# DESI Early Data Release Milky Way Survey Value-Added Catalogue

Sergey E. Koposov,1,2,3 C. Allende Prieto,4 A. P. Cooper,5 T. S. Li,6 L.
Beraldo e Silva,7,8 B. Kim,1 A. Carrillo,9 A. Dey,10 C. J. Manser,11,12 F.
Nikakhtar,13 A. H. Riley,9 C. Rockosi,14,15,16 M. Valluri,7,17 J. Aguilar,18
S. Ahlen,19 S. Bailey,18 R. Blum,10 D. Brooks,20 T. Claybaugh,18 S. Cole,9 A.
de la Macorra,21 B. Dey,22 J. E. Forero-Romero,23,24 E. Gaztañaga,25,26,27 J.
Guy,18 A. Kremin,18 L. Le Guillou,28 Michael E. Levi,18 M. Manera,29,30 A.
Meisner,10 R. Miquel,31,30 J. Moustakas,32 J. Nie,33 N. Palanque-
Delabrouille,34,18 W. J. Percival,35,36,37 M. Rezaie,38 G. Rossi,39 E.
Sanchez,40 E. F. Schlafly,41 M. Schubnell,42,17 G. Tarlé,17 B. A. Weaver,10
and Z. Zhou33
Authors affiliations are provided in the Appendix D E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We present the stellar value-added catalogue based on the Dark Energy
Spectroscopic Instrument (DESI) Early Data Release. The catalogue contains
radial velocity and stellar parameter measurements for $\simeq$ 400,000 unique
stars observed during commissioning and survey validation by DESI. These
observations were made under conditions similar to the Milky Way Survey (MWS)
currently carried out by DESI but also include multiple specially targeted
fields, such as those containing well-studied dwarf galaxies and stellar
streams. The majority of observed stars have $16<r<20$ with a median signal-
to-noise ratio in the spectra of $\sim$ 20\. In the paper, we describe the
structure of the catalogue, give an overview of different target classes
observed, as well as provide recipes for selecting clean stellar samples. We
validate the catalogue using external high-resolution measurements and show
that radial velocities, surface gravities, and iron abundances determined by
DESI are accurate to 1 km $s^{-1}$, $0.3$ dex and $\sim$ 0.15 dex
respectively. We also demonstrate possible uses of the catalogue for chemo-
dynamical studies of the Milky Way stellar halo and Draco dwarf spheroidal.
The value-added catalogue described in this paper is the very first DESI MWS
catalogue. The next DESI data release, expected in less than a year, will add
the data from the first year of DESI survey operations and will contain
approximately 4 million stars, along with significant processing improvements.

###### keywords:

catalogues – surveys – Galaxy: general – Galaxy: kinematics and dynamics –
stars: abundances

††pubyear: 2015††pagerange: DESI Early Data Release Milky Way Survey Value-
Added Catalogue–D

##  1 Introduction

In the last twenty years, major progress in stellar astrophysics, Galactic
dynamics and Galactic archaeology has been driven to a large degree by the
arrival of massive digital surveys. The first surveys were mainly photometric,
such as 2MASS (Skrutskie et al., 2006), SDSS-I (York et al., 2000) and
PanSTARRS (Chambers et al., 2016). More recently, these datasets have been
complemented by unprecedented astrometric measurements from Gaia, which
provided high-quality proper motions and parallaxes (Gaia Collaboration et
al., 2021). Over the last ten years or so, an increasingly large number of
surveys such as SEGUE (Yanny et al., 2009; Rockosi et al., 2022), LAMOST (Zhao
et al., 2012), GALAH (De Silva et al., 2015), APOGEE (Majewski et al., 2017),
Gaia-ESO (Gilmore et al., 2022) and Gaia itself (Katz et al., 2023) have
provided spectroscopic data for hundreds of thousands, and in some cases
millions, of stars. These surveys have made particularly valuable
contributions to our understanding of the Galaxy and its constituent stellar
populations. The combination of radial velocities with proper motions provides
full 6-D coordinates that enable, for example, the discovery of ancient
substructure in "integral of motion" spaces (Myeong et al., 2018; Koppelman et
al., 2019; Naidu et al., 2020) and measurements of the distribution of mass in
the Milky Way (Deason et al., 2021; Bird et al., 2022; Koposov et al., 2023).
The measurement and interpretation of chemical abundances on the scale of
these surveys is a rapidly developing field that has already provided
significant insight into the formation and assembly of the Milky Way (Horta et
al., 2021; Hayden et al., 2015; Mackereth et al., 2019; Belokurov & Kravtsov,
2022). Finally, the large numbers of stars in these surveys allow for many
serendipitous discoveries, including extremely metal-poor stars (Caffau et
al., 2011; Allende Prieto et al., 2023) and hyper-velocity stars (Koposov et
al., 2020). These successes have motivated a new generation of even more
ambitious stellar spectroscopic surveys such as DESI, 4MOST, SDSS-V and WEAVE
(de Jong et al., 2019; Cooper et al., 2023; Kollmeier et al., 2017; Jin et
al., 2023).

