# The Hobby Eberly Telescope VIRUS Parallel Survey (HETVIPS)111Based on
observations obtained with the Hobby-Eberly Telescope (HET), which is a joint
project of the University of Texas at Austin, the Pennsylvania State
University, Ludwig-Maximillians-Universität München, and Georg-August-
Universität Göttingen. The HET is named in honor of its principal benefactors,
William P. Hobby and Robert E. Eberly.

Gregory R. Zeimann Hobby Eberly Telescope, University of Texas, Austin,
Austin, TX, 78712, USA  Maya H. Debski  Department of Astronomy, The
University of Texas at Austin, 2515 Speedway Boulevard, Austin, TX 78712, USA
Donald P. Schneider  Department of Astronomy & Astrophysics, The Pennsylvania
State University, University Park, PA 16802, USA  Institute for Gravitation
and the Cosmos, The Pennsylvania State University, University Park, PA 16802,
USA  William P. Bowman Department of Astronomy, Yale University, New Haven, CT
06511, USA  Niv Drory  McDonald Observatory, The University of Texas at
Austin, Austin, TX 78712, USA  Gary J. Hill McDonald Observatory, The
University of Texas at Austin, Austin, TX 78712, USA  Department of Astronomy,
The University of Texas at Austin, 2515 Speedway Boulevard, Austin, TX 78712,
USA  Hanshin Lee McDonald Observatory, The University of Texas at Austin,
Austin, TX 78712, USA  Phillip MacQueen  McDonald Observatory, The University
of Texas at Austin, Austin, TX 78712, USA  Matthew Shetrone  University of
California Observatories, University of California Santa Cruz, Santa Cruz, CA
95064, USA

(Accepted March 18, 2024)

###### Abstract

The Hobby-Eberly Telescope (HET) VIRUS Parallel Survey (HETVIPS) is a blind
spectroscopic program that sparsely covers approximately two-thirds of the
celestial sphere and consists of roughly 252 million fiber spectra. The
spectra were taken in parallel mode with the Visible Integral-field Replicable
Unit Spectrograph (VIRUS) instrument when the HET was observing a primary
target with other HET facility instruments. VIRUS can simultaneously obtain
approximately 35,000 spectra covering 3470 Å to 5540 Å at a spectral
resolution of $\approx$ 800\. Although the vast majority of these spectra
cover blank sky, we used the Pan-STARRS1 Data Release 2 Stacked Catalog to
identify objects encompassed in the HETVIPS pointings and extract their
spectra. This paper presents the first HETVIPS data release, containing
493,012 flux-calibrated spectra obtained through 31 March 2023, as well as a
description of the data processing technique. Each of the object spectra were
classified, resulting in a catalog of 74,196 galaxies, 4,087 quasars, 259,396
stars, and 154,543 unknown sources.

catalogs – stars: general — surveys – methods:statistical

††facilities: HET, MAST (Pan-STARRS)††software: Astropy (Astropy Collaboration
et al., 2013, 2018, 2022), scipy (Virtanen et al., 2019), numpy (Harris et
al., 2020)

May 1, 2024

##  1 Introduction

Over the past two decades, advances in instrumentation and in data processing
power have enabled systematic surveys that have produced datasets of
unprecedented size for the astronomical community. The first digital
“megasurvey”, the Sloan Digital Sky Survey (SDSS; York et al. 2000), has
produced over a dozen public data releases containing deep $ugriz$ photometry
over a quarter of the celestial sphere and optical/near-infrared spectroscopy
of millions of sources. Other examples are the Kepler mission (Borucki et al.
2010), with its high-precision photometric light curves of tens of thousands
of stars, and the Gaia mission (Gaia Collaboration et al. 2016), with its
stunning compilation of precise proper motions of on order a billion stars. In
a few years, the Large Synoptic Survey Telscope (LSST; Ivezić et al. 2019)
will begin operations, producing sub-arcsecond sampling of a large fraction of
the sky in multiple filters on the timescales of a few days.

In this paper we describe a new large astronomical program, the Hobby-Eberly
Telescope VIRUS Parallel Survey (HETVIPS), that will be generating data
regularly over the next several years, and present its first public data
release. The observations were obtained on the Hobby-Eberly Telescope (HET;
Ramsey et al. 1998; Hill et al. 2021) with the Visible Integral-field
Replicable Unit Spectrograph (VIRUS222VIRUS is a joint project of the
University of Texas at Austin,Leibniz-Institut für Astrophysik Potsdam (AIP),
Texas A&M University (TAMU), Max-Planck-Institut für Extraterrestrische Physik
(MPE), Ludwig-Maximilians-Universität Muenchen, Pennsylvania State University,
Institut für Astrophysik Göttingen, University of Oxford, and the Max-Planck-
Institut für Astrophysik (MPA). In addition to Institutional support, VIRUS
was partially funded by the National Science Foundation, the State of Texas,
and generous support from private individuals and foundations.; Hill et al.,
2021), a fiber-fed, multi-spectrograph instrument that can obtain low-
resolution optical spectra of nearly 35,000 sky positions simultaneously.
VIRUS is the instrument employed in the Hobby-Eberly Telescope Dark-Energy
Project (HETDEX, Hill et al., 2008; Gebhardt et al., 2021).

In addition to targeted observations like HETDEX, VIRUS is operated in a
parallel mode during HET observations using other facility instruments. The
parallel VIRUS data are, in essence, taken on areas of “blank” sky that are
within approximately 10′ of the primary target of the observation. The vast
majority of the spectra obtained in parallel mode are simply sky observations.
Using a deep imaging catalog from the PanSTARRS project (Chambers et al.
2016), we locate objects that have fallen in the VIRUS parallel fields, and
extract their spectra.

This first data release (HETVIPS DR1) is based upon the VIRUS parallel data
taken between 1 January 2019 to 31 March 2023. The raw database consists of
approximately 252 million spectra obtained in 7,389 observations. The HETVIPS
DR1 Continuum Catalog consists of 493,012 unique object spectra located over a
wide region on the celestial sphere. The spectra are classified into four
groups (star, galaxy, quasar, or unknown). The information contained in the
HETVIPS DR1 Catalog, including spectral classifications and redshifts, can be
used to address a wide variety of scientific projects ranging from the
chemical history of the Galaxy, individual stellar systems such white dwarfs,
and properties of quasars out to redshifts of 3.5.

The VIRUS parallel observations, including the sky coverage, exposure time
distribution, and the spectral depth are described in §2. The procedure for
extracting the calibrated fiber spectra, and the extraction of the object
data, are presented in §3 and §4, respectively. The continuum catalog
construction is reported in §5. The spectral classification methodology is
reviewed in §6. The HETVIPS DR1 catalog data products and the data
distribution system for the spectra are presented in §7, and a brief summary
of HETVIPS is provided in §9.

##  2 Observation Technique

###  2.1 Instrumentation

The HET is a fixed-altitude (zenith angle of $35^{\circ}$) telescope with an
11-m diameter mirror composed of 91 identical hexagonal spherical mirror
segments. During an observation, the telescope superstructure remains
stationary; the tracking is provided by a platform located at the prime focus
that carries the wide field corrector optics and fiber optic inputs (see Hill
et al., 2021). The telescope can access all points on the sky at
$-10.3^{\circ}<\delta<+71.6^{\circ}$; the maximum length of the tracks is
dependent upon declination, with the available track time generally increasing
from approximately one hour at the southern limit to 2.5 hours for the
northernmost observations.

The HET operates in a queue mode (Shetrone et al. 2007) and has three facility
instruments (all fiber fed): the aforementioned VIRUS, the Low-resolution
Spectrograph 2 (LRS2; Chonis et al. 2016), an optical/near-IR, $R\approx 2000$
integral field spectrograph, and the Habitable Zone Planet Finder (HPF;
Mahadevan et al. 2018), a precision radial velocity spectrograph that operates
in the near-infrared. The input for VIRUS consists of 78 integral field units
(IFUs) positioned in a grid that nearly spans the HET’s 22′ diameter corrected
field of view, positioned on an input head mount plate. The fibers for the
LRS2 and HPF are located in the central region of the plate (see Figure 6 of
Hill et al., 2021). The fiber inputs for all three instruments share a common
focal surface and shutter.

