extracting and producing a final (one-dimensional) flux-calibrated
(summed) spectrum for the entire observation. While CalCOS typically produces
‘ready to go’ final spectra, it was necessary to re-reduce a large portion of
our datasets due to the extended nature of $\sim$60% of the CLASSY targets.

HST/COS is a spectrograph optimized for UV spectroscopy of point sources. This
is evident in several aspects of the COS pipeline, most notably in the TWOZONE
extraction technique, which utilizes a point-source profile for the extraction
of the data on the FUV detector (815–2050Å). The NUV detector (1700–3200Å)
instead uses a fixed extraction box, which we describe in detail below.777The
TWOZONE extraction is implemented on the FUV detector only because it was
designed to reliably extract spectra close to regions suffering from gain sag
(which primarily result from bright Ly$\alpha$ airglow emission). This
extraction method is only implemented in the CalCOS pipeline for data at
Lifetime Positions 3-5. The NUV detector does not suffer from gain sag and
does not require optimized extraction. The full details on the extraction
methodologies employed by CalCOS can be found in the COS data handbook
(DHB)888https://hst-docs.stsci.edu/cosdhb, COSDHB. Owing to the extended
nature of $\sim$60% of our targets, where non-negligible amounts of flux can
extend beyond the extraction profiles used in CalCOS, it was necessary to test
different extraction techniques with the aim of increasing the signal-to-noise
(S/N) of the CLASSY spectra of extended sources. In order to determine whether
a source was classified as compact, extended and/or had multiple component
(MC) light profiles, we inspected each of the COS NUV target acquisition
images by collapsing the light profiles along the cross-dispersion and
dispersion direction, and subsequently fitting the spatial and spectral traces
with single Gaussian profiles. The width of these profiles, which will be
broadened in the cases of extended and/or MC light profiles, can help
determine whether a source is considered to be a non-compact source. It is
important to note that determining whether a target is extended or not also
has implications on the spectral resolution of the spectra, in that extended
sources will have a decreased spectral resolution compared to that of a point
source (see Section 3.2). As such, for data quality purposes, we take both
profiles into account when classifying the morphological nature of the CLASSY
targets.

Targets whose spatial or spectral profile had GFWHM $>$ 0$\farcs$6 (i.e., when
the distribution of incoming light on the COS/FUV detector is significantly
wider than that of a point source) were classified as ‘extended sources’.
Targets whose spatial or spectral profile had GFWHM $<$ 0$\farcs$6 but showed
more than one peak in their profile were classified as being MC sources.
Targets whose spatial or spectral profile had GFWHM $<$ 0$\farcs$6, with
single peaked profiles, are classified as the CLASSY ‘compact sources’. This
classification scheme was further verified by inspecting the half-light radii
(r50) measured from the COS acquisition images (provided in Xu et al, 2022,
in-prep). All galaxies that were classified as ‘compact’ have
r${}_{50}<$0$\farcs$3, ‘extended’ have r${}_{50}>$0$\farcs$6 and ‘MC’ sources
typically have 0$\farcs$3$<$r${}_{50}<$0$\farcs$6\. However, it should be
noted that these surface brightness profiles can be underestimated for
extended sources in that the COS aperture (1) does not cover the entire galaxy
and (2) is affected by flux vignetting beyond a radius of 0$\farcs$4\. As such
r50 profiles measured from COS acquisitions images provide limited guidance
for this purpose. In support of this, we additionally consulted the r50 values
presented in berg22, which were measured from optical images. The optical r50
values are indeed much larger than those measured from COS acquisition images
because they encompass the entire galaxy, are affected by ground-based seeing,
and trace the more extended optical emission rather than the highly-ionized UV
emission which is typically highly concentrated around the central ionizing
source. Thus being, without in-hand high spatial resolution, non-vignetted UV
images of each source, we feel that the spatial and spectral light profiles
from the COS acquisition images provide the best guidance on source
classification currently available to our study.

As such, we proceed with the classification of CLASSY galaxies into ‘compact’,
‘extended’, and ‘multi-component’ based on the GFWHM cut described above. The
target-acquisition images for all CLASSY targets are shown in Figures 1– 3,
along with the spectral and spatial traces. The targets with compact light
profiles are shown in Figure 1, those with extended single-component light
profiles are in Figure 2, and those with MC light profiles in Figure 3. Within
these figures, we additionally display the collapsed light profiles along the
dispersion and cross-dispersion axes. However, it should be noted that due to
the slitless design of COS, all light within the COS aperture will contribute
to the two-dimensional spectra, not just the light indicated by the spatial
trace. It is also important to note that all aberration and vingetting effects
can be seen within the acquisition images simultaneously. For example, light
can be seen beyond the 1$\farcs$25 radius aperture because light at the
aperture plane has not been corrected for spherical aberration, which takes
place at spectroscopic gratings. To this end, light beyond the 2$\farcs$5
diameter can reach the COS detector.

The majority of COS target acquisitions are taken with the MIRRORA
configuration (this applies to 87 of our datasets). However, if a target is
particularly bright, an attenuated light configuration is utilized, called
MIRRORB, which serves to protect the detector. MIRRORB, was employed for ten
galaxies in CLASSY (each one labelled accordingly in Figures 1–3, that are
especially bright in the UV. Unfortunately, MIRRORB has the drawback of
producing a double peaked image that impedes our interpretation of the spatial
distribution of light. This can be seen most clearly in some of our compact
targets acquired with MIRRORB (e.g., J0036-3333 and J0808+3948, Figure 1),
where the fainter secondary image (marked with a gray box) can be seen in the
direction of the spatial trace. For these specific cases extra care was taken
when assessing the distribution of light and classifying the galaxy as
extended, MC, or compact, and additional pre-existing optical imaging was
inspected (when available).

[t]0.8  [t]0.8

Figure 4: Cross-dispersion light profiles for a compact (e.g., J0127-0619,
top-panel) and an extended source (e.g., J0934+5514, bottom panel), as seen on
Segment A of the COS FUV detector, here for G130M/1291 observational setup.
The light profile collapsed along the x-direction is shown in the right
panels. In each panel we show the extraction profiles used. In the top panel,
which concerns a TWOZONE extraction, we show the inner and outer regions in
red and blue, respectively (see Section 2.1 for details). On the bottom panel
we show a BOXCAR extraction profile, where only an outer region is utilized. A
BOXCAR extraction was the default setting for this specific observation
because it was executed at Lifetime Position 1. However, due to the extensive
width of the J0934+5514 light profile on the detector, a BOXCAR extraction may
always be required to retrieve the full extent of the flux.

[t]0.8

[t]0.8

Figure 5: Signal-to-noise ratio (per 6 pixel resolution element) as a function
of observed wavelength for the TWOZONE and BOXCAR extraction techniques (see
text for details) for single visit G160M spectra of two example targets,
J1119+5130 and J1105+4444. Despite these sources being classed as ‘extended’
in their target acquisition light profiles, only a very minor increase in S/N
is seen from the BOXCAR extractions. This may be due to the fact that despite
the extended profiles, the vast majority of light is still contained within
the central 0$\farcs$8 diameter. These specific targets and gratings were
chosen because they showed the largest change in S/N (shown in the bottom
panel, $\Delta SNR=S/N_{BOXCAR}-S/N_{TWOZONE}$) of all the extended sources.
Dashed lines display $\Delta$SNR=0, solid black lines display the mean
$\Delta$SNR values of 0.05 and 0.08 for J1119+5130 and J1105+4444,
respectively.

###  2.1 FUV Extraction

As discussed above, the observations of extended sources by COS can require a
customized extraction. To illustrate the difference between the cross-
dispersion profile of a compact and an extended source, in Figure 4 we show
G130M/1291/FUVA profiles of J0127-0619 (GFWHM$\sim$0$\farcs$1) and J0934+5514
(GFWHM$\sim$1$\farcs$5). Due to the width of the cross-dispersion profile of
J0934+5514, compared to that of a compact (e.g., J0127-0619), an extended
extraction box may be necessary to collect the full extent of its flux. As
part of this customized extraction, as suggested by the COS DHB, the extended
source CLASSY datasets were re-processed using a BOXCAR extraction technique.
This involves a box of fixed height ($\sim 27-45$ pixels depending on the
CENWAVE), centered on the spectral profile defined within the extraction tab
reference file (XTRACTAB), thus covering the entire cross-dispersion profile,
including any low-level emission that may exist in the wings of the profile -
simultaneously increasing the signal and potentially the noise. By comparison,
the TWOZONE extraction method involves two extraction regions - an inner zone
corresponding to the core of the profile, and an outer zone that covers the
entire region used for the spectral extraction (i.e., outer+inner zone). The
boundaries for these zones refer to the fraction of enclosed energy within the
cross-dispersion profile of a point source (80% and 99% for the inner and
outer zones, respectively) and are a function of wavelength. The separation of
the profile into an outer and inner zone enables the COS pipeline to only
reject wavelength bins if bad pixels occur within the core region, rather than
by those that lie in the outer wings of a profile. By comparison, in a BOXCAR
extraction, any bad pixels that lie within the box will result in rejected
wavelength bins, which can lead to a loss of S/N or gaps in the data if the
BOXCAR extraction region extends into regions of gain sag on the detector
(where the local sensitivity of the detector is decreased).