In this contribution, we describe the first catalogue of measurements from the
stellar part of the Dark Energy Spectroscopic Instrument (DESI) Survey’s Early
Data Release (EDR; DESI Collaboration et al., 2024b). DESI is a 5020-fiber,
$3.2^{\circ}$-diameter field-of-view, moderate-resolution spectrograph
($2000<\lambda/\Delta\lambda<5000$ for 3600Å $<\lambda<$ 9800Å spread across
three arms) mounted at the prime focus of the Mayall 4 m telescope at the Kitt
Peak National Observatory (DESI Collaboration et al., 2016; Abareshi et al.,
2022). While DESI’s primary mission is to provide sub-percent cosmological
measurements of the expansion history of the universe, the instrument is also
being used for a Milky Way Survey (DESI-MWS; Cooper et al., 2023) of $\approx
7$ million stars with $r\lesssim 19$ mag, focused on the thick disc and
stellar halo of our Galaxy. DESI-MWS will also create the largest
spectroscopic catalogue to date of white dwarfs, stars within 100 pc of the
Sun, RR Lyrae and BHB stars up to distances of 100 kpc. Serendipitous
discoveries have already been made using DESI-MWS data, with the
identification of metal-poor stars and rare magnetic white dwarfs exhibiting
emission features (Allende Prieto et al., 2023; Manser et al., 2023)

Following the first light with the instrument in October 2020 and the initial
period of commissioning, DESI moved into a Survey Validation (SV) phase (DESI
Collaboration et al., 2024a). During this period (approximately Dec 2020 to
June 2021), the DESI Collaboration observed specific areas of sky (referred to
as tiles) with a variety of instrument configurations and target selection
schemes to validate observing and data reduction procedures and to optimise
the definition of the primary survey target classes (i.e., Milky Way Stars and
white dwarfs, Allende Prieto et al. 2020; Cooper et al. 2023; Bright Galaxies,
Ruiz-Macias et al. 2020; Hahn et al. 2023; Luminous Red Galaxies, Zhou et al.
2020, 2023; Emission-Line Galaxies, Raichoor et al. 2020, 2023; and QSOs,
Yèche et al. 2020; Chaussidon et al. 2023). The final definitions of all the
DESI target classes are summarised in Myers et al. (2023). The SV phase was
split into 3 campaigns: the first two took an iterative approach to optimise
the target selection and survey strategy. During the third campaign, DESI
conducted a “One-Percent Survey”, covering approximately 140 deg2 of sky
(i.e., 1 per cent of the overall DESI footprint) using the same target
selection as the main DESI survey but with much higher completeness across
different target classes. After completing the SV phase, DESI began its main
5-year survey program in May 2021.

In this paper, we describe the observation and analysis of stellar spectra
taken during the DESI commissioning and SV campaigns, supplementing and
updating the description given in Cooper et al. (2023), and provide a guide to
the Milky Way Survey Value-Added
Catalogue111https://data.desi.lbl.gov/doc/releases/edr/vac/mws/ (MWS VAC)
constructed from stellar spectra in the DESI EDR. In Section 2, we give an
overview of the target selections used in each campaign. Section 3 describes
the MWS-specific data analysis pipelines that measure stellar radial
velocities and atmospheric parameters. Section 4 presents the individual data
products provided in the MWS VAC. In Section 4.3, we describe the combined
catalogue of measurements, which we expect to be the most convenient data
product for general use. Section 5 presents several use cases that exemplify
scientific applications of the EDR data, and Section 6 describes known
limitations of the MWS VAC.

##  2 Dataset

The DESI EDR comprises observations obtained during commissioning and survey
validation, which began in December 2020 and ended in June 2021. As described
in DESI Collaboration et al. (2024b), the data are organised by survey and
program. Table 1 provides the list of survey and programme combinations with
the number of stars in the MWS VAC for each combination.