All of the HETVIPS observations were obtained with VIRUS. This instrument is
comprised of a set of identical 78 integral field spectrographs that produce
$\approx$ 35,000 spectra covering the wavelength range 3470Å – 5540Å at a
resolving power $R\approx 800$. The instrument’s sky footprint is 56 arcmin2
within an 18′ diameter field covered by the IFU grid layout; the fill factor
is $\simeq$ 1/4.5. Each IFU of 448 fibers covers a solid angle of
approximately 50″ x 50″ and feeds a pair of spectrographs. The separation
between IFUs is 100″. The individual fibers are 1.5″ in diameter, arrayed in a
hexagonal pattern with spacing between the fiber centers of 2.2″. During
HETDEX observations, a triangular dither pattern of three exposures nearly
fills these gaps ($\sim$94% sky coverage). All of the VIRUS parallel
observations are un-dithered; thus, the sky coverage within an IFU is roughly
one-third. A good analogy for the HETVIPS observations would be an imaging
instrument that covered an 18′ diameter field of view, but only slightly more
than 1 in 14 pixels of the device record data (1/3 factor for un-dithered
observations, 1/4.5 factor for the IFU coverage).

###  2.2 Observations

If the HET is acquiring an observation where the primary instrument is not
VIRUS and the exposure time is greater than five minutes, a VIRUS exposure is
obtained. The exposure time is set so that the VIRUS observation does not
impact the primary observation (i.e., VIRUS is employed in “parallel” mode).
The primary targets (using the LRS2 or HPF) of the VIRUS parallel observations
are located near the center of the field of view, and consist of a wide range
of classes of objects; thus, the HETVIPS survey is not contiguous, but
consists of a collection of a large number of pointings throughout the HET
observable sky ($\sim$2/3 of the total 4$\pi$ sky). Also, the VIRUS IFUs have
a $\sim$1/3 fill factor with the intent that a three-point dither pattern
results in a near full sky coverage for a given IFU. However, all of the
parallel mode observations in HETVIPS are un-dithered, leaving a gap of 2.2″
between the centers of each of the 1.5″ diameter fibers. While there are more
than 252 million fiber spectra in our dataset, the VIRUS fibers primarily
collected light from the night sky and rarely covered detectable astronomical
sources.

A total of 7,389 observations taken in this mode from 1 January 2019 through
31 Mar 2023 met our criteria (see S 3.13) for inclusion in the HETVIPS
database. Figure 1 displays the location of each HETVIPS pointing in Galactic
coordinates. Of the 7,389 pointings, 2,082 are unique celestial positions.
Nearly 50 locations have more than 10 visits (primarily HPF observations). The
HETVIPS observations cover both the Galactic plane and high-Galactic
latitudes. The total unique area covered by HETVIPS is roughly 26 deg2.

Figure 1: The location of the HETVIPS observations (red dots) displayed in
Galactic coordinates. The background is an infrared dust map of the Milky Way
(Schlegel et al., 1998). Many of the pointings have multiple exposures arising
from repeated visits to a given primary target (e.g., HPF radial velocity
monitoring of a bright star). The fixed azimuth of the HET results in an
observable declination constraint, producing two gaps in coverage; the small
hole in the upper right ($\delta>+71.6^{\circ}$) and the large area on the
lower left ($\delta<-10.3^{\circ}$).

Figure 2 presents the histogram of the exposure times for the HETVIPS
pointings. The exposure times range from 300 - 4500 seconds; the most common
exposure times are multiples of 300 seconds, and the median exposure time is
1800 seconds.

Figure 2: The histogram of exposure times for the 7,389 pointings in HETVIPS
DR1. HET observations shorter than 300 s are excluded from HETVIPS. The
maximum exposure time is limited by the travel constrains of the HET’s prime
focus tracking system.

Given the blind nature of the survey, and the sparse sampling of the
instrument, a catalog of sources in the HETVIPS fields is required to provide
astrometric and photometric calibration of the data. Such a catalog should
contain photometric measurements in the VIRUS bandpass, have high astrometric
precision, and reach relatively faint magnitudes. The Pan-STARRS (Chambers et
al. 2016) Data Release 2 Stacked Object Catalog (PS1 DR2, Flewelling et al.
2020) provides an excellent match to our requirements and fully encompasses
the HETVIPS observations.

##  3 Processing the Fiber Spectra

The VIRUS parallel observations were processed by
Remedy333https://github.com/grzeimann/Remedy, a software package designed to
produce flux-calibrated spectra for every VIRUS fiber, as well as create
spectra for objects of known position in the VIRUS footprint. VIRUS, at full
capacity, is composed of 78 IFUs, with each IFU feeding two spectrographs.
Each of these spectrographs has its own CCD, and each CCD is read through two
amplifiers. As a result, VIRUS data contains 312 distinct sets of CCD
parameters, each with its own bias structure, gain, read noise, and dark
current. A full set of VIRUS calibration observations consists of six types of
data frames: science, twilight, flat-field from a Laser Driven Light Source
(LDLS), arc lamp, bias, and dark.

###  3.1 Overscan Subtraction and Construction of Master Frames

The data from each of the 312 amplifiers has an overscan region. After
excluding the first two columns of the overscan due to potential “bleeding”
from the data section, the overscan’s biweight location (Beers et al., 1990)
is used to create a single value that is subtracted from the image. The
overscan region is trimmed from the frame, leaving only the data section.

To perform bias and dark current subtraction, distortion mapping, wavelength
calibration, fiber normalization, and poor data identification, we create
master frames for each of the six types of VIRUS exposures (bias, dark, arc
lamp, twilight, LDLS, and science). For bias, dark (all dark exposures are
360s), twilight, and LDLS exposures, we collect all observations in a calendar
month and perform a median average. Combining the data produces a master frame
for each calibration class for all 312 amplifiers. The Hg and Cd lamp frames
are combined separately and these “lamp masters” are used to produce a master
arc frame. For all of the master frame creations, we ignore amplifier
exposures that were all zeros; this situation occurs rarely and is due to
electronic failures.

With 312 different sets of electronics, VIRUS is a complicated instrument. As
a result, a given observation, night, or longer period can suffer from a
variety of problems. To broadly identify any issue, we use our median master
science frames, which are constructed from all HETDEX observations taken in a
calendar month ($\sim 50-200$). This median-filtered image should be devoid of
real sources, containing only signal from an “average sky”. We construct a
pseudo-error image for this master frame that mimics the error of a single
observation, rather than that of the stacked master frame. This approach
allows identification of issues in the sky-subtracted master frame spectra
that can produce systematic variations beyond the threshold of the typical
statistical error (i.e., when the systematic error is greater than this
threshold times the average statistical error). For example, charge traps are
not well-modeled in our fiber normalization process and produce systematic
outliers in sky-subtracted master science frame spectra.

###  3.2 Bias and Dark Subtraction

There are two classes of bias structure in a VIRUS amplifier. The first is a
slowly-evolving large-scale structure whose amplitude is typically much less
than the amplifier read noise, and is easily removed from a bias master frame.
The second type of structure occurs on a pixel-to-pixel scale, and resembles
an interference pattern. This feature changes rapidly throughout the night and
is not easily eliminated. It is, however, only $\lesssim$1 electron in
amplitude, and even less when averaged over a fiber profile in the trace
direction. We use the master bias frames to subtract the large-scale structure
as well as any coherent interference pattern.

The dark current in VIRUS amplifiers produce $\lesssim$0.5 electrons in a 360s
exposure. The VIRUS amplifiers are subject to spurious charge on the side of
the detectors closest to the amplifier readouts. The spurious charge can be
$\sim$3-4 electrons (in 10% of the amplifiers) and decays over the course of
approximately 100 rows. These features are locked into the master dark frames
and scaled by exposure time for subtraction.

###  3.3 Gain Correction

The output of the amplifier images are in ADUs (analog-to-digital units).
These values to are converted to electrons using the gain value for the
amplifier in the image header. This gain was calculated in the laboratory
prior to the detector’s installation in the spectrograph.