To establish whether the BOXCAR extractions were an improvement over the
default TWOZONE extractions adopted for spectra taken at LPs 3+4, we compared
the signal-to-noise ratios (S/N) of the binned spectra for each FUV grating
and CENWAVE. Only very minor increases ($\lesssim$ 5%) were found in the
BOXCAR extractions, which we show examples of in Figure 5 for single visit
spectra (i.e., before coaddition occurs). In some cases, no improvement in S/N
was found when employing the BOXCAR technique, which may be due to only low
levels of light falling beyond the central 0$\farcs$4 radius beyond which the
flux is vignetted (discussed in detail in Section 3.5), or due to the
naturally large width of the light profile in the cross-dispersion direction
for the specific CENWAVE (e.g., G130M/1222) which is fully encompassed by both
the TWOZONE and BOXCAR extractions. On the other hand, the effects of an
increased number of bad pixels lying within the BOXCAR extraction region
compared to the TWOZONE were noticeable, which can be seen as negative spikes
of $\Delta$S/N in the lower panels of Figure 5.

Checks were also performed on the equivalent widths (EW) of the emission lines
found within this regime, to assess whether the narrow TWOZONE extraction
profiles were excluding any extended nebular emission that would have been
extracted via a BOXCAR extraction, and thus consequently underestimating the
EW measurements of those emission lines. Measurements were made on the O iii]
$\lambda\lambda$1660,66 emission line doublet with both extraction
methodologies applied to the G160M datasets. In all cases, the EW(O iii])
remained unchanged within the uncertainties of the measurement. While this
test could have also been performed with He ii $\lambda$1640 and Ly$\alpha$ we
note that EW measurement uncertainties on these lines will be impacted by the
spectral continuum fit and subtraction to the extracted spectra, thus
preventing a true comparison of the two extraction techniques.

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Figure 6: Collapsed counts from the flat-fielded science images along the
dispersion direction on the NUV detector for J0934+5514 (IZw18), in YFULL
coordinates (Y pixels corrected for thermal and geometric distortion, walk,
and offset in the cross-dispersion direction, based on the wavecal spectrum).
Overlaid on each plot are the extraction boxes for the wavelength calibration
spectrum (green), the background (blue) and the science spectrum (grey). In
the left-hand panel we show the standard extraction boxes, as defined in the
xtractab reference file. In the right-hand panel we show the updated
extraction boxes, where the background extraction boxes have been moved away
from the source spectrum, and the science spectrum extraction regions have
been widened to account for the extended light profile in the cross-dispersion
direction. Credit: HST/COS Helpdesk Team

Similarly, a too-narrow extraction aperture may miss the full extent of the
continuum flux in extended sources, consequently leading to flux calibration
issues. Indeed, several extended source datasets required minor scaling
factors to align the continuum flux between gratings, which we describe fully
in Section 3.3. Here we note that while a BOXCAR extraction may have helped
minimize minor flux calibration offsets between gratings, since the vast
majority of FUV science utilizes continuum-normalized spectra, we felt that
contiguous and optimized S/N data was of more benefit to the user. As such, we
decided to remain with the TWOZONE extraction for all the FUV extended source
datasets for which it is available (i.e., those that were taken at at Lifetime
Positions 3 and 4).

From our findings, we recommend that COS users targeting extended sources
(GFWHM $>$ 0$\farcs$6) can remain with the default TWOZONE extraction method
without impacting the S/N of their extracted spectra. However, this can have
consequences on the flux calibration of the spectra, which we discuss further
in Section 3.3.

###  2.2 NUV Extraction

For COS NUV data (i.e., data taken with the G185M and G225M gratings), CalCOS
employs a BOXCAR extraction by default (57 pixels in height). However, again
due to the extended nature of our sources, in several cases the spectral
profiles were not sufficiently covered by the size of the extraction boxes. As
such, it was necessary to re-process our NUV datasets with ad-hoc extraction
profiles, centered on the source. To do this, we inspected the collapsed light
profile on each of the NUV detector stripes (A, B, C) and adjusted the default
height of the extraction boxes in the XTRACTAB reference file. Extraction
boxes 95 pixels in height were chosen such that they sufficiently covered the
width of the profile, without extending into the light profile of the adjacent
stripe. We show an example plot used for this assessment in Figure 6, where
the collapsed counts in the dispersion direction (i.e., X pixels) are shown as
a function of Y pixels on the NUV detector. We also denote the extraction
regions for stripes A, B, and C (grey boxes, where A is the lowest on the
detector and C the highest), the three respective wavelength calibration
regions (green boxes) and the background extraction regions (blue boxes). Upon
inspection of these plots, it was found that only targets classified as
extended sources (Figure 2) required larger extraction boxes. Once the
datasets had been re-reduced with updated extraction heights, the final summed
flux were found to increase by factors of up to two times the original flux
for the extended sources. As such, these datasets were now in better agreement
with the FUV flux levels seen in the corresponding G130M and G160M datasets
for the same targets (as shown in Fig. 7).

In order to ensure that the default extraction boxes were not excluding
potentially extended nebular emission in our sources, we additionally measured
the equivalent width of C iii] $\lambda\lambda$1907, 09 with the default and
widened extraction boxes in all of our sources. A widened extraction box was
deemed suitable if the change in EW(C iii]) was larger than the uncertainty on
the measurements. In addition to the extended sources identified previously
identified, we additionally found one compact source (J1024+0524) whose EW(C
iii]) increased significantly with the widened extraction due to extended
nebular emission. We discuss the scientific implications of this change in
EW(C iii]) in Section 4.2.

When extending the extraction boxes, care was also taken to avoid
contamination from spectra extracted from a neighbouring stripe. The highest
level of contamination would be between stripe A and B, which are closest in
location on the detector. The level of contaminating flux was estimated to be
$<1$% for all cases, with the exception of J0934+5514 which reached a
contamination level of $\lesssim 3$%. This is typically well within the level
of uncertainties on the emission line fluxes measured within this wavelength
regime and as such poses only a negligible effect on the scientific results.

Figure 7: Increasing the NUV extraction box height for extended sources (such
as J0934+5514 shown here) enabled a better match in the flux calibration
between the FUV and NUV datasets. Here we show the coadded data for J0934+5514
where the NUV data had been reduced with the default (grey spectrum) and
extended (purple spectrum) extraction boxes. The difference in flux ($\Delta
F_{\lambda}=F_{updated}-F_{old}$) is shown in the bottom panel, with a dashed
line displaying $\Delta F_{\lambda}$=0.

It should also be noted that it was necessary to re-locate the background
extraction boxes for all of our NUV datasets. During our re-extraction process
for the NUV datasets, the default back-to-back positioning of background
extraction regions and the source extraction regions (as seen in Figure 6a)
were found to be problematic for the extended CLASSY sources in that the
resultant background levels were too high due to the contribution of counts
from the extended source profiles. To solve this issue, the COS team assisted
the CLASSY collaboration in re-locating background extraction regions at the
top and bottom of the detector, shown in Figure 6b, which were sufficiently
distanced from the source (a distance of $\sim$+400 pixels and $\sim-50$ away
from the source for the upper and lower background extractions regions,
respectively). While the original locations provided sufficient background
subtraction for compact light profiles, this is not the case for extended
sources and the COS Team hope to release a new NUV XTRACTAB in the near future
with updated background regions for use with both point source and extended
objects. In addition to this, the vignetting issues of NUV data, which affects
the first $\sim$150 pixels of the NUV detector (as detailed in the COS IHB
COSIHB), were automatically removed during the CalCOS reduction process via
data-quality masks located in the bad-pixel table (BPIXTAB).

From our findings we recommend that users of NUV observations of extended
source profiles examine their NUV flat-fielded science images (flt images) and
assess the width of the NUV extraction boxes with respect to the collapsed
light profile, and adjust it to incorporate the entire cross-dispersion
profile. Moreover, to ensure the most accurate NUV background subtraction is
applied, we advise users to use updated background extraction regions, as
detailed above. We discuss the effects of our optimized NUV extractions with
respect to scientific application of the data in Section 4.2.

##  3 Coaddition of Spectra

The final calibrated product of spectroscopic reduction pipelines is
traditionally a single spectrum. In most cases, this product combines the
individual exposures within a set of observations, all of which have similar
observational setup and conditions - i.e., the same filter, wavelength
setting, sky-exposures etc. While coadding spectra of similar observational
setups but different conditions can be relatively straightforward, the
coaddition of spectra taken with different gratings, filters and, for the COS
instrument, lifetime positions999The COS FUV detector is susceptible to gain
sag, a reduction in the ability of the detector to convert incoming photons
into electrons. A strategy used to mitigate the effects of this on COS data is
to occasionally change the location along the cross-dispersion direction where
spectra are recorded on the detector, which is defined as the lifetime
position (LP). on the FUV detector, can be far more problematic due to
differences in resolution, wavelength solution and flux calibration accuracy.
In this way, the HST/COS reduction pipeline, CalCOS, is no different in that
it only produces a final x1dsum spectrum for each grating and CENWAVE,
combined across the separate FP-POS positions101010There are four FP-POS
positions designed to provide an automatated spectral dithering technique
which removes fixed pattern noise in COS observations, as detailed in the COS
IHB, COSIHB for each observational visit.