The surveys, CMX, SV1, SV2 and SV3, correspond to commissioning (CMX) and
several stages of survey validation (SV1, SV2 and SV3), respectively. The SV3
data are the closest in target selection to those in the main DESI survey.

The programs, such as ‘dark’, ‘bright’ and ‘backup’, correspond to the
observational conditions. During a given survey, DESI conducts observations in
different programs based on a metric referred to as survey speed (Schlafly et
al., 2023). The SV1 program called ‘other’ is a special category comprising
observations dedicated to specific validation tasks. The main MWS survey is
carried out exclusively in the ‘bright’ program. However, during survey
validation, a large number of targets in specific fields and special classes
of stellar targets outside the main MWS selection function were observed in
‘dark’ and ‘other’ programs.

The target selection evolved as DESI progressed from commissioning and survey
validation up to the start of the main survey. Additionally, new target
classes and customised target selection criteria were introduced for specific
fields during the SV programs. In the following sections, we briefly summarise
different target classes relevant to Milky Way science.

Table 1: Different DESI survey and program combinations that are in the EDR data release, together with the number of objects included in the MWS VAC. Survey | Program | Number of stars
---|---|---
CMX | other | 728
SPECIAL | dark | 2457
SV1 | backup | 60,959
SV1 | bright | 45,738
SV1 | dark | 41,984
SV1 | other | 82,247
SV2 | backup | 2953
SV2 | bright | 7217
SV2 | dark | 3956
SV3 | backup | 83,976
SV3 | bright | 236,448
SV3 | dark | 57,025
Total |  | 625,688

###  2.1 Targets

Details of the DESI target selection and different target classes are given in
Myers et al. (2023); DESI Collaboration et al. (2024a, b); Cooper et al.
(2023). Here, we provide a summary relevant to Milky Way science and necessary
for the interpretation of the value-added catalogue.

During commissioning, targeting information was not carefully tracked. Since
the total number of spectra from commissioning included in the DESI EDR is
very small, we do not discuss them here. After commissioning, each of the SV1,
SV2 and SV3 surveys used distinct target selection strategies in which new
target classes were introduced, and some selections were modified or renamed.

The majority of stellar spectra in the EDR were observed as MWS targets, flux
calibration standards, or one of several classes of ‘secondary’ targets.
Secondary program targets include those in several specific fields (for
example, the M31 observations described by Dey et al. 2023) and spare fibre
programs used in multiple SV fields.

In the MWS VAC, as in other DESI data products, information about why a given
source was targeted is encoded in the integer columns (bitmasks) DESI_TARGET,
MWS_TARGET and SCND_TARGET. The DESI_TARGET bitmask provides high-level
separation between the different components of the DESI survey, distinguishing
between targets associated with the primary extra-galactic and stellar
programs, the secondary programs, standard stars, and sky fibres; MWS_TARGET
is a detailed bitmask for the Milky Way Survey, separating different classes
of Milky Way target; likewise, SCND_TARGET bitmask separates different classes
of secondary targets (both stellar and extra-galactic). In the SV programs,
many secondary classes correspond to subsets of the main MWS survey target
selection that were given higher priority to increase the number of
representative spectra for particular types of stars.

Since the SV1, SV2 and SV3 surveys used different target selections, the MWS
VAC has three distinctly named target bitmasks for each survey; for example,
SV1_DESI_TARGET, SV1_MWS_TARGET and SV1_SCND_TARGET are the bitmasks for the
SV1 survey. Table 2 specifically lists the bitmask names for SV1, SV2 and SV3.
Stars observed in a given SV program should be selected using the
corresponding set of target bitmasks.

Targeting column names
---
SV1_DESI_TARGET
SV1_MWS_TARGET
SV1_SCND_TARGET
SV2_DESI_TARGET
SV2_MWS_TARGET
SV2_SCND_TARGET
SV3_DESI_TARGET
SV3_MWS_TARGET
SV3_SCND_TARGET
Table 2: The name of the columns that contain selection bit masks for SV1, SV2
and SV3 surveys.