###  3.4 Error Propagation

The error value for each pixel is propagated from the read noise recorded in
the frame header and the Poisson error from the number of electrons detected.
No additional correction is made in the low-count regime, where the error
inferred from the observed counts differs from that derived from the true
number (Gehrels, 1986; Kraft et al., 1991). It is extremely rare for any of
the pixels that contribute to our spectral extractions to be in this low-count
regime due to contribution of sky continuum/emission illuminating the fibers.

###  3.5 Pixel Mask

The master dark frame is used to identify “hot” pixels and low-level charge
traps which must be masked in subsequent processing. The median value in each
column is subtracted from the master dark to remove the large-scale columnar
pattern, then the median value of each row is subtracted to similarly
eliminate low-level patterns in the row direction. In the final step, a
$\sigma$-clipping algorithm identifies and masks all pixels that are
$5\,\sigma$ outliers. The vast majority of these outliers are either hot
pixels or located at the base of low-level charge traps.

###  3.6 Fiber Trace

The traces of the individual fibers on the detectors are identified from high
signal-to-noise ratio LDLS exposures. For each wavelength pixel for an
individual fiber, the peak pixel and its two neighboring pixels are used to
define a quadratic function. The peak of the fitted quadratic function defines
the center of the trace of the fiber for a given column. A third-order
polynomial fit to the trace centers is used to produce a smooth curve for the
fiber.

To monitor the behavior of the fiber trace, the trace map of each frame is
compared to the trace from the current master LDLS frame. Over the two years
of HETVIPS observations the system has remained quite stable. The night-to-
night drift in position, driven primarily by changes in the ambient
temperature, is $\lesssim 0.2$ pixels for an individual fiber; the traces
appear to move around a stable location and do not experience a secular drift.

###  3.7 Fiber Wavelength Calibration

The wavelength calibration for each fiber is obtained from the Hg and Cd arc
lamp master frame exposures. The centers of the lines are identified with a
peak-finding algorithm with a priori knowledge of the strong line wavelengths.
A third-order polynomial fit to the line centers creates a stable wavelength
solution, and, as was the case for the fiber trace, typical shifts of
$\lesssim$0.2 pixels occur due to changes in the ambient temperature. The
wavelength solutions have typical rms errors of 0.05 Å. There are rare
failures in the wavelength solution process ($\approx$ 1% of the time), which
lead to a full masking of the amplifier.

###  3.8 Spectrograph Scattered Light

Low-level wings of light, scattered by residual imperfections in the surface
finish of the VIRUS spectrograph optics, are evident on the point spread
functions of the fiber images on the detectors (Hill et al., 2021). These
wings are modeled as a power-law, in which the monochromatic intensity of a
given fiber has a profile that falls off inversely with distance from the
center of the trace. When the sum of the signal from the central cores of all
the fibers is compared to the total light recorded by the detector, it appears
that approximately 3-4% of the incident fiber light is scattered out of the
cores of the point-spread functions into a relatively smooth background. This
value of total scattered light agrees with direct measurements on isolated
fibers (Hill et al. 2021, Fig. 11) and with expectations for the surface
finish specifications of the optics. The signal in the fiber gaps is used to
measure the background light and interpolate this background across an
amplifier. This background model is subtracted from the data. We consider the
light as lost from the system and do not attempt to correct for this loss of
signal in post-processing of the data.

###  3.9 Spectral Extraction

The fiber spectra are extracted by summing the central five pixels, i.e., $\pm
2.5$ pixels about the trace. The two outer pixels in this range are assigned
linear weights which represent the fraction of the pixel that is within 2.5
pixels of the trace. A normalized $\chi^{2}$ for each fiber is calculated by
comparing the fiber profile to that in the master LDLS frame. This $\chi^{2}$
provides a metric by which to flag cosmic rays and bad columns that fail to
follow the expected profile shape. We propagate the error for the spectrum in
quadrature, using the appropriate weight for the outer pixels. When the
spectrum is extracted, linear interpolation is used to place it and the
associated propagated errors on a rectified linear wavelength grid between
3470 Å and 5540 Å at a dispersion of 2 Å per pixel.

###  3.10 Fiber Normalization

The fiber normalization corrects the signal in each fiber relative to the
average throughput. We utilize two master frames to achieve this goal: the
twilight and LDLS. Since we did not correct our CCDs for pixel-to-pixel
variations, our summed extracted spectra still include the imprint of small
scale quantum efficiency differences. The LDLS intrinsic spectrum is smooth
with no sharp features. We use the LDLS master frame to extract spectra
following §3.9, and normalize our extracted spectra using a biweight smoothing
spline (kernel$\sim$80 Å). The resulting product provides a correction for our
small wavelength scale fiber normalizations.

The master twilight frame is employed to evaluate the larger wavelength scale
fiber normalizations by making the assumption that, averaged over many
twilight frames, the illumination across our $18\arcmin$ field-of-view is
uniform. We again extract master twilight frame spectra following §3.9, and
fit a biweight smoothing spline (kernel$\sim$80 Å) for the large-scale
normalization. We divide all science fiber spectra by the appropriate
normalization and propagate this division into the error spectra.

###  3.11 Spectral Masking

A VIRUS observation of one exposure contains up to 78 IFUs, each with 448
fibers, i.e., an individual exposure can contain up to 34,944 separate
spectra. As each IFU has two CCDs and each CCD has two amplifiers, there are a
number of CCD defects which must be identified and flagged. The pixel masking
procedure in the raw data was presented in §3.5. During the fiber extraction,
when collecting the relevant pixels for a given column and fiber, if just one
of the five pixels over which we sum to extract the spectra (see § 3.9) has a
masked value, we conservatively exclude the spectrum at that
column/wavelength. Similarly, when evaluating the normalized $\chi^{2}$ for
each wavelength of each fiber’s spectrum (see §3.9), if the normalized
$\chi^{2}$ is greater than five, that spectral wavelength is masked, as well
as columns $\pm 2$ pixels from the high $\chi^{2}$ column. Since the primary
cause for high $\chi^{2}$ values is the presence of a cosmic ray, our
conservative masking approach avoids the contribution from the fainter wings
of these artifacts. Finally, we also exclude fibers with normalizations less
than 50% (i.e., low throughput or dead fibers).

As discussed in §3.1, we construct master science frames from all HETDEX
observations in a calendar month. We use the fiber normalizations to build a
single sky spectrum for our master science images, subtract that sky from our
master science spectral extractions, and construct a pseudo-error image for
this master frame that mimics the error of a single observation. Data are
flagged for removal if they are less than $-0.5$ or greater than 0.75 times
the pseudo statistical error.

Finally, an entire fiber is removed if more than 20% of the columns in the
fiber are flagged. An entire column of an amplifier is eliminated if the
fraction of flagged pixels or the fraction of removed spectra exceeds 20%.

###  3.12 Sky Subtraction

We construct a single sky spectrum for each exposure using the biweight
location at each wavelength, excluding fibers whose average emission is
2-$\sigma$ above the background. This single composite sky spectrum is
subtracted from each fiber. After removing this sky model, the “blank” sky
fibers are used to create a smoother and more local sky residual employing a
Gaussian kernel in the fiber and wavelength direction. The kernel has a sigma
of seven fibers in the fiber direction and 14 Å in the wavelength direction.
As expected, this approach to sky subtraction performs best in observations
with a large fraction of “sky” fibers and encounters challenges when the field
of an IFU contains extended objects with angular sizes comparable to the IFU,
or large numbers of unresolved sources (e.g., globular clusters).