Several extensions exist within each x1dsum spectrum, including flux,
wavelength, error, upper- and lower-bound error estimates, and information on
counts, background variance, and data-quality flags. It should be noted that
CalCOS delivers spectra with approximate 1-$\sigma$ statistical errors
calculated using numerical approximation of the Gehrels:1986 analytic
functions, where it combines the counts in the re-sampled, linearized pixels
and take the error of that re-sampled sum. As shown in (JohnsonISR:2021),
errors can become asymmetric in the low count regime of $<$20-30 counts/pixel
and assuming symmetry in this regime can lead to severely over-estimated lower
confidence limits. While this count threshold is exceeded in the majority
(65%) of CLASSY datasets, there are instances of G130M and G160M spectra where
the count level proceeds below 20 net counts per native COS pixel ($\sim$84%
and 44% of G160M FUVA and FUVB raw datasets, respectively, and $\sim$25% of
both FUVA and FUVB G130M datasets). Interestingly, despite their somewhat
lower S/N, only $\sim$10% of the G185M/G225M datasets lie in this regime.
Throughout the CLASSY coaddition process we extract and propagate the ERROR
extension of the x1dsum data files (which pertains to the Gehrels upper
confidence limit), and apply propagation techniques that assume a symmetric
error distributions throughout. Within the N¡20 regime, the statistical error
is expected to be accurate to within 5-6% (Gehrels:1986), which we deem
sufficiently accurate for the majority of the scientific application of the
CLASSY data. The decision to assume symmetric errors for all datasets (which
is appropriate for 65% of all CLASSY raw datasets) is based on the need for a
consistent treatment of errors throughout all datasets and the crucial
requirement of propagating symmetric errors during re-sampling. Deriving a
technique that correctly propagates asymmetric errors is beyond the scope of
this study, and currently beyond the capabilities of the CalCOS pipeline due
to its mathematical complexity. While we could adopt a method that
approximates a symmetrical distribution from the separate upper and lower
bound errors reported by CalCOS, the errors would themselves remain
approximate in nature. As such, users whose scientific application of our data
may be affected by our error propagation in these low count regimes are
advised to revisit the original datasets and perform customized error
propagation on those specific datasets. To aid users on this front, we provide
a data flag in the final co-added spectrum (flag = 2) for wavelength regions
where the original x1dsum datasets were below 20 counts per native pixel,
where the statistical error is expected to be over-estimated and correct to
within 5-6%. Finally, it should also be noted that, along with the COS DHB
(COSDHB), we do not recommend using the raw, non-binned spectra in general. As
such, the summed counts are higher per resolution element in the recommended
binned spectra such that the error arrays are appropriate there.

In order to create the final CLASSY spectra spanning the
G130M-G160M-G185M/G225M grating for each galaxy, a careful sequence of
spectral coadditions was used. Each coaddition (1) performed a common
resampling of the wavelengths to the highest dispersion of a given combination
of spectra and (2) used a combined normalized data quality weight (to filter
out or de-weight photons correlated with anomalies) and exposure-time-weighted
calibration curve ( see § 3.4). A detailed discussion of the main steps in the
coaddition process is presented in berg22, however, we provide a brief
overview below. Note that a main goal of the CLASSY templates is to provide
useful spectra to the community for a wide variety of science objectives that
have different data requirements. Therefore, the steps of CLASSY coaddition
process were ordered to allow for templates with different spectral
resolutions to be produced.

  1. 1.

Joining grating segments and stripes: The first step of the coaddition process
unifies the spectral segments/stripes for each grating dataset while
accounting for the wavelength-dependent dispersion. To do so, the python
pysynphot package (lim13) was used to interpolate the flux and error arrays of
each segment onto a new, uniform wavelength grid defined by the largest step
in the original wavelength array. Error arrays were resampled using the
standard error propagation techniques (i.e., in quadrature).

  2. 2.

Coadding single grating datasets: The spectral resolutions of the CLASSY COS
gratings decrease with wavelength such that they are ordered from highest to
lowest resolution as G130M, G160M, G185M, G225M, and G140L. Therefore, to
preserve the highest spectral resolution for a given template, multiple
datasets of a given grating were coadded.

  3. 3.

Coadding multiple grating datasets: Multiple templates with decreasing
spectral resolution were created for each galaxy by progressively including
lower resolution gratings in the coadd process. The resulting templates are
(1) the Very High Resolution (VHR) coadds: consisting of only G130M spectra,
(2) the High Resolution (HR) coadds: consisting of the CLASSY medium-
resolution FUV gratings, or G130M$+$G160M spectra, (3) the Moderate Resolution
(MR) coadds: consisting of the CLASSY FUV$+$NUV medium resolution gratings, or
G130M$+$G160M$+$G185M$+$G225M spectra, and (4) the Low Resolution (LR) coadds:
consisting of the CLASSY medium and low resolution gratings. Possible grating
combinations include G130M$+$G160M$+$G140L and
G130M$+$G160M$+$G185M$+$G225M$+$G140L.

  4. 4.

Binning the spectra: The COS medium-resolution gratings have a resolving power
of $R\sim 15,000$ for a perfect point source, which corresponds to six FUV
detector pixels in the dispersion direction. All coadds were binned by a
factor of six to reflect this using the SpectRes (carnall17) python function,
which efficiently resamples spectra and their associated uncertainties onto an
arbitrary wavelength grid while preserving flux. This function employs
Gaussian error propagation, which can be inappropriate in the very low S/N
regime where errors become asymmetrical (i.e., ¡20 counts per native COS
pixel). As discussed above, this regime occurs in most of our raw (pre-
coaddition) G160M datasets and is flagged accordingly in our ERROR mask array.
The final high-quality CLASSY coadded spectra are shown in the Appendix of
berg22.

The final coadded CLASSY spectral templates are HLSP multi-extension fits
files that include different extensions for observed-frame and rest-frame
wavelength spectra, observed and Galactic reddening-corrected spectra,
original and binned data. We direct the reader to berg22 for a full
description of each of these extensions.

The final CLASSY coaddition process outlined above was designed based on a
series of intermediate analyses to test the impact of different methods on the
reduced spectra. Below we describe the methodologies employed to test or
account for the wavelength calibration (§ 3.1), differing spectral dispersions
(§ 3.2), flux calibration (§ 3.3), coaddition weighting (§ 3.4), and
vignetting and optical aberrations (§ 3.5).

Table 1: Lines used for wavelength calibration.

Line ID | Wavelength (Å) | Type | Grating Coverage | $z^{\dagger}$
---|---|---|---|---
He ii | 1640.42 | Nebular | G160M | $<0.09$
O iii] | 1660.81, 1666.15 | Nebular | G160M | $<0.08$
C iii] | 1906.68, 1908.73 | Nebular | G185M | $<0.12$
N i | 1193.30 | ISM | G130M, G160M | $<0.23,0.12-0.46$
Si iii | 1206.50 | ISM | G130M, G160M |  $<0.20$, $0.12-0.48$
Si ii | 1260.42 | ISM | G130M, G160M |  $<0.15$, $0.06-0.42$
O i∗ | 1302.17 | ISM | G130M, G160M |  $<0.11$, $0.03-0.37$
C ii∗ | 1334.53 | ISM | G130M, G160M |  $<0.09$, $0.01-0.34$
Al ii | 1670.79 | ISM | G160M | $<0.07$
C iii | 1175.53 | Photospheric | G130M, G160M |  $<0.23$, $0.14-0.52$
C iii | 1247.38 | Photospheric | G130M, G160M |  $<0.16$, $0.08-0.44$
Si iii | 1294.54 | Photospheric | G130M, G160M |  $<0.12$, $0.04-0.38$
O iv | 1341.64 | Photospheric | G130M, G160M |  $<0.08$, $0.03-0.33$
S v | 1501.76 | Photospheric | G160M |  $<0.19$

###  3.1 Wavelength Calibration

While the wavelength solution of the medium-resolution grating COS spectra is
expected to be within $\sim$15 km s-1, and monitored regularly throughout the
calendar year, differences between the gratings are not monitored and could
potentially diminish the wavelength accuracy of the final coadded data. This
would be evident, for example, in velocity differences between the NUV and FUV
nebular emission lines, or incorrect outflow velocities from FUV absorption
lines calculated relative to NUV nebular emission lines. In order to check the
wavelength solution alignment between gratings, we inspected a suite of lines
within each spectrum by overlaying them in velocity space before performing
any coaddition. Since ISM absorption lines are typically found to be
blueshifted with respect to the nebular gas (e.g., shapley03; steidel10;
heckman02), it is imperative that velocity comparisons are only made between
the same type of feature (i.e., nebular to nebular). The lines used for the
grating-to-grating wavelength calibration check are listed in Table 3.

Relatively-strong, non-resonant, nebular emission lines are in many of the
CLASSY datasets. These lines enabled robust comparisons between the G160M and
G185M/G225M datasets. For 12 galaxies, no suitable emission lines were present
for a comparison between the G160M and G185M/G225M gratings and, therefore,
the wavelength calibration of the G185M/G225M gratings remains unchecked.
However, the only features of importance in the G185M/G225M gratings are
nebular emission lines, and so the lack of lines/wavelength calibration has no
impact on the scientific analysis of these objects. For comparison between the
G130M and G160M datasets, no pairs of strong, non-resonant emission lines are
present. Instead, isolated, unsaturated ISM absorption lines of similar
ionization states were utilized, as listed in Table 3. A comparison between
the photospheric line profiles was also made between the G130M and G160M
gratings when possible.

Upon inspection, differences of $<15$ km s-1, were found between the gratings
(see e.g., Figure 15 b–d). Since this is within the wavelength accuracy
expected from each grating, it was not necessary to adjust the wavelength
scale of the data before proceeding with the coaddition procedure. However,
for one particular extended target, J1016+3753, an offset of $\sim 0.5$ Å
($\sim$107 km s-1) was observed in the Si iv line profiles between the G130M
and G160M data (Figure 15a). In this case, the two exposures were taken with
very different position angles ($\Delta$PA$\sim 100^{\circ}$) and as such,
this wavelength offset represents a real offset in the velocity of the gas
observed at the different PAs and is not due to wavelength calibration issues.
Since these are physical differences, we do not adjust the coadded data for
wavelengths offsets such as this. As can be seen in the final coadded spectrum
of J1016+3753, despite the velocity offset the combined features in this
overlap regime are not significantly broadened due to the weighting effects of
our coaddition process. However, velocity offsets may be seen when measuring
lines of the same origin (i.e., ISM, nebular, photospheric) in the final
CLASSY coadded data of J1016+3753 if they lie within different gratings.