###  2.2 High-level DESI target types

The DESI_TARGET bitmask encodes the overall DESI survey component for which
each source was observed and also separates science targets from flux
calibration standard stars and sky fibres. Table 3 lists the subset of
DESI_TARGET bits corresponding to standard stars, MWS targets and secondary
targets. These bits have the same meaning for SV1, SV2 and SV3, although the
actual selections did change somewhat222The magnitude range for STD_BRIGHT was
changed from $15\leq G<18$ in SV1 to $16\leq G<18$ in SV2 and SV3. The colour
criteria and the magnitude range of STD_FAINT were the same in SV1, SV2 and
SV3.. The DESI_TARGET bitmask for a given SV stage is nonzero only for stars
observed in that stage. For example, only stars observed in SV1 have a non-
zero value of SV1_DESI_TARGET333The VAC also has a small number (759) of rows
with zero SV1_DESI_TARGET, SV2_DESI_TARGET and SV3_DESI_TARGET, but non-zero
DESI_TARGET. These are fibres targeted as sky but having significant flux in
them..

Table 3: DESI_TARGET bits for target classes relevant for MWS (the bits are identical for SV1, SV2 and SV3) Bit name | Bit index | Description
---|---|---
STD_FAINT | 33 | Standard stars for dark/grey conditions
STD_WD | 34 | White Dwarf standard stars
STD_BRIGHT | 35 | Standard stars for bright conditions
MWS_ANY | 61 | Any MWS bit is set
SCND_ANY | 62 | Any secondary bit is set
Table 4: Milky Way Survey targeting bits for the SV1. Bit name | Bit index | Description
---|---|---
MWS_MAIN_BROAD | 0 | MWS magnitude-limited bulk sample
MWS_WD | 1 | MWS White Dwarf target
MWS_NEARBY | 2 | MWS volume-complete ($\sim$100 pc) sample
MWS_MAIN_BROAD_NORTH | 4 | MWS targets from Bok/Mosaic
MWS_MAIN_BROAD_SOUTH | 5 | MWS targets from DECam
MWS_BHB | 6 | MWS Blue Horizontal Branch targets
MWS_MAIN_FAINT | 14 | MWS magnitude-limited sample
MWS_MAIN_FAINT_NORTH | 15 | MWS magnitude-limited sample from Bok/Mosaic
MWS_MAIN_FAINT_SOUTH | 16 | MWS magnitude-limited sample from DECam
GAIA_STD_FAINT | 33 |  Gaia-based standard stars for dark/grey conditions
GAIA_STD_WD | 34 |  Gaia-based White Dwarf stars for use as standards
GAIA_STD_BRIGHT | 35 |  Gaia-based standard stars for bright conditions
BACKUP_GIANT_LOP | 58 |  Gaia-based possible giant stars for backup survey
BACKUP_GIANT | 59 |  Gaia-based selection of giant stars for backup survey
BACKUP_BRIGHT | 60 | Bright backup Gaia targets
BACKUP_FAINT | 61 | Fainter backup Gaia targets
BACKUP_VERY_FAINT | 62 | Even fainter backup Gaia targets

###  2.3 MWS survey targets

The criteria used to select stars for the Milky Way Survey (i.e. those with
the MWS_ANY bit set in the DESI_TARGET bitmask) were different in SV1 and SV2.
The SV3 observations used the same target classes and selections as SV2. The
MWS target classes defined for SV1 are given in Table 4, while the new or
modified SV2 target classes are given in Table 5. The exact criteria that
define these classes in the main DESI survey are provided in Cooper et al.
(2023); Myers et al. (2023). Note that the criteria used to define a given
class in SV may not be the same as those used for the main survey class with
the same name, although in most cases, they are very similar. To make it
easier to interpret the catalogues we also summarise the selection criteria
for the main (most numerous) MWS classes for the SV3 data in the appendix.
Briefly, these classes are as follows.

  * •

MWS_MAIN_BROAD class correspond to a magnitude limited selection in the range
$16<r<19$ without kinematic or colour cuts, supplemented by basic photometric
quality requirements and was used in SV1 only 444Several target classes (e.g.
MWS_BROAD) have NORTH and SOUTH sub-categories (e.g. MWS_BROAD_NORTH and
MWS_BROAD_SOUTH). Sources selected using Bock/Mosaic photometry in the
northern section of the DESI survey footprint have the NORTH bit set, while
those selected using DECam photometry in the southern section of the footprint
have the SOUTH bit set. Since the MWS selection criteria were identical for
the northern and southern parts of the footprint, these subclasses only
indicate the origin of the input photometry..