###  3.13 Astrometric Calibration

The astrometric calibration for each of the 252 million fibers is provided by
a comparison to the sources in PS1 DR2. We proceed IFU by IFU, collapsing each
sky-subtracted spectrum into a single value (integrated over the PS1 $g$
filter), interpolating this signal onto a uniform grid on the sky using the
focal plane information, detecting point sources in the collapsed image, and
matching these detections to the objects in the PS-1 DR2 catalog. We then fit
for a shift and rotation from the initial VIRUS astrometry provided in the
data frame information. The typical positional shift is less than 2″ and the
rotation is less than $0.1^{\circ}$. The biweight rms deviation of individual
stars’ positions about the solution for 95% of our fields is $\lesssim
0.5\arcsec$. We define the astrometric error for a field centroid as the
individual star rms divided by the square root of the number of stars used for
the solution. The astrometric error for 95% of our field centroids is
$\lesssim 0.15\arcsec$. For a VIRUS parallel observation to be included into
the final HETVIPS catalog, we required that at least 5 stars were used to get
the astrometric solution, that the astrometric error for the field centroid
was $\lesssim 0.25\arcsec$, and that the biweieght rms deviation of the
individual positions was $\lesssim 0.5\arcsec$.

In addition to the astrometric error of the field there are also uncertainties
in the focal surface position angle, IFU seat positions, IFU seat rotations,
and the fiber positions within an IFU. As previously noted, the focal surface
position angle is initially provided by the data frame headers and the re-
fitting for the angle involves a shift of less than $0.1^{\circ}$. Assuming an
accuracy of half of the adjustment, $0.05^{\circ}$ at a 4′ distance from the
field center is a positional uncertainty of 0.2″. The IFU seat positions are
known to 0.05″ accuracy from lab measurements and confirmed on-sky (Gebhardt
et al. 2021). The IFU seat rotations are known to $0.2^{\circ}$, which amounts
to a 0.08″ error at 22″ from the IFU center. Finally, the fiber positions
within the IFU are known to 0.05″ from laboratory measurements (Hill et al.,
2021). Taking all of these astrometry errors in quadrature, including the
field centroid error and the initial error in the PS1 catalog, there is a net
expected astrometric error of 0.30″ for fiber positions.

###  3.14 Flux Calibration

During an observation the HET guide cameras (GCs) are constantly providing
measurements of the system’s $g$-band throughput. The Remedy VIRUS throughput
curve (see Figure 3) for an effective 50 m2 mirror illumination is adjusted
based on this GC information. During an observation the HET tracker moves
across the primary mirror; thus the illumination of the primary mirror is
constantly changing during an observation. We use the GCs to monitor the
tracker position for each exposure and model the integrated mirror
illumination using HETILLUM. After correcting for the GCs’ throughput and the
primary mirror illumination, we use the set of PS1 DR2 sources from the
astrometry calibration, create their spectra (see §3.5), construct a synthetic
PS1 $g$ magnitude, and correct any average residual offset between the
synthetic and catalog magnitudes. The residual correction is usually less than
10%.

Figure 3: The Remedy VIRUS throughput curve used for the HETVIPS flux
calibration. The curve is quite similar to that of Figure 17 in Hill et al.
(2021) despite a different derivation and this work’s throughput curve is
plotted for an effective mirror illumination of 50 m2 rather than a full 10-m
aperture.

The median-average flux-calibrated fiber error spectrum is calculated for each
exposure; given that the vast majority of the spectra are of the sky, this
quantity is, in essence, the 1-$\sigma$ noise in the background. The
distribution of these 1-$\sigma$ error arrays are shown in Figure 4. The
5th–95th percentiles span three photometric magnitudes at all wavelengths,
with the greatest sensitivity at the night sky Ca II H and K absorption
features. The large span in sensitivity is due to the parallel nature of the
observations. HPF is a near-IR instrument that is primarily used during bright
moonlight conditions; most of the LRS2 data are acquired when the sky
brightness is considerably lower. In addition, the HETVIPS observations have a
large range of exposure times. These two factors result in a sizeable spread
in sensitivity of the exposures.

The 1-$\sigma$ of an individual fiber can be roughly converted to a 1-$\sigma$
continuum depth for a point source using a factor of five. Details about
source extraction and fiber covering fraction for HETVIPS can be found in §4.
As an example, if the 1-$\sigma$ of an individual fiber is 24.0 AB magnitude
at 4500 Å (which is near the average of the HETVIPS sensitivity), then the
1-$\sigma$ continuum depth of a point source for that observation and
wavelength would be $\approx$22.3 AB.

Figure 4: The left panel displays the median 1-$\sigma$ fiber error spectrum
in the sky per resolution element (spectral and spatial, in orange) for the
HETVIPS observations (essentially the background uncertainty). The average for
all 7,389 exposures is shown in black. The histogram of the AB magnitude
sensitivities at 5000 Å is shown on the right.

##  4 Extraction of the Object Spectra

Figure 5: The filled circles represent fiber footprint on the sky illustrating
the 1.5″ diameter. The spacing between the fiber centers is is 2.2″; this
expanded view of an IFU shows the 1/3 fill factor for a given VIRUS parallel
observation. The values in each fiber are the covering fraction for a point
source model centered at 0,0 with 1.8″ FWHM. The covering fraction is the
fraction of light from a source that the fiber is collecting. This covering
fraction changes with wavelength due to differential atmospheric refraction.
The dashed line is a 3″ aperture and is the radius at which we collect fibers
for extraction. The left and right panels show the two possible extreme source
locations with respect to the VIRUS fibers: a source centered on a fiber
(left) and a source that fell between the fibers (right).

Given HETVIP’s sparse and rather coarse sampling (compared to the seeing disk)
of the sky, it is not a straightforward task to reconstruct an object’s
spectrum from VIRUS parallel observations. Complicating matters further,
differential atmospheric refraction (DAR) shifts an objects location with
respect to the VIRUS fibers by $\sim$1″ across 3470 Å - 5540 Å. (The variation
of the effects of differential refraction between exposures is mitigated by
the fact that the HET operates at a fixed zenith angle.) Even if a source
falls directly on a fiber, the fraction of the object’s light collected by the
fiber changes as a function of wavelength. Additionally, since the average
seeing conditions for HETVIPS is 1.8″, the fraction of light collected by the
closest VIRUS fiber is typically $\lesssim$30% at any given wavelength.
However, if we can estimate the fraction of light each fiber collects within a
reasonable radius, we can convert the observed fiber spectra into a single
object spectrum.

Figure 6: Simulations of 2000 sources for two different locations with respect
to the VIRUS fibers were created to determine the effect of astrometric
uncertainty on extracted object spectra. Left Panel: The blue circles
represent VIRUS fibers in IFU coordinates. The ’X’ markers are colored by
radial distance and represent the 2000 simulated source input positions. The
assumed location of the source is (0,0) and the seeing has a FWHM of 1.8″. A
black vector arrow shows the direction and magnitude of DAR across the VIRUS
wavelength coverage; this change is the net effect of a source moving in the
IFU plane. The effect is applied equally for the simulated sources and the
extracted output objects. The two rows show the simulation for two scenarios:
astrometric offsets centered on a fiber and in-between fibers. Middle Panel:
The $16^{\rm th}$-$84^{\rm th}$ percentile ranges in binned astrometric
offsets for the extracted spectrum (Output) versus the simulated spectrum
(Input). The larger the offset the larger the systematic and relative flux
calibration issue. In the case of an object centered in the fiber (top row),
larger offsets tend to have less extracted flux because the assumed location
is centered on the fiber while the true location is offset, i.e., the true
coverage fraction is lower than expected and thus and requires a larger
normalization than the assumed applies. Right Panel: The same $16^{\rm
th}$-$84^{\rm th}$ percentile ranges in binned astrometric offsets for the
extracted spectrum (output) versus the simulated spectrum (input), but we
first normalize the output spectrum to the input spectrum. This step, as will
be seen later, is more reflective of the relative flux errors expected in the
HETVIPS continuum catalog.