As a general recommendation for COS data, the wavelength calibration offsets
between gratings should be inspected using the methodology described above (if
possible with respect to line detection). However, wavelength calibration
offsets are likely negligible if the targets are compact sources or are
extended targets where the individual datasets have been taken with the same
position angle and pointing.

0.49  0.49

Figure 8: The COS line spread functions change with position on the COS FUV
detector, which need to be taken into account when combining data that have
been taken at different lifetime positions (LPs). As an example, we show the
modeled COS LSFs for the G130M/1291 CENWAVE at 1300 Å at each of the four LPs
(left-hand panel) and the percentage decrease in the LSF FWHM relative to LP4
(i.e., corresponding to an increase in resolution relative to LP4) as a
function of wavelength (right-hand panel).  Table 2: Properties of the four
final CLASSY coadded spectra provided within the separate extensions of each
CLASSY spectral data product: very high resolution (VHR), high resolution
(HR), medium resolution (MR) and low resolution (LR).

CLASSY coadd ID | Gratings Combined | Dispersion | Dispersion | Observed $\lambda$ coverage
---|---|---|---|---
|  | (mÅ/pixel) | (Å/resel∗) | (Å)
VHR | G130M | 9.97 | 0.060 |  $\sim$1200–1430
HR | G130M+G160M | 12.23 | 0.073 |  $\sim$1200–1750
MR | G130M+G160M+G185M | 33.0 | 0.200 |  $\sim$1200–2000
LR | G130M+G160M+G140L+G185M/G225M | 80.3 | 0.482 |  $\sim$1200–2200

${}^{*}1$ resel $=6$ pixels, as defined by the COS Data Handbook for the FUV
detector (COSDHB).

###  3.2 Accounting for Resolution

Accounting for the change in spectral resolution between individual gratings,
and datasets, is a particularly difficult task, especially since resolution
can depend on several different factors. First, the resolution is dependent on
the extent of the UV light profile and, consequently, the position angle of
the observation if the object is not azimuthally symmetric. The position
angles of all observations included within CLASSY can be found in berg22,
while the position angles of all G130M observations are shown in Figures 1–3).
Secondly, resolution changes as both a function of wavelength across the
detector and as a function of lifetime position (LP) on the COS FUV detector.
Within a single G130M CENWAVE, resolution can change by $\Delta R\sim 5,000$
as a function of wavelength, and at a fixed wavelength can change by a further
$\Delta R\sim 5,000$ between different positions on the detector (i.e., at
different LPs) - resulting in an overall (point source) resolution range of
$R\sim 11,000-20,000$ for G130M/1291 alone within the CLASSY datasets due to
the use of both archival and new data taken at different LPs. G160M is
somewhat similar to G130M, with a (point source) resolution range of $R\sim
13,000-24,000$ across all CENWAVEs. The resolution of NUV datasets are
somewhat different, $R\sim 16,000-20,000$ for G185M and $R\sim 20,000-24,000$
for G225M. CLASSY also utilizes the COS low-resolution gratings in some cases,
G140L, which has a resolution range of $R\sim 1,500-4,000$. However, readers
are reminded that the spectral resolution of CLASSY spectra for targets that
have been classified as having extended and/or MC light profiles will be
significantly reduced compared to targets with compact or point-source light
profiles.

Conserving the resolution to account for optical effects, such as changing
line spread functions (LSFs), within coadded data would require a convolution
kernel to match the wavelength-dependent resolution of one observation to that
of another. In the left-hand panel of Figure 8 we plot the model LSF for
G130M/1291 at 1300 Å at the four different LPs on the COS detector, for a
point source centered in the aperture111111Available at
https://www.stsci.edu/hst/instrumentation/cos/performance/spectral-resolution,
which are generated from two-dimensional, theoretical point spread functions
(discussed in Section 3.5). The percentage change in LSF, relative to LP4
(where $\sim$60% of the CLASSY FUV data is obtained) as a function of
wavelength is plotted in the right-hand panel of Figure 8. The resolution of
LP4 spectra decreases by up to 25% compared to LP1 spectra. Consisting of a
combination of both new and archival data, CLASSY has several targets where
data are coadded across multiple LPs.

However, given the moderate S/N of the CLASSY datasets, and due to the
extended nature of many of our sources (whose broad light profiles would have
a dominant effect on the LSF resolution), changes in LSF resolution between LP
were not accounted for when coadding datasets that were taken across separate
LPs at the same grating and CENWAVE. We explore the scientific effects of
using a non-optimized LSF on absorption line fitting in Section 4.3. It should
be noted, however, that this may not be the case for high S/N observations of
point-source targets and in such cases, the change in resolution should be
taken into account before the coaddition of data across different LPs.

Figure 9:  A demonstration of the flux calibration between the gratings of the
CLASSY spectra of J0942+3547. These flux calibrations were performed to all of
the datasets via small scaling-factors to ensure highly accurate flux
calibrations between gratings. Upper panel: Featureless continuum regions in
the G130M/G160M spectrum (blue/orange shading) were fit with a 1D spline
(blue-dotted/red-dashed line) and used to scale the G130M spectrum to the
G160M spectrum at their mid-way intercept point (vertical blue dashed line).
Lower panel: Similarly, the featureless continuum regions in the G160M/G185M
spectrum (blue/orange shading) were fit with a 1D spline (blue-dotted/red-
dashed line) and used to scale the G185M spectrum to the G160M spectrum at
their mid-way intercept point (vertical blue dashed line). The final coadded
dataset and scaling factor (used to multiply the G130M and G185M continuum
flux) is shown in Figure 12. In each panel we show the flux$\pm 1\sigma$ error
spectrum, binned by 6 pixels. Scaling figures for all galaxies within the
CLASSY sample are provided in Figure Set 17.

The spectral dispersion of the gratings differ considerably, with averages of
9.97 (0.060), 12.23 (0.073), 33.0 (0.200), and 80.3 (0.482) mÅ/pixel (Å/resel)
for G130M, G160M, G185M, and G140L, respectively, where we define one ‘resel’
(or resolution element) as being six pixels121212This is the fiducial FUV
resolution element as defined by the COS Data Handbook. The fiducial NUV
resolution element is instead three pixels, however we choose to adopt a
6-pixel resel here for our NUV datasets in order to provide a uniformly binned
data product to the users. While the actual size of a resolution element
changes as a function of wavelength and grating, we adopt the ‘resel’
nomenclature to mean the fiducial case, in concordance with the COS DHB.. To
take this into account, at each stage of the coadding process, we resample the
spectra to the largest average dispersion available within that combination of
gratings. This results in four different types of coadded data (VHR, HR, MR,
and LR), whose properties we show in Table 2. Each are provided to the
community within separate extensions of the final coadded CLASSY spectra, in
addition to the original, unbinned data. This combination of data products
enables users of CLASSY data to access both full-wavelength spectra of each
object, albeit at lower resolution, in addition to the higher resolution
spectra which may be more appropriate for their scientific goals.

[t]0.6  [t]0.6

Figure 10: J1105+4444, an extended source, where the G130M and G185M data were
offset from the G160M data by PA of $\sim$ 45∘. Figure 11: J1444+4237, a
multi-component source, where the G130M, G160M, and G185M datasets were offset
by $\Delta$(PA)$\sim$118 overall. Figure 12: Differences in observational set-
up between the datasets used for the CLASSY coadded data sometimes required
small scale factors being applied to the data. Here we show three examples of
coadded data before (blue) and after (purple) scaling (top two panels) with
the difference between the scaled versus non-scaled data (lower two panels).
The difference in flux offsets seen in J1105+4444 (a) and J1444+4237 (b)
demonstrates that the observed offset depends on both $\Delta$PA and also the
specific shape of the light distribution of each target. Despite J0942+3547
(c) being a compact source, an offset in flux of $-0.1$– $-0.4\times
10^{-15}ergs^{-1}cm^{-2}$Å-1 is seen, which is within the uncertainty of the
continuum. The top panels show the flux$\pm 1\sigma$ error spectrum, binned by
6 pixels. Scaling figures for all galaxies within the CLASSY sample are
provided in Figure Set 17.

[t]0.6

Figure 12: J0942+3547, a point-source target where the G130M data was offset
in PA from the G160M+G185M data by 207∘. Figure 13: *

Figure 10 - continued.

###  3.3 Flux Calibration

Offsets in flux can sometimes exist when comparing observations of the same
object taken within different programs, either between gratings, between
CENWAVEs of the same grating, or observations of extended objects with
different position angles. The majority of these offsets exist due to
differences in the observational set-up of the object’s target-acquisition
(e.g., PA, pointing etc). For example, a difference in PA between observations
of extended sources can result in altered light profiles in the cross-
dispersion direction and the amount of flux extracted. While larger extraction
regions have the potential to aid us here, we consider the optimization and
conservation of S/N a higher priority than minor offsets in flux calibration,
especially given the predominantly flux-normalized nature of UV-based science.