We approach object extraction independently, i.e., we do not attempt to
extract many objects simultaneously but instead extract one object from one
observation at a time. For any given right ascension and declination, we first
collect all fiber spectra within a 7″ radius. Using the GC measurement of the
seeing, we construct a Moffat PSF model ($\beta=3.5$, Moffat 1969). At every
wavelength, we shift our fiber locations following our DAR models and convolve
that PSF with the VIRUS fibers. This estimates the fraction of the object’s
light covered by each fiber. Figure 5 displays the extreme variations in fiber
coverage of a standard point source and average seeing conditions for the
HETVIPS survey (1.8″). We limit our extractions to only fibers that intersect
with a 3″ aperture. Using these fiber coverage values as weights, we first
normalize the weights to one, retaining the normalization value, then perform
a weighted extraction using the Horne (1986) optimal extraction formula.
Finally, the resultant spectrum is corrected to a total flux using the
normalization value. Within a 3″ aperture, there are typically seven VIRUS
fibers with a total fiber coverage between 30-40%.

Because the total fiber coverage of a given source is relatively low, small
absolute errors in fiber weights can be magnified. We examined this effect by
simulating 2000 sources with a known input spectrum. We assume the source is
always at the same location relative to the fiber centers and thus the fiber
coverage weights for object extraction are identical, but we perturb the
simulated source input location so the true fiber coverage and fiber spectra
change. We uniformly distribute the offset location in radii between
0.0-0.5″and randomly distribute the angle. This procedure is performed for two
different configurations: the object centered on a VIRUS fiber, and the object
positioned equidistant between three fibers. The simulations and net effect on
relative flux calibration can be seen in Figure 6. For astrometric errors of
0.3″ as calculated in §3.13 we expect a 20-25% relative flux error in the blue
and a 5% flux error in the red. We also performed simulations where we varied
the seeing of the PSF and found that an incorrect assumption in the seeing of
0.2″ results in only a 7% flux error in the blue and $<$1% error in the red
(in 1.8″ images). The impact of an incorrect PSF is smaller than that due to
the estimated astrometric errors. Finally, errors in our DAR model are
expected to be $<$0.05″ across our wavelength range and have minimal effect on
our relative flux calibration ($<$3% in the blue).

For resolved objects, a point source model is incorrect but generally only
induces a small relative flux calibration issue (typically $<$5%) since in the
bulk of the cases most of the spectral information is produced from a single
fiber (see Figure 5). The measured flux from objects whose angular scales are
considerably larger than several arcseconds will obviously be underestimated
(perhaps by a large factor) by this technique, but the flux from sources whose
scale is comparable to a few arcseconds can be over or underestimated,
depending upon the scale size and location of the center relative to the fiber
grid.

##  5 HETVIPS DR1 Catalog

Figure 7: The relation between the PS1 DR2 $g$ magnitude (AB) versus the VIRUS
S/N per 2 Å pixel for the 1,644,191 individual HETVIPS spectra. The average
relationship between $g$-magnitude and S/N is indicated by the solid white
curve, with the 16th and 84th percentiles as dashed white curves. At $g$=19.5,
the average VIRUS S/N is roughly 10.

We use the PS1 DR2 catalog (with a 5-$\sigma$ AB photometric depth in $grizy$
of 23.3, 23.2, 23.1, 22.3, and 21.3) as the basis for creating the HETVIPS
object spectra. For observations before 2021-June-1, we used the PS1 DR2
stacked catalog and for observations on/after 2021-June-1, we used the PS1 DR2
mean catalog. We used the astropy.mast query system for both catalogs. All the
PS1 data used in this paper can be found in MAST:
https://doi.org/10.17909/s0zg-jx37 (catalog https://doi.org/10.17909/s0zg-
jx37). The two date ranges represent two discrete reduction efforts, and
between the two efforts we could not recover our initial query mode and had to
switch to the mean catalog. We extract all PS1 DR2 catalog sources within the
VIRUS field of view following §4. If less than 5% of the light is covered by
VIRUS fibers (assuming a point source model), the spectrum is excluded from
further consideration. We also require that the signal-to-noise ratio per 2 Å
pixel averaged over the PS1 $g$ filter is greater than one in the extracted
VIRUS spectrum. This last constraint may exclude emission-line objects that
have weak continua. For the 7,389 VIRUS exposures in HETVIPS, 1,644,191 PS1
DR2 sources meet these criteria. Because many primary targets have multiple
visits, a given HETVIPS source can have multiple spectra. There are 493,012
unique objects associated with the 1,644,191 spectra. For duplicate sources we
retain the extracted spectrum with the highest S/N. The relationship between
PS1 $g$-magnitude and the VIRUS S/N is displayed in Figure 7.

Figure 8: Repeat fields are used to compare the spectra of the same sources
observed multiple times to quantify our flux calibration as a function of
wavelength. We use 39,526 repeat spectra and compare each spectrum to the
highest signal-to-noise ratio source. We then calculated the median absolute
fractional difference using the highest signal-to-noise ratio source as the
denominator. Converting the median absolute fractional difference to a
standard deviation includes the multiplication factor of $\approx$1.4826, and
since we are searching for the flux calibration error of a single spectrum and
not the differences of spectra we divide by $\sqrt{2}$. This procedure
produces our estimate of the flux calibration error as a function of
wavelength.

In the creation of the catalog, we perform an additional flux calibration step
where a synthetic PanSTARRS $g$-magnitude from the VIRUS spectrum is
normalized to the measured aperture $g$-magnitude from the PS1 DR2 catalog
(recall that the aperture magnitudes from PS1 are corrected to a total flux
using a PSF model). This procedure ensures relatively accurate absolute flux
calibration despite the sparse fiber coverage of the sources. For resolved
sources, a normalization to the PS1 aperture $g$-magnitude (PSF-corrected) may
not be the most accurate approach and a more appropriate normalization would
be to the PS1 Kron magnitude. However, any extrapolation of a resolved
source’s spectral information beyond the extracted 3″ aperture used in Remedy
should be done with care.

Figure 9: A comparison of the flux calibration of 1,028 galaxies and 746 stars
found in both the SDSS and the HETVIPS surveys (limiting the HETVIPS catalog
to only objects with VIRUS S/N $>$ 10). After normalizing the HETVIPS spectra
to SDSS measurements the median trend shows $<$5% offset for both stars and
galaxies. The shaded regions cover the 16th-84th percentile offsets for stars
and galaxies (orange and blue, respectively), and represent the quadratic sum
of errors for SDSS and HETVIPS flux calibration.

Since many of our fields have repeat observations, we can internally calibrate
our relative flux calibration. We identified 12,989 sources in our unique
object catalog that had a S/N $>$ 100, and matched them to our larger catalog
of spectra including all of the repeat observations. After excluding self
matches, we constructed an array of 39,526 repeat spectra requiring that each
have a S/N $>$ 20\. We calculated the median absolute fractional difference
using the highest signal to noise ratio source as the denominator to estimate
our flux calibration error as a function of wavelength; Figure 8 shows the
results. We find the lowest calibration errors occur near the pivot wavelength
of the PS1 $g$ filter of around 2% and rise to 4% at 5500Å and nearly 25% at
3500Å.

The HETVIPS catalog was matched to the Sloan Digital Sky Survey (SDSS) Data
Release 16 (SDSS DR16) spectroscopic catalog (Ahumada et al., 2020) to measure
the quality of our flux calibration externally. HETVIPS covers roughly 26 deg2
on sky and roughly one half of that falls within the footprint of SDSS DR16.
There were a total of 4,616 matches between the two catalogs. Restricting the
analysis to sources with S/N $>$ 10, there are 1,028 galaxies and 746 stars in
common based on their SDSS classification. Dividing the sample into those two
categories reveals a good agreement between the two spectroscopic surveys
after allowing a single normalization (Figure 9).

##  6 Diagnose: Spectral Classification

To aid in the scientific use of this catalog, we developed a classification
code, Diagnose 444https://github.com/grzeimann/Diagnose, which is described in
the next subsection. The code assigns one of four classifications for each
source in the catalog (star, galaxy, quasar, or unknown), a redshift estimate
for the galaxies and quasars, and a radial velocity estimate for the stars.
Similar to Bolton et al. (2012), the code is a spectral classification and
redshift-finding algorithm via a ${\chi}^{2}$ minimization for linear
combinations of principal component templates.

Diagnose uses a principal component analysis (PCA) to classify sources as
stars, galaxies, quasars, or unknown. We used the templates of redrock
555https://github.com/desihub/redrock-templates, which are the same templates
used for SDSS-IV (Ross et al., 2020). The templates include 10 components for
galaxies and four components for quasars. Stars are further classified by
type, with six components for B, A, F, G, K, M, and white dwarfs.

Figure 10: Three example spectra from the HETVIPS continuum catalog. The VIRUS
spectra are plotted as blue error bars and the best-fit model is shown in
orange.

The top spectrum is a G star, the middle spectrum is a low-redshift galaxy,
and the bottom spectrum is a quasar. The gaps in the spectra (e.g., on the
short-wavelength wing of the [O II] emisison line in the galaxy spectrum) are
areas of masking where the fiber coverage of the source dropped below the
threshold.

We fit the stars over a range of radial velocities and the galaxies/quasars
over a range of redshifts. Before fitting, we construct a grid for each
template across the range of redshifts/velocities shown in Table 1. At each
grid point we convolve the high-resolution templates from redrock to the
spectral resolution of VIRUS. For a given source spectrum, the reduced
$\chi^{2}$ (${\chi}_{\nu}^{2}$) is used to evaluate the goodness of fit for
each template and grid point. The ${\chi}_{\nu}^{2}$ is just the original
$\chi^{2}$ divided by the number of degrees of freedom (DoF), where DoF is
equal to the number of unmasked data points in a spectrum minus the number of
principle components for a template. For each template, we refine the fit
around the minimum ${\chi}_{\nu}^{2}$ velocity/redshift using the two
neighboring grid points and fitting a second-order polynomial to the minimum.
For each spectrum, the classification code provides the best fit stellar type
and velocity, the best fit galaxy and redshift, and the best fit quasar and
redshift, as well as the best ${\chi}_{\nu}^{2}$ for each class. The absolute
minimum ${\chi}_{\nu}^{2}$ determines the best fit among the each of the
template types. We calculate the difference between the template with the best
${\chi}_{\nu}^{2}$ and the template with the second best ${\chi}_{\nu}^{2}$;
if this difference in values is larger than a statistical threshold
($\sqrt{2/DoF}$), we classify the source as the best fit template (i.e., star,
galaxy, or quasar). If the difference is not larger than the threshold, we
classify the source as unknown. Figure 10 displays three example spectra from
the HETVIPS continuum catalog, along with their best fit Diagnose model.

Table 1: Diagnose Velocity/Redshift Fit Parameters Classification | Redshift/Velocity Range | Redshift/Velocity Step Size
---|---|---
Star | [-500, 500] km s-1 | 50 km s-1
Galaxy | [0.005, 0.470] | 0.001
Quasar | [0.10, 4.00] | 0.01

Our ability to classify a source is, as expected, strongly correlated with the
S/N in the VIRUS spectrum. Figure 11 presents the fraction of sources with a
robust classification versus S/N. We also have notable exceptions in which the
target has a moderate or high S/N, but the classification is unknown due to a
small $\Delta$${\chi}_{\nu}^{2}$ between the three labels. Figure 12 shows
three examples of unknown spectra and the three best fit models from each
group.

Also, just because a source has a classification does not necessarily means
the label is correct. To determine the accuracy and efficacy of Diagnose, we
compared the Diagnose classifications and redshifts to those from SDSS for the
4,616 sources that were spectroscopically observed in both surveys.

Figure 11: The fraction of sources with a classification from Diagnose versus
VIRUS S/N. A classification from Diagnose requires a the difference in
${\chi}^{2}$ values between the best fit label and the second best fit label
to be above a given threshold. At lower S/N, the ${\chi}^{2}$ values for
multiple labels are too close to confidently distinguish between a galaxy,
star, or quasar.

For the 4,616 sources spectroscopically observed in SDSS and HETVIPS, the
labels assigned by the two surveys are in good agreement. If we adopt the SDSS
classification as truth, the Diagnose classification matched SDSS’s
classification for 96.9%, 94.7%, and 92.3% for stars, galaxies, and quasars,
respectively, when both SDSS and HETVIPS classifications were available.
Figure 13 shows the Diagnose classification versus SDSS classification as a
function of VIRUS S/N. The unknown sources are clustered at low S/N while the
mis-classified sources are more evenly distributed across S/N.

Figure 12: Three examples of spectra of moderate to high S/N that were
classified as unknown. The VIRUS spectra are plotted as blue error bars and
the best-fit star, galaxy and quasar models are shown in orange, green and
red, respectively. The SDSS classifications are star (top), low-redshift
galaxy (middle), and low-redshift quasar (bottom). Because the
$\Delta$${\chi}_{\nu}^{2}$ between the best fit VIRUS spectrum from each
object is too small to meet the threshold, each of these sources is classified
as unknown.

We also investigate the reliability of our classification as a function of the
difference of the best fit model ${\chi}_{\nu}^{2}$ minus the
${\chi}_{\nu}^{2}$ of the second best fit model, divided by the statistical
threshold ($\sqrt{2/DoF}$). Figure 14 presents the Diagnose classification
reliability (i.e., the classification matches SDSS) versus the
$\Delta$${\chi}_{\nu}^{2}$ threshold ratio. The reliability of our
classification at exactly a threshold ratio of one is roughly 80% and the
reliability of a catalog with a threshold ratio greater than one is about 95%.

Figure 13: A comparison of the classifications from Diagnose to that of SDSS
for stars, galaxies, and quasars as a function of VIRUS S/N per 2 Å pixel. The
blue histogram with binomial error bars represents the fraction of sources
with matching classifications, while the red histogram shows sources with
mismatching classifications. The orange histogram are the sources without a
classification from Diagnose in which the the difference in ${\chi}^{2}$
values between the best fit label and the second best fit label was below the
significance threshold. The mismatches are not correlated with S/N but are
rather uniformly distributed. Figure 14: A relation between the classification
reliability from Diagnose (i.e., classification matched SDSS) as a function of
difference of the best fit model ${\chi}_{\nu}^{2}$ minus the second best fit
model ${\chi}_{\nu}^{2}$ divided by the statistical threshold
($\sqrt{2/DoF}$). The blue histogram represents the actual label reliability,
while the blue curve is a 3rd order polynomial fit to the blue histogram. The
cumulative reliability is indicated in red, which would be the expected
reliability of the total catalog for a given threshold ratio cut. At 0.2 times
the current ${\chi}_{\nu}^{2}$ threshold, the entire sample is expected to be
90% reliable in its labels, while at the current threshold value the
reliability is around 95%. The “differential” reliability in blue drops
rapidly below 1 as a function of threshold choice.

The most common mis-classification occurs when Diagnose labels the source as a
galaxy while SDSS identifies the source as a quasar. The limited wavelength
coverage of VIRUS compared to SDSS means that single, narrow lines can easily
be reproduced by both a galaxy template basis and a quasar template basis.
Since the galaxy basis has 10 components rather than the 4 of the quasar, the
extra degrees of freedom (even when accounted for in the ${\chi}_{\nu}^{2}$)
result in a Diagnose mis-classification. This case of mis-classification
produces an incorrect redshift assessment as the upper boundary of the galaxy
redshift range is $z$=0.47, and Ly$\alpha$ is often confused with [O II].

We compare the redshifts derived from Diagnose and SDSS in Figure 15. The
galaxy redshifts are in excellent agreement with only 5% outliers, and a
redshift standard deviation of ${\Delta}z=0.0002$. The quasar redshifts in
Figure 15 are also in good agreement, but include nearly 10% outliers and a
larger standard deviation of ${\Delta}z=0.0013$.

Figure 15: The galaxy and quasar redshifts from Diagnose are compared to those
from SDSS. The normalized median absolute deviation (NMAD) and outlier
fraction are calculated as in Momcheva et at. (2016). The redshifts for the
galaxies are highly reliable; the quasar measurements, while generally
accurate, do suffer from a number of catastrophic failures.