Moreover, due to the vignetting through the COS aperture (which is detailed in
Section 3.5), slight offsets between COS pointings due to mis-matched
centering on multi-component light profiles, can result in non-negligible
differences (i.e., $>5\%$) in the amount of light reaching the detector. Along
the same lines, if there are failures to re-acquire guide-stars during the
observation’s visit, the target can potentially drift within the aperture,
causing a decrease in flux. Datasets suffering from failures such as these are
flagged accordingly within the HST archive but not within the files themselves
(such datasets were identified and removed from the CLASSY data sample).
Changes in the LP of the observation (i.e., the position on the detector) do
not account for flux offsets, since flux calibration is done self-consistently
via the time dependent sensitivity calibrations performed as part of the
standard COS calibration
program131313https://www.stsci.edu/hst/instrumentation/cos/calibration. Flux
offsets between gratings/CENWAVEs within the 2% relative flux calibration
accuracy achieved by COS are expected whereas the absolute flux calibration
accuracy (i.e., between COS observations and models) is 5% (COS IHB, COSIHB).
However, given the vignetting through the COS aperture (detailed in Section
3.5), the accuracy of the absolute flux calibration ultimately depends on the
particular source morphology.

Differences in flux were observed both within and between gratings for several
of the CLASSY targets, for the specific reasons outlined above, including
offsets within the relative flux calibration accuracy of COS. For scaling
within the same grating, a reference dataset was chosen (according to the most
centered pointing and/or most common PA) and spectra were scaled towards the
longest exposure time dataset within the reference set. Flux offsets within a
grating were found to be minor ($\sim 2$% on average). For scaling between
different gratings, the G160M data was chosen to be the ‘nominal’ dataset as
it represents the middle wavelength range between the three gratings.141414It
should be noted that the choice of the nominal dataset is somewhat arbitrary
for our needs here, mostly because FUV data is continuum normalized before
measuring absorption lines and any line ratios between optical and UV data
will be performed after scaling optical data to match the UV. However, for
scientific use cases where the continuum level needs to be accurately
evaluated, when coadding or comparing data taken at different PAs, the NUV
light profiles should be taken into account and assessed before assigning a
nominal dataset.  Since the FUV data typically has more continuum and a higher
S/N than the NUV data, G130M was first scaled to G160M, then G185M was scaled
towards the G130M+G160M combination. Scale factors were derived via a reduced
$\chi^{2}$ 1D spline fit to regions of featureless high S/N continuum and
comparing the flux offset in the mid-way interception point between the two
fits (i.e., the midpoint of the overlap in wavelength ranges of the spectra),
as demonstrated in Figure 9. The error arrays were not included in the
derivation of the scale factor. In some cases, due to small gaps in wavelength
between G130M and G160M spectra or G160M spectra and the gaps between NUV
stripes, the midway point may occur in a region that pertains to the
extrapolated fits and thereby represents our best estimate of the flux in this
wavelength region. Several fitting functions were tested for this process and
a 1D spline was found to sufficiently represent the shape of the continuum for
each target within the wavelength ranges covered. To accurately determine the
slope of the continua, all spectral features were masked with velocity extents
according to the specific type of feature: $-3500<v$(km s-1)$<+2000$ for
stellar wind lines, $-200<v$(km s-1)$<+200$ for photospheric lines,
$-1000<v$(km s-1)$<+200$ for ISM lines, $-200<v$(km s-1)$<+200$ for MW ISM
lines, $-100<v$(km s-1)$<+100$ for fluorescent lines, and $-300<v$(km
s-1)$<+300$ for nebular lines, and geocoronal emission lines. The fluorescent
lines are typically very narrow and do not need the wider masks used for
strong nebular emission lines.

Figure 14: A demonstration of different coadding techniques explored for the
CLASSY final coadded spectra. Here we show the combined G130M+G160M+G185M data
for J0944-0038 using three different coaddition techniques, consisting of
normalized exposure time and variance weighting, edge flagging, and DQ-
weighting (as described in the legend). The difference in flux relative to the
DQ-weighted coaddition is displayed in the bottom sub-panel, which illustrates
the DQ-weighted coaddtion technique being most beneficial in removing noise in
the overlap regions. Spectra have been corrected for Galactic reddening and
redshifted to restframe wavelengths.

For the majority of targets, a negligible offset in flux was found between the
entire suite of G130M+G160M+G185M/G225M datasets (i.e., within the relative
$<2$% flux accuracy of COS). However, flux offsets in overlapping wavelength
regions between the gratings were found to be $\sim$3–15% in 12 individual
cases, which is outside the relative flux calibration of the COS instrument.
These targets were J0337-0502, J0942+3547, J1132+5722, J1157+3220, J0036-3333,
J0021+0052, J0127-0619, J0944-0038, J1105+4444, J1016+3754, and J1359+5726.
The majority of these targets showed either extended or multi-component light
distributions and changes in position angle between the observational setup
(grating and/or CENWAVE). This resulted in differing cross-dispersion profiles
on the detector, and thus slightly different levels of signal extracted during
the TWOZONE extraction method. When inspecting the BOXCAR versus TWOZONE
extractions for these targets, it could be seen that the BOXCAR flux levels
were in agreement within the wavelength overlap regions. This is because a
circular aperture, with a symmetric vignetting function, collects the same
amount of light for the same pointing, regardless of changes in PA. However,
as described in Section 2.1, the increase in S/N was not sufficient enough to
warrant a BOXCAR extraction for these sources, especially given the increased
number of bad pixels that were included within the BOXCAR extraction box,
which resulted in data gaps.

Observations of extended/multi-component targets at the same PA or compact
sources at different PAs do not result in large flux offsets. In Figure 12 we
show an example of the small flux offset observed between the G130M, G160M,
and G185M gratings for J0942+3547. This target is a compact source where the
G130M data were offset in PA from the G160M+G185M data by 207∘, where roughly
the same level of signal should be expected. The scale factors were found to
be $\sim$3–6% of the G160M continuum flux) and may be attributable to absolute
flux calibration offsets intrinsic to the COS instrument itself.

In some faint targets (e.g., J0944-0038), it should be noted that small
sections of G185M/G225M spectra lie within the unphysical negative flux
regime. Upon inspection of the original, unscaled, spectra, these particular
wavelength regions were already extending below zero despite the improved
(lower) background estimate discussed in Section 2.2. While the (sometimes)
downward scaling of our G185M/G225M will potentially exaggerate the issue, it
should be noted that the original cause is due to background subtraction
issues. The uncertainties are particularly large in these wavelength regimes
and may be asymmetric (and are flagged accordingly). The calculation of scale
factors involve featureless regions of continuum and do not include
uncertainties on the data and thus provide the most accurate flux calibration
available between the gratings.

From our findings we conclude that inspecting the flux alignment between
individual gratings is imperative, especially when working with spectroscopy
of extended or multi-component sources. Moreover, the position angle of
observations of extended or multi-component sources should be kept constant
between observations in order to achieve an accurate flux calibration between
datasets, while also optimizing the S/N of the dataset. We discuss the
scientific impacts of correcting for flux offsets between gratings in Section
4.1.

###  3.4 Coadd Weighting Techniques

After accounting for any issues related to wavelength and flux calibration,
and differences in spectral dispersion owing to different observational setups
(i.e., grating and LP), the individual datasets are considered ready for
coaddition. Coaddition was performed as a weighted mean of the flux and error
arrays (propagated in quadrature), where the weighting of the error arrays was
modified if the weights included the error or variance. Several weighting
techniques exist for coadding data, such as weighting by exposure time, data
quality (DQ) arrays, error arrays, S/N, and various combinations. Other
methodologies, such as weighting by the variance, squared S/N, inverse
variance, and modified exposure time (where flagged pixels are devalued) have
also been explored for COS data (e.g., danforth10).

Each weighting technique was investigated as part of the CLASSY coadded
process by assessing the overall S/N of the final spectra and checking for
spurious features. In particular, regions of increased noise can appear in the
overlap regions of gratings, due to large uncertainties at the edge of a
gratings’s bandpass where the throughput decreases. These edge effects are
flagged within the COS data with both DQ flags (integer values depending on
the quality condition) and DQ_WGT flags (0 to 1 in the individual x1d files to
indicate whether an event should be included in the final x1dsum data and
values of 0 to N in the x1dsum data, where N is the number of spectra used to
create the x1dsum). DQ_WGT arrays are derived from serious data quality flags
(SDQFLAGS), which will set the DQ_WGT values to 0 for photon events that lie
near the edge of the detector, dead spots, hot spots etc.

A comparison of three different coaddition weighting methods is presented in
Figure 14, consisting of a normalized exposure time $+$ variance weight
method, which is then modified to include an edge flagging technique and a DQ-
weighting technique. In the bottom panel of Figure 14 we show the difference
between each technique relative to the DQ-weighting method. Spikes in noise
can be seen throughout, which are most apparent at the spectral overlap
regions between the individual datasets. The CLASSY coadds were, therefore,
constructed using the best resulting method that weights by the (1) normalized
exposure time, (2) normalized DQ_WGT arrays, and (3) variance arrays,
resulting in spectra with maximal S/N and smooth transitions between gratings.
Since the coaddition takes place on x1dsum data, the DQ_WGT arrays were
normalized by the maximum value within each array before being utilized as a
weight.

|
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Figure 15: Spectral overlaps for two extended sources (J1016+3754 and
J1157+3220; top panels) and two sources with multi-component light profiles
(J1225+6109 and J1359+5726; bottom panels) where the G130M and G160M data were
observed at different position angles, and the effects of optical aberrations
and/or different cross-dispersion profiles would be most apparent. Only a
minor difference in the continuum flux is seen for each object (shown as
$\Delta F=F_{\rm G130M}-F_{\rm G160M}$ in the bottom of each panel) and a
$\sim$0.5 Å ($\sim 107$ km s-1) wavelength (velocity) offset is seen in
J1016+3754 due to a change in the dispersion profile on the detector. In the
upper panel of each sub-plot we show the spectra from the original datasets
(purple/cyan) overlaid with the final CLASSY coadded spectrum (black).