###  6.1 Catalog Breakdown

After processing the 493,012 unique HETVIPS continuum sources through
Diagnose, 259,396 objects are classified as stars, 74,196 as galaxies, 4,087
as quasars, and 154,543 as unknown. Diagnose also produces the best fit
stellar type. The program classified 2,093 objects as B stars, 12,040 as A
stars, 24,152 as F stars, 66,552 as G stars, 133,457 as K stars, 13,617 as M
stars, 1,648 as white dwarfs. We caution interpretation of the spectral type
distribution, as there are known issues with the subgroup classifications from
Diagnose (see §6.2).

###  6.2 Known Issues

As stated in §6, $\sim$ 97% of sources classified as stars by Diagnose were
also classified as stars by SDSS. Diagnose also produces the best fit stellar
type: B, A, F, G, K, M, or W (white dwarf). We inspected each population of
stars within the subgroups and found the labels to be somewhat unreliable.
Flux calibration is discussed in §4, and with individual errors as large as
20-25% in the blue, it is quite easy for stars to move from one subgroup label
to another due to mis-calibration, limited wavelength coverage, and PCA
flexibility. Also, even though one subgroup may be listed as the best fit, the
second best ${\chi}_{\nu}^{2}$ may not be substantially different than the
best fit subgroup label.

We plan to improve Diagnose’s ability to assign stellar types in the future;
in this paper, the label of a HETVIPS star is likely to be correct but, the
sub classification should be independently verified by the user.

Since sources are extracted individually with no consideration for
contribution from their neighboring systems, we expect that some of our object
spectra include small contributions from their neighbors. Also, we perform a
local sky subtraction, which can be effected by bright stars and extended
galaxies. Sky subtraction and neighbor contribution effects are strongest in
crowded stellar fields. Over subtraction of the “sky” can lead to negative
fluxes, incorrect continua shape, and mis-classifications (especially for
stellar subgroups).

Although Remedy employs a fiber masking effort to identify fiber spectra
effected by bad columns, cosmic rays, or amplifier issues, with 252 million
fiber spectra in HETVIPS DR1 there are a number of potential issues that may
be unidentified. Objects extracted from fibers with unidentified issues
typically exhibit odd continua and exaggerated absorption features. The
subgroup of F stars seem to contain most of these issues. This behavior is not
an intrinsic feature of F stars, but the F star PCA basis has the greatest
flexibility to model the object when it is truly a star and suffers from these
data processing issues. In future releases, we plan to more thoroughly capture
odd behaviors in object spectra and provide a comprehensive flag system for
quality assurance.

##  7 Data Products

The overview of the HETVIPS data products is provided by an ASCII table, which
has one line per source. The table contains celestial coordinates, PS1
photometry, VIRUS S/N, ${\chi}_{\nu}^{2}$ for each classification, the
redshift for each classification, and the best fit redshift (see Table 2).

Column Name | Description | Units
---|---|---
objID | HETVIPS Object ID | string
RA | PanSTARRS1 Right Ascension (J2000) | degree
Dec | PanSTARRS1 Right Ascension (J2000) | degree
shotid | VIRUS Shot ID {DATE}_{OBSNUM} | string
gmag | PanSTARRS1 aperture $g$ magnitude (PSF corrected to total flux) | AB magnitude
rmag | PanSTARRS1 aperture $r$ magnitude (PSF corrected to total flux) | AB magnitude
imag | PanSTARRS1 aperture $i$ magnitude (PSF corrected to total flux) | AB magnitude
zmag | PanSTARRS1 aperture $z$ magnitude (PSF corrected to total flux) | AB magnitude
ymag | PanSTARRS1 aperture $y$ magnitude (PSF corrected to total flux) | AB magnitude
sn | VIRUS S/N averaged over PS1 $g$ filter | float
barycor | Barycentric velocity correction | m s-1
mjd | Modified Julian Date for the middle of the VIRUS exposure | day
exptime | Exposure time for the VIRUS observation | s
chi2_star |  ${\chi}_{\nu}^{2}$ for best fit stellar PCA template (and velocity) | float
chi2_galaxy |  ${\chi}_{\nu}^{2}$ for best fit galaxy PCA template (and redshift) | float
chi2_qso |  ${\chi}_{\nu}^{2}$ for best fit quasar PCA template (and redshift) | float
z_star | Best fit stellar template redshift | float
z_galaxy | Best fit galaxy template redshift | float
z_qso | Best fit quasar template redshift | float
z_best | Best fit template redshift (if classification is unknown, z_best = -999) | float
classification | STAR, GALAXY, QSO, or UNKNOWN | string
stellartype | Best fit templates for the stellar PCA (B, A, F, G, K, M, C, or W) | string
Table 2: Description of the ASCII catalog columns. Stellar type W is white
dwarf.

Diagnose produces a FITS file with several extensions containing information
about each source. These extensions include: the best fit models (star,
galaxy, and quasar), the classification, ${\chi}_{\nu}^{2}$ (for stars,
galaxies, and quasars), statistical threshold for the
$\Delta$${\chi}_{\nu}^{2}$, redshifts (star, galaxy, and quasar), the best fit
stellar type, as well as a table of information include PS1 photometry and sky
position. The catalog is broken into twenty FITS files to keep the individual
size to roughly 500Mb.

We describe in greater detail each of the extensions in numerical order:

  1. 1.

The Diagnose models and VIRUS spectra. It is a 3-dimensional array that is
Nobj long, six rows down, and 1,036 columns deep. The 1,036 columns are the
length of the spectra starting at 3470Å in intervals of 2Å ending at 5540Å.
The six rows are the best fit Diagnose star model, galaxy model, quasar model,
VIRUS spectrum, VIRUS error spectrum, and VIRUS fiber weight array (see §4),
respectively. The header of the primary extension describes this information,
as well including the wavelength information.

  2. 2.

The Diagnose classifications. We use numerical representation for the
classifications; specifically, stars are 1, galaxies are 2, quasars are 3, and
unknown sources are 4. All extensions are Nobj long with the same ordering as
extension 1.

  3. 3.

The ${\chi}_{\nu}^{2}$ values for the best fit models of the three groups:
stars, galaxies, and quasars, respectively.

  4. 4.

The ${\chi}_{\nu}^{2}$ threshold for each source in the FITS file. The
${\chi}_{\nu}^{2}$ statistical threshold is $\sqrt{2/DoF}$. This threshold was
used to compare the best fit Diagnose ${\chi}_{\nu}^{2}$ model to the second
best fit, and if the difference was bigger than the threshold, a unique
classification was determined.

  5. 5.

The best fit redshifts/velocities for each source. The first column is the
best fit velocity in km/s, while the second and third column are the best fit
redshift for the galaxy and quasar groups, respectively.

  6. 6.

The best fit stellar type: B, A, F, G, K, M, or W (white dwarf).

  7. 7.

A binary table about each source in the FITS file. The columns of the table
include the HETVIPS object ID, right ascension (J2000), declination (J2000),
VIRUS shot ID, the PS1 photometry, the average VIRUS S/N in the spectrum, the
barycentric velocity correction (not already applied to the spectra), the
date, and the VIRUS shot’s exposure time.

We do not provide the database of 252 million VIRUS fiber spectra from which
the catalog was generated. That product remains internal to the HET community.

###  7.1 Data Distribution

The ASCII catalog and the FITS files are available publicly at
https://web.corral.tacc.utexas.edu/hetdex/HETVIPS/. We suggest downloading the
ascii catalog to investigate the sky positions of the 493,012 sources in the
FITS files. If you want to investigate the spectra, the easiest method is to
download the FITS files and loop through each file to execute your desired
program.

##  8 Science Cases

The HETVIPS spectroscopic dataset is valuable for a range of scientific
programs. We highlight just a few science cases for consideration. The HETVIPS
spectra, covering 3470 Å to 5540 Å, can be used to determine a star’s
effective temperature, gravity, and metallicity (e.g., [Fe/H]) as demonstrated
by Hawkins et al. (2020). Future studies could look for hyper velocity stars,
extremely metal poor stars, or investigate stellar metallicity of the Milky
Way along many sight-lines through the galaxy. The Balmer lines (e.g.,
H$\beta$) can be used to determine the mass accretion rate of a young stellar
objects (YSOs) to help understand how stars form and how their circumstellar
disks evolve with time as was already done with a small subset of VIRUS
parallel observations in Willett et al. (in prep). The HETVIPS spectra can
also provide a way to classify a number of supernova objects found by
photometric surveys as shown in Vinkó et al. (2023). Finally, Liu et al.
(2023) illustrated that VIRUS spectra can also be used to investigate the
spatially resolved pre-explosion local environments of known supernovae.

##  9 Summary

We present HETVIPS DR1 derived from VIRUS parallel data taken on the HET from
01 Jan 2019 to 31 Mar 2023. The raw database consists of approximately 252
million spectra obtained in 7,389 observations and although most fibers are
dominated by sky, we construct and publicly distribute the HETVIPS DR1
Continuum Catalog, which consists of 493,012 unique object spectra located
over a wide region on the celestial sphere. The spectra are classified using a
code called Diagnose, with each object divided into one of four groups (star,
galaxy, quasar, or unknown). The information contained in the HETVIPS DR1
Catalog, including spectral classifications and redshifts, can be used to
address a wide variety of scientific projects ranging from the chemical
history of the Galaxy to the local environment of extragalactic supernovae.

We thank the staff at the Hobby Eberly Telescope for making this project
possible. The authors acknowledge the Texas Advanced Computing Center (TACC)
at The University of Texas at Austin for providing computing resources that
have contributed to the research results reported within this paper. URL:
http://www.tacc.utexas.edu The Pan-STARRS1 Surveys (PS1) and the PS1 public
science archive have been made possible through contributions by the Institute
for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the
Max-Planck Society and its participating institutes, the Max Planck Institute
for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial
Physics, Garching, The Johns Hopkins University, Durham University, the
University of Edinburgh, the Queen’s University Belfast, the Harvard-
Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global
Telescope Network Incorporated, the National Central University of Taiwan, the
Space Telescope Science Institute, the National Aeronautics and Space
Administration under Grant No. NNX08AR22G issued through the Planetary Science
Division of the NASA Science Mission Directorate, the National Science
Foundation Grant No. AST–1238877, the University of Maryland, Eotvos Lorand
University (ELTE), the Los Alamos National Laboratory, and the Gordon and
Betty Moore Foundation. Some of the data presented in this paper were obtained
from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope
Science Institute. The specific observations analyzed can be accessed via
https://doi.org/10.17909/s0zg-jx37 (catalog https://doi.org/10.17909/s0zg-
jx37). STScI is operated by the Association of Universities for Research in
Astronomy, Inc., under NASA contract NAS5–26555. Support to MAST for these
data is provided by the NASA Office of Space Science via grant NAG5–7584 and
by other grants and contracts. Funding for SDSS-III has been provided by the
Alfred P. Sloan Foundation, the Participating Institutions, the National
Science Foundation, and the U.S. Department of Energy Office of Science. The
SDSS-III web site is http://www.sdss3.org/. SDSS-III is managed by the
Astrophysical Research Consortium for the Participating Institutions of the
SDSS-III Collaboration including the University of Arizona, the Brazilian
Participation Group, Brookhaven National Laboratory, Carnegie Mellon
University, University of Florida, the French Participation Group, the German
Participation Group, Harvard University, the Instituto de Astrofisica de
Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns
Hopkins University, Lawrence Berkeley National Laboratory, Max Planck
Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics,
New Mexico State University, New York University, Ohio State University,
Pennsylvania State University, University of Portsmouth, Princeton University,
the Spanish Participation Group, University of Tokyo, University of Utah,
Vanderbilt University, University of Virginia, University of Washington, and
Yale University.

## References

  * Ahumada et al. (2020) Ahumada, R., Prieto, C. A., Almeida, A., et al. 2020, ApJS, 249, 3, doi: 10.3847/1538-4365/ab929e
  * Astropy Collaboration et al. (2013) Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068
  * Astropy Collaboration et al. (2018) Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f
  * Astropy Collaboration et al. (2022) Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167, doi: 10.3847/1538-4357/ac7c74
  * Beers et al. (1990) Beers, T. C., Flynn, K., & Gebhardt, K. 1990, AJ, 100, 32, doi: 10.1086/115487
  * Bolton et al. (2012) Bolton, A. S., Schlegel, D. J., Aubourg, É., et al. 2012, AJ, 144, 144, doi: 10.1088/0004-6256/144/5/144
  * Borucki et al. (2010) Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977, doi: 10.1126/science.1185402
  * Chambers et al. (2016) Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints, arXiv:1612.05560.  https://arxiv.org/abs/1612.05560
  * Chonis et al. (2016) Chonis, T. S., Hill, G. J., Lee, H., et al. 2016, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9908, LRS2: design, assembly, testing, and commissioning of the second-generation low-resolution spectrograph for the Hobby-Eberly Telescope, 99084C, doi: 10.1117/12.2232209
  * Flewelling et al. (2020) Flewelling, H. A., Magnier, E. A., Chambers, K. C., et al. 2020, ApJS, 251, 7, doi: 10.3847/1538-4365/abb82d
  * Gaia Collaboration et al. (2016) Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1, doi: 10.1051/0004-6361/201629272
  * Gebhardt et al. (2021) Gebhardt, K., Mentuch Cooper, E., Ciardullo, R., et al. 2021, ApJ, 923, 217, doi: 10.3847/1538-4357/ac2e03
  * Gehrels (1986) Gehrels, N. 1986, ApJ, 303, 336, doi: 10.1086/164079
  * Harris et al. (2020) Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2
  * Hawkins et al. (2020) Hawkins, K., Lucey, M., Ting, Y.-S., et al. 2020, MNRAS, 492, 1164, doi: 10.1093/mnras/stz3132
  * Hill et al. (2008) Hill, G. J., Gebhardt, K., Komatsu, E., et al. 2008, in Astronomical Society of the Pacific Conference Series, Vol. 399, Panoramic Views of Galaxy Formation and Evolution, ed. T. Kodama, T. Yamada, & K. Aoki, 115.  https://arxiv.org/abs/0806.0183
  * Hill et al. (2021) Hill, G. J., Lee, H., MacQueen, P. J., et al. 2021, AJ, 162, 298, doi: 10.3847/1538-3881/ac2c02
  * Horne (1986) Horne, K. 1986, PASP, 98, 609, doi: 10.1086/131801
  * Ivezić et al. (2019) Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c
  * Kraft et al. (1991) Kraft, R. P., Burrows, D. N., & Nousek, J. A. 1991, ApJ, 374, 344, doi: 10.1086/170124
  * Liu et al. (2023) Liu, C., Chen, X., Er, X., et al. 2023, ApJ, 958, L37, doi: 10.3847/2041-8213/ad0da8
  * Mahadevan et al. (2018) Mahadevan, S., Anderson, T., Balderrama, E., et al. 2018, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10702, The habitable-zone planet finder: engineering and commissioning on the Hobby Eberly telescope, 1070214, doi: 10.1117/12.2313835
  * Moffat (1969) Moffat, A. F. J. 1969, A&A, 3, 455
  * Ramsey et al. (1998) Ramsey, L. W., Adams, M. T., Barnes, T. G., et al. 1998, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 3352, Advanced Technology Optical/IR Telescopes VI, ed. L. M. Stepp, 34–42, doi: 10.1117/12.319287
  * Ross et al. (2020) Ross, A. J., Bautista, J., Tojeiro, R., et al. 2020, MNRAS, 498, 2354, doi: 10.1093/mnras/staa2416
  * Schlegel et al. (1998) Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525, doi: 10.1086/305772
  * Shetrone et al. (2007) Shetrone, M., Cornell, M. E., Fowler, J. R., et al. 2007, PASP, 119, 556, doi: 10.1086/519291
  * Vinkó et al. (2023) Vinkó, J., Thomas, B. P., Wheeler, J. C., et al. 2023, ApJ, 946, 31, doi: 10.3847/1538-4357/acbfa8
  * Virtanen et al. (2019) Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2019, arXiv e-prints, arXiv:1907.10121.  https://arxiv.org/abs/1907.10121
  * York et al. (2000) York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579, doi: 10.1086/301513