###  3.5 Vignetting & Aberrations

As discussed in Section 1, the COS spectrograph was designed to perform
optimally for point-source targets at the nominal (off-axis) location of COS
in the optical telescope assembly (OTA) focal plane, which corresponds to the
first lifetime position of COS (LP1, May 2009 until July 2012). The COS FUV
grating corrects for the OTA spherical aberration while dispersing the light
and corrects for some of the astigmatism inherent in the Rowland Circle design
with a single optical element when a target is at this position. The NUV
channel does a similar correction using multiple optical elements.

During a COS FUV observation, the selected aperture (PSA or BOA) is moved to a
unique offset position for each LP. Because the COS aperture is encountered
before the correction for the OTA’s spherical aberration is made, the aperture
does not function as a traditional field stop with sharp edges, and thus the
effects of the aperture on the light entering the spectrograph are more
complicated than in a typical optical system. Light coming from more than 0.4”
from the center of the aperture will be partially vignetted, and some light
beyond 1.25” from the center (nominally outside of the aperture), reaches the
detector (as demonstrated in the acquisition images shown in Figures 1–3).

Because of this complex vignetting function, the distribution of counts on the
detector for a non-symmetric extended source in an NUV image (taken at LP1)
does not directly reflect the distribution of light on the sky. In addition,
since vignetting effects vary with LP, there is no direct way to map its
effects from one LP to another. This means that the 2D distribution of light
passing through the aperture (and ultimately reaching the detector) will be
slightly different at each LP. A further difference between NUV and FUV is
that the mid-frequency wavelength errors (MFWEs) on the primary and secondary
mirrors of HST cause a broadening of the PSF which is a function of wavelength
(as detailed in the COS IHB, COSIHB). Although the MFWE effects were not
accounted for in the design of the spectrograph, they are included in the
theoretical PSFs provided by the COS team and discussed in Section 3.2. In
summary, NUV images of extended objects can not be used to correct for the
effects seen in spectra.

The instrumental focus is set to maximize the performance for a point-source
target in the center of the aperture for each LP. Light entering the aperture
but not at the center, such as that from an extended object, will necessarily
be out of focus, and these effects will depend on the position angle. In
addition, the vignetting effects will selectively remove light in a
complicated way, further distorting the two-dimensional PSF at the detector.

Since individual spectra cannot be corrected for these effects, caution should
be used when combining datasets taken at different pointings, especially for
extended sources whose light profiles extend beyond the central
$\sim$0$\farcs$4 region of the aperture. For example, the same extended source
observed with one pointing may have different cross-dispersion heights and/or
different dispersion profiles on the detector, leading to changes in spectral
resolution. Additionally, changes in cross-dispersion profile heights can lead
to flux offsets when adopting the default extraction technique. For several of
the extended CLASSY targets where datasets were observed at different PAs,
flux offsets between the spectra were observed due to these effects and had to
be scaled accordingly (as discussed in Section 3.3).

We assessed these effects in the regions of overlapping wavelength coverage
between extended source datasets observed at different PAs. In Figure 15 we
show the Si iv $\lambda\lambda$1393, 1402 line profiles of four example
targets where G130M and G160M data were taken at different PAs: two extended
sources J1016+3754 and J1157+3220, and two multi-component sources, J1225+6109
and J1359+5726.

The offsets in PA resulted in changes in flux by factors of $\sim$1–20% of the
continuum flux for extended sources (due to the extracted flux along the
different cross-dispersion profiles). In J1016+3754 a shift of $\sim$0.5Å
($\sim 107$ km s-1) is observed between the two datasets, most likely due to
the fact that this is an extended object, and the change in aperture position
angle modifies its dispersion profile on the detector (see Section 3.1). As
such, we advise that care should be taken when averaging data for extended
sources when different position angles were utilized for the observations.
Inspecting the data for offsets in wavelength and/or flux is recommended
before coadding datasets of this nature.

When it comes to scientific application of the data, it should be noted that
aberration effects are accounted for with point sources via the LP-dependent
line-spread functions (available on the COS
website151515https://www.stsci.edu/hst/instrumentation/cos/performance/spectral-
resolution) which should be convolved with the model spectra or model line
profile before comparing them with COS spectra (i.e., during line profile
fitting). For extended sources, however, the effects of vignetting and
aberrations in the dispersion direction can affect the shape of the LSF. While
LSF profiles are available for point sources, we did not attempt to create ad-
hoc LSFs for each of the extended sources as a function of wavelength. While
the vignetting affects both the acquisition images and spectrographic light
profiles in the same way (both are taken through the same aperture), since it
varies with LP, one could only use the vignetted image to model the extended-
source LSF, and apply it to the spectral profile on the FUV detector, if the
spectrum was taken at the same LP as the acquisition images (LP1). This
accounts for only 8/177 datasets included within CLASSY.

Additionally, it should be noted that photometric measurements of extended
sources will also be affected by vignetting, since the light near the center
is weighted more heavily than the light at larger radii. Along the same lines,
studies of nebular lines may also be affected, in that emission from ions near
the center of a light profile will be more heavily weighted than ions further
out. As such, care should be taken when comparing photometric and/or nebular
emission line measurements of extended sources obtained from COS
images/spectra against data obtained with non-vignetted apertures. However,
this should only be applicable when a significant amount of the source’s flux
extends beyond the central $\sim$0$\farcs$4 radius of the aperture.

##  4 Results & Discussion

The addition of archival and new observations of 45 nearby star-forming
galaxies has resulted in the first high-resolution and high-S/N spectroscopic
atlas covering the entire COS UV wavelength range. In order to provide the
best possible spectroscopic dataset to the community, the utmost care was
taken to extract, reduce, and combine each target’s suite of observations,
resulting in a catalogue of high-S/N medium-to-high resolution spectra for
each target. The final coadded CLASSY spectroscopic datasets are described in
detail in berg22 and are available for download at
https://mast.stsci.edu/CLASSY. A thorough documentation of the reduction and
coadding procedures used to create this dataset are detailed in the above
section, along with guidelines and best practices for performing these
procedures on COS spectroscopic datasets in general. During these procedures,
enabling the maximum scientific accuracy of our data was of prime importance.
However, as with all spectroscopic observations, there are several aspects of
the data reduction and coaddition procedures that have the potential to affect
the scientific results obtained from the data. Here we use a range of typical
spectroscopic analysis techniques such as continuum fitting, emission line
fitting, and absorption line fitting to understand the extent to which our
data reduction techniques affect the scientific output of the CLASSY data.

Figure 16:  Parameters derived from stellar continuum fitting of CLASSY
coadded spectra with and without scaling between the individual grating
datasets, expressed as a percentage change relative to the parameter derived
before scaling. The top panel demonstrates that the reddening value due to
dust, $E(B-V)$, derived from the stellar continuum fits is quite sensitive to
relative flux calibration. On the other hand, the middle and lower panels show
little change in the luminosity-weighted stellar population metallicity
relative to solar, age, and $Z/Z_{\odot}$, respectively.

###  4.1 Continuum fitting

The majority of UV spectral analyses require robust stellar continuum fits,
either for continuum normalization or for determining the properties of the
stellar population. For the latter application, the properties derived from
continuum fitting with simple population synthesis (SPS) models are directly
dependent on the shape of the continuum. Reversed, the observed UV continuum
shape depends both on the intrinsic stellar continuum slope and the reddening
due to dust. Given that the dust is intrinsic to the system observed, we can
investigate the effect of the relative flux calibration on the derived stellar
population properties.

Figure 17: The effects of relative flux scaling on the NUV nebular emission
lines. The flux measurements for the C iii]$\lambda$1907,1909 emission-line
doublet are plotted for the CLASSY coadded spectra before and after flux
scaling was applied between the FUV and NUV datasets. The bottom panel shows
the difference in C iii] flux (scaled-unscaled) in dex for each target. Figure
18: The effects of relative flux scaling on the NUV nebular emission lines.
The effects of relative flux scaling on the direct-method C/O abundances,
derived using the C iii]$\lambda$1907,1909 to O iii]$\lambda$1666 line ratio,
from the scaled and unscaled coadds. In general, scaling the NUV gratings to
the FUV gratings results in slightly decreased C iii]$\lambda$1907,1909 C/O
abundances. The dashed-blue line represents the 1:1 ratio.

As discussed in Section 3.3, the continuum level of all gratings of an
individual galaxy were scaled to its corresponding G160M grating continuum.
While the scaling factors between the G130M, G160M, and G185M datasets were
usually relatively small (at most a factor of 1.7), if the CLASSY data had not
been aligned correctly there may have been adverse affects on the accuracy of
properties derived from fitting the stellar continuum. To test the effects of
this scaling method, we used a subset of 11 CLASSY galaxies with larger than
average scaling parameters and created two sets of coadded spectra, one with
the optimal scalings and one with no scaling. We then performed stellar
continuum fits using the methodology detailed in chisholm19, which fits a
linear combination of single-age and single-metallicity Starburst99 SPS models
(leitherer99; leitherer10) to determine which combination of models best fits
the observations, while simultaneously accounting for the dust attenuation
($E(B-V)$). The attenuation law from reddy16 was used for all fits. From this
best-fit, we derive $E(B-V)$ values and the light-weighted ages and
metallicities of the ionizing stellar population. Uncertainties on these
parameters were derived via a Monte Carlo technique, modulating the observed
flux with a Gaussian kernel centered on zero with a width equal to the error
on the flux. The stellar continuum was then re-fitted and the $E(B-V)$, age,
and metallicity values tabulated. This process was repeated 100 times to build
a distribution of parameters from which we calculated the standard deviation
of the distribution as the error on the estimated parameters.

In Figure 16 we compare the derived properties from the stellar continuum fits
of our scaled and non-scaled samples. The most pronounced difference is in the
best-fit reddening values (see top panel of Figure 16), with
0.0$<\Delta$E(B-V)$~{}\lesssim$0.12, and a (weighted) average percentage
reduction of $\sim$14%. The difference in the luminosity-weighted stellar
population age (middle panel of Figure 16) was less pronounced, however, with
a (weighted) mean difference of only $\sim$5% (corresponding to
$\langle\Delta$age$\rangle\sim$0.17 Myr). The scaling also had a negligible
effect on the stellar metallicity (bottom panel of Figure 16), with a
(weighted) mean percentage difference of only $\sim$0.3% overall
(corresponding to $\sim$0.004Z⊙). These difference in effects on each
parameter are due to the fact that the stellar wind and photospheric features
in the FUV that constrain the metallicity and age are not very sensitive to
changes in the continuum slope. These results suggest that an accurate
alignment of the continuum flux across the full FUV to NUV wavelength range is
imperative for deriving an accurate reddening of the stellar light. However,
flux calibration of the continuum is less important for deriving accurate
metallicity and age estimates of the stellar population.

Figure 19: The effect of COS NUV spectral extraction method on the C III
$\lambda$$\lambda$1907,1909 equivalent widths. In the top panel we show the
equivalent widths (in units of Å) of the C iii] $\lambda$$\lambda$1907,1909
emission-line doublet from the default CalCOS and optimised extraction method
utilized by CLASSY. The bottom panel shows the corresponding difference in EW
for each system. All galaxies classified as extended sources were re-processed
with the optimized extraction for the CLASSY coadd data, along with any
systems that showed a change in EW(C iii]) outside the uncertainties in the
measurement.

###  4.2 Emission line measurements

Nebular UV emission lines have a plethora of scientific applications,
including the determination of physical and chemical conditions, the level of
ionization of a source, and diagnostics of stellar age and metallicity. CLASSY
data have a suite of nebular emission line detections, including He ii
$\lambda$1640161616Both a narrow nebular and a broad stellar component are
often seen in He ii emission. As such, all emission line fitting of this line
takes place on stellar continuum subtracted data, as detailed in Mingozzi et
al. 2022, in-prep., O iii] $\lambda\lambda$1660,66, [C iii],C iii]
$\lambda\lambda$1907, 1909 (referred to as C iii] hereafter), and nebular
components of C iv $\lambda\lambda$1548,1551. Emission line fit parameters
from CLASSY data will potentially be affected by two components of the
procedures utilized to make the CLASSY coadded data: (i) the scaling between
the NUV and FUV gratings; and (ii) the optimized extraction of the NUV
datasets for extended sources. We explore each of these below.

To assess the effects of flux scaling between the FUV and NUV datasets on the
nebular emission-line science, we fit the C iii] emission-line doublets in the
CLASSY coadded spectra, with and without relative scalings applied. Flux
measurements were made for galaxies where C iii] and O iii] were detected,
producing a sample of 37 galaxies with C iii] and 29 galaxies with O iii]. In
Figure 17 we show the C iii] flux before and after scaling for the individual
sources (whose scale factors can be found in Figure 17). Overall we found a
median increase of 5% in the C iii] flux. Since the O iii] emission lies
within the G160M grating, which was chosen as the nominal dataset towards
which the G130M and G185M data were scaled, their fluxes do not change before
and after scaling.

Next, to test the scientific effect of the C iii] flux scaling, we computed
the C/O abundance ratios for both the scaled and unscaled flux values. We
determine direct-method C/O abundances using the method outlined in berg19a
with values from berg22 for the electron temperatures and densities, log([O
III] $\lambda$5007/[O II] $\lambda$3727), and 12+log(O/H) values. Briefly,
this method uses the C III]$\lambda$$\lambda$1907,1909/O III]$\lambda$1666
line ratio and the high-ionization zone temperature and density with the pyneb
python package to calculate the C+2/O+2 ion abundance. C+2/O+2 is then
converted to a total C/O abundance using an ionization correction factor
inferred from the log([O III] $\lambda$5007/[O II] $\lambda$3727), and
12+log(O/H) values.

The resulting C/O abundances are plotted in Figure 18, where we see that the
effect of the relative grating scalings is to slightly decrease the C/O
abundances. On average, the C/O abundances decreased by
$\langle\Delta$log(C/O)$\rangle$ = -0.055 dex, with a dispersion of
$\sigma\Delta$log(C/O) = 0.32 dex. Given the $\sim 0.1$ dex magnitude of this
effect, its relevance will depend on the S/N of emission lines of a given
spectrum and the subsequent uncertainty on the C/O value.

The customized extraction of the NUV data for extended sources that CLASSY
utilized will affect both the continuum and emission line flux within the NUV
wavelength range. In order to assess the effects of our methodology, we
measured the equivalent width of the C iii] $\lambda\lambda$1907, 1909
emission line doublet. Being a ratio between the emission line flux and the
continuum level flux, the equivalent width provides an unbiased view of the
NUV extraction method in that it is unaffected by the additional flux scaling
that was applied to several of the NUV datasets. In Figure 19 we compare the
EW(C iii]) of all the sources, measured on both the original NUV datasets and
with widened NUV extraction boxes. It can be seen that the optimized
extraction of the NUV datasets has a range of effects on the EW(C iii]), with
an overall mean decrease of $\sim-3$% but a mean increase of $\sim 17$% if we
consider just the extended sources. For each case, if the target was extended
or change in EW(C iii]) was larger than the uncertainties on the measurement,
we adopted the optimized extraction. For the majority of cases we see a
decrease in the EW measurement, owing to the fact that non-optimized NUV
extraction (1) did not fully encompass the cross-dispersion profile for
extended and MC sources and (2) over-estimated the background levels (as shown
in Figures 6 and 7). As such, one would expect an overall increase in the
continuum level and subsequent decrease in the EW values. However, for several
extended source targets, we instead see an increase in the EW(C iii]) - this
suggests that in these cases, the nebular emission is more spatially extended
than the continuum emission, resulting in larger net increase in emission line
flux compared to the continuum. The larger uncertainties in the optimized
extraction are due to an increased level of noise resulting from a larger
extraction box.

With regards to scientific impact, the C iii] EW has shown to have a
dependence on stellar age and nebular metallicity, in that the EW decreases as
a function of age and metallicity (e.g., erb10; stark14; rigby15; jaskot16;
senchyna17; nakajima18), although it remains unclear as to whether this
dependence holds for extremely metal poor systems such as SBS 0335-052
(wofford21). Thus being, if only the C iii] lines were used to constrain these
parameters (i.e., in high-$z$ systems where only bright UV nebular emission
lines may be observable in the optical, or low-$z$ systems with only UV
wavelength coverage), an over/underestimate of the C iii] EW would lead to
under/overestimated metallicities and stellar ages for a particular target. We
therefore highly recommend that users of NUV data of extended sources perform
a customized extraction of the NUV source profiles (e.g., using the
methodology described in Section 2.2 in the case of low-$z$ systems with COS
observations).

###  4.3 Absorption line fitting

As discussed in Section 3.5, and shown in Figure 8, the point source LSF of
the COS data is dependent on the lifetime position of the observation, and
changes in resolution of up to 25% can occur between data obtained at LP1 and
LP4. As such, when fitting absorption line profiles, users need to incorporate
the correct LSF for the particular dataset. However, since several of our
coadded spectra consist of data taken across multiple lifetime positions on
the detector, incorporating several LSFs into a line fitting procedure would
be complex and inefficient. Also, a change of $<$25% in the LSF may not be
noticeable given that (1) $\sim 60\%$ of our sources are extended such that
their spectral resolution will be lower than that achieved by a point source
profile and (2) the S/N of the CLASSY data is $<20$.

To assess the effects of changes in LSF within our datasets we fit a range of
isolated ISM and photospheric absorption lines (Si iv $\lambda$1402, Si ii
$\lambda$1260, Si ii $\lambda$1304, and C ii $\lambda$1334) using both the
correct LSF (i.e., the LSF for the LP of that particular dataset) and an LP4
LSF. An LP4 LSF was chosen as a comparison case because the majority of the
CLASSY data are taken at LP4 (October 2, 2017 until October 2021) and as such,
applying an LP4 LSF to all ISM measurements of CLASSY data would be
straightforward for users of CLASSY data if no differences are found. In both
cases we convolve the chosen LSF with a Gaussian profile with a width equal to
that measured from the COS acquisition images, thus creating a non-point-
source LSF. Lines were fit using a Gaussian line profile (as detailed in Xu et
al., in-prep) on stellar continuum-subtracted data (as detailed in Chisholm et
al. and Senchyna et al., both in-prep), binned by 15 pixels, using non-point-
source LSFs binned by the same amount. The velocity of the outflow (i.e., the
line centroid) was measured using the correct LSF and an LP4 LSF, for four
lines: Si ii $\lambda$1260, Si ii $\lambda$1304, C ii $\lambda$1334 and Si iv
$\lambda$1402\. The change in velocity measurement was found to be negligible,
with mean differences ranging between 0.4–2.8 km s-1 which is within the
uncertainties of the measurements. A similar test was performed by calculating
the FWHM of the same line profiles measured using both the correct LSF and the
LP4 LSF. Again, the difference was found to be minimal, with mean differences
ranging between 3–7 km s-1 which is within the COS calibration uncertainties.

This test shows that for this particular scientific application of the CLASSY
data, it is not necessary for the user to adopt the LSF particular to that
dataset and users can convolve their CLASSY coadded spectra with any of the
COS LSFs (LP1 to LP4) without affecting the line properties themselves. Of
course, this advice is applicable to the CLASSY data specifically. For COS
observations of point sources, for very high S/N data (S/N$>$20), and/or data
at the native resolution of COS (i.e., binned to only 6 pixels) the effect of
using the correct LSF may be far more apparent, and users are advised to use
the correct LSF according to the LP of their data.

##  5 Conclusions

The COS Legacy Archive Spectroscopic SurveY will provide the astronomical
community with the first high-resolution UV spectroscopic atlas of nearby
star-forming galaxies. With chemical and physical properties akin to systems
at high-$z$, the galaxies were chosen to provide a UV spectroscopic training
set for observations of galactic systems in the early Universe. The design and
full description of CLASSY and its primary HLSPs can be found in berg22. Here
we provide technical details of the data reduction and coaddition strategies
utilized by CLASSY, with particular attention paid to the extended source
profiles for which COS is not optimized. As such, this paper hopes to serve as
a technical guide for UV spectroscopic observations in general, with a
specific focus on observations of extended sources with HST/COS. We
additionally explore the scientific implications of our strategies. In
summary:

Data reduction: For sources with extended and/or multi-component light
profiles, no significant change in the S/N was found in FUV spectra using a
BOXCAR extraction method over the default optimized extraction method. This is
due to the fact that the majority of the source light profile was contained
within the aperture’s central 0$\farcs$4 radius. Moreover, spikes of decreased
S/N were seen due to an increased number of bad pixels being included within
the BOXCAR extraction. As such, the default TWOZONE extraction was adopted for
all sources for which it was available (i.e., datasets taken at LPs 3 and 4).

Improvements in the S/N and flux calibration of NUV data of extended/multi-
component sources were obtained by performing ad-hoc spectral extractions
according to the width of the cross-dispersion profile of each source. The
effects of the optimized NUV extraction can be seen in the equivalent width
measurements of C iii] which had a mean change of $\sim-3$% for all sources,
and an increase of 17% if we consider just the extended/MC sources. Without
this extraction method, EW(C iii]) measurements, which can be used to
constrain nebular metallicity and stellar age, may have resulted in poorly
estimated metallicities and stellar ages for these particular targets
according to an empirical relation found at high redshift.

Data coaddition: The primary CLASSY HLSP for each galaxy, the coadded data
product covering the full UV wavelength range, involves the combination of
spectra taken with several different gratings, many of which were taken at
different position angles and LPs on the COS detector. As such, it was
necessary to perform several checks on the alignment of data before the final
coaddition could take place, such as wavelength alignment, spectral
dispersion, flux calibration, PA or pointing offsets.

Firstly, the agreement in wavelength solution between the gratings was
investigated by comparing the alignment of a suite of isolated emission and
ISM absorption lines that were coincident within the datasets. An excellent
agreement was found in the wavelength scale between the COS gratings and no
adjustment was necessary.

Secondly, changes in spectral dispersion between gratings were accounted for
by re-binning the data to the largest dispersion value of the dataset
combination. While changes in spectral resolution of up to $\sim$25% between
datasets observed with different LSFs within the coadds (due to the change in
illumination angle at each LP on the detector) could not be accounted for,
such differences were found to have a negligible affect on the velocity and
outflow measurements of isolated absorption lines. This is mostly due to the
S/N of the CLASSY data (S/N$<$20) and binning of the data beyond the native
resolution of COS. This may not be the case for high S/N datasets (S/N$>$20)
and should be accounted for accordingly before coadding data across different
LPs on the COS detector.

Thirdly, the flux calibration within and between the FUV and NUV gratings was
inspected. Offsets of up to 20% in flux were found for extended/MC sources
where datasets were observed with different PAs. Such offsets were accounted
for by scaling towards a nominal dataset (in this case the G160M grating,
which represents the middle wavelength regime). For the majority of UV-
spectroscopic science cases, this approach is suitable in that the FUV spectra
will be continuum normalized and the continuum level itself is arbitrary.
However, properties of the massive star populations can be affected by the
shape of the continuum, in that reddening estimates would have changed by up
to $\Delta E(B-V)\sim 0.12$ if the FUV data had not been scaled accordingly
before coaddition took place. Conversely, no effects were seen on the
metallicity estimate of the stellar population between scaled and unscaled
data. The effects of flux calibration between the FUV and NUV data were also
seen in the C iii] emission line fluxes, which resulted in a mean offset of
$\sim-$0.1 dex in the C/O abundance ratios.

One important aspect of the coaddition process concerned the propagation of
uncertainties on the flux. During this process we assumed a symmetrical flux
distribution, which pertains to the Gehrels upper confidence limit reported by
CalCOS. This is appropriate for 65% of all CLASSY raw datasets. For those
remaining cases in the low count regime, i.e., where errors were in fact
asymmetric, we caution that the errors are accurate within 5-6%, and flag them
accordingly in the HLSP. As an issue that consistently plagues observations of
this type, we hope that the re-sampling of asymmetric errors becomes a focus
of future efforts in the community.

When combining datasets for extended/MC sources taken with different PAs,
changes in the dispersion and cross-dispersion profile may have an effect on
absorption line profiles and/or continuum flux level. The latter effect can be
mitigated by utilizing a BOXCAR extraction, albeit at a cost to S/N. We advise
users to use a constant orientation when observing extended/MC sources with
COS if they intend to combine datasets at the highest possible S/N.

Finally, after accounting for wavelength, flux, and spectral dispersion
offsets between individual datasets, it was found that a coaddition technique
that incorporates weighting by both the exposure time (squared) and the data-
quality flag of each pixel provided coadded data products with maximum S/N,
with smooth transitions between spectra from different gratings.

In summary, the data reduction and coaddition processes described here are
aimed to guide users of UV spectroscopic data towards creating accurately
calibrated and combined spectroscopic datasets, ready for scientific
application. In particular, we highlight the care and attention that needs to
be employed when dealing with extended/MC source observations made with an
instrument optimized for point source targets and the scientific impacts of
those specialized reductions.

As a result of the technical decisions and techniques that were carefully
assessed and adopted throughout this process, CLASSY will not only deliver the
first high-resolution UV spectral atlas of star-forming galaxies, but also
provide the astronomical community with a suite of high level science
products, including properties of the massive star population, Lyman continuum
escape fractions, outflows, metallicities, ionization strengths, UV-based
nebular diagnostics and much, much more. With a sample designed to mimic the
extreme specific SFRs and low-metallicities of high-$z$ systems, the CLASSY
spectral atlas and its plethora of scientific products will provide an
essential tool-kit for interpreting the high-$z$ systems observable in the
upcoming JWST/ELT era.

BLJ is thankful for support from the European Space Agency (ESA). DAB is
grateful for the support for this program, HST-GO-15840, that was provided by
NASA through a grant from the Space Telescope Science Institute, which is
operated by the Associations of Universities for Research in Astronomy,
Incorporated, under NASA contract NAS5-26555. The CLASSY collaboration extends
special gratitude to the Lorentz Center for useful discussions during the
”Characterizing Galaxies with Spectroscopy with a view for JWST” 2017 workshop
that led to the formation of the CLASSY collaboration and survey. The CLASSY
collaboration thanks the COS team for all their assistance and advice in the
reduction of the COS data, with particular thanks to Elaine Frazer for NUV
data extraction and Christian Johnson in assisting with error analysis.

##  Appendix A CLASSY Scaling Figures

In the following figures we show the flux calibration between the gratings for
each of the 45 CLASSY targets. A full description of this flux scaling process
can be found in Section 3.3, along with Figures 9 and 12. For each galaxy we
show the final coadded dataset and scaling factor applied to the G130M and
G185M/G225M gratings, along with the coadded data before and after scaling.
Flux calibration procedures for all 45 galaxies in the CLASSY sample are
provided in Figure Set 17, which is available in its complete form in the
online journal.

0.89  0.89  0.6

Figure 17:  Figure 17:  Figure 17:  Figure 17: Flux calibration procedure for
J0021+0052: (a) Featureless continuum regions in the G130M/G160M spectrum
(blue/orange shading) were fit with a 1D spline (blue-dotted/red-dashed line)
and used to scale the G130M spectrum to the G160M spectrum at their mid-way
intercept point (vertical blue dashed line) by the scale factor shown in the
legend.; (b) Same as in (a), here showing the 1D spline and derived scale
factor used to scale the G185M spectrum to the G160M spectrum; (c) Final
coadded data before (blue) and after (purple) scaling. The top two panels show
zoom-ins to the G130M and G185M/G225M datasets, with the corresponding
difference between the scaled versus non-scaled data in the lower two panels.
In each flux ($F_{\lambda}$) panel we show the flux$\pm 1\sigma$ error
spectrum, binned by 6 pixels.

Fig. Set17. CLASSY Co-add Scaling: available in the online journal