  * •

MWS_MAIN_FAINT corresponds to faint stellar targets with $19<r<20$ observed
during SV1 only. We note however that in the main survey this target category
is observed as a secondary target, rather than the main survey target.

  * •

MWS_MAIN_BLUE comprises stars with $g-r<0.7$. This class has no proper motion
or parallax selection and was used starting from SV2.

  * •

MWS_MAIN_RED comprises stars selected using proper motion and parallax with
$g-r>0.7$, intended to favour the selection of distant giants. This category
was used starting from SV2.

  * •

MWS_BROAD class correspond to a magnitude limited selection in the range
$16<r<19$ with $g-r>0.7$, but not satisfying astrometric cuts of MWS_MAIN_RED
and was used starting from SV2.

  * •

MWS_NEARBY selects stars in a volume-limited sample within 100 pc, using Gaia
parallaxes, and was used starting from SV1.

  * •

MWS_WD corresponds to white dwarfs and was used starting from SV1 555Different
selections were used in different SVs and will be described in the dedicated
paper

  * •

MWS_BHB comprises blue horizontal branch stars selected using the DESI Legacy
Survey photometry and was used from SV1. We note that this category, which is
described in (Cooper et al., 2023), is distinct from the BHB secondary target
category defined for observations of more distant BHB candidates in the dark
time (see below).

  * •

GAIA_STD_WD, GAIA_STD_FAINT, GAIA_STD_BRIGHT are flux calibration standards
selected based on Gaia data alone. These are used in areas without the DECaLS
photometry to select regular DESI standards. They were included starting from
SV1.

  * •

BACKUP_BRIGHT, BACKUP_FAINT, BACKUP_VERY_FAINT, BACKUP_GIANT, BACKUP_GIANT_LOP
are targets that were observed when conditions were too poor for the bright or
dark SV programs. The exact definition of the backup survey and its target
classes will be described in a follow-up paper. Backup targets were included
starting from SV1.

For all the target classes listed above in Tables 6, 7, 8 we provide the
number of objects in these classes observed in different surveys and
programmes included in this VAC. Note that the same object may have more than
one target class and may have been observed in multiple surveys.

In this paper we do not describe the selection function of the survey in
detail, partially because this VAC is based on the early data release which is
a combination of commissioning and science verification surveys and the target
selection was changing throughout. However, for each survey and programme the
information needed to reproduce the selection function or replay the target
selection is available as part of the DESI EDR. Specifically:

  * •

The details of the target selection for different classes are given in Myers
et al. (2023)

  * •

The code used for target selection in SV1, SV2, SV3 and main survey is
publicly available https://desitarget.readthedocs.io/en/latest/ and is based
on public Legacy Survey photometry data.

  * •

The list of all potential survey targets selected by the DESI targeting code
is available as part of the DESI EDR. I.e. the list of all the potential
targets of different classes for SV3 is available here
https://data.desi.lbl.gov/public/edr/target/catalogs/dr9/0.57.0/targets/sv3/resolve/.
These are all the objects that DESI considered targeting, before the fiber
assignment on individual DESI tiles was run.

To give an idea of how this can be used, Table 9 provides the fraction of
objects of different MWS target types in the SV3 footprint that ended up being
observed in SV3 in bright programme and are in this VAC. These numbers were
obtained by taking the ratio of number of objects in different target
categories in the VAC divided by the number of objects in the same target
class in the SV3 footprint in the input catalogues. The reason why those
fractions are not 100% is primarily because of the fiber allocation, as the
number of potential targets can be larger than the number of fibers and
because there is competition between fiber allocation to sky, standard stars,
BGS program targets and MWS targets. The table shows that for the main MWS
classes in SV3 survey the completeness of observations was above 70%.

Table 5: Additional and updated names and targeting bits for the Milky Way Survey used during SV2. Bit name | Bit index | Description
---|---|---
MWS_BROAD | 0 | MWS magnitude-limited bulk sample
MWS_BROAD_NORTH | 4 | MWS cuts in Bok/Mosaic imaging footprint
MWS_BROAD_SOUTH | 5 | MWS cuts in DECam imaging footprint
MWS_MAIN_BLUE | 8 | MWS magnitude-limited blue sample
MWS_MAIN_BLUE_NORTH | 9 | MWS magnitude-limited blue sample in Bok/Mosaic imaging footprint
MWS_MAIN_BLUE_SOUTH | 10 | MWS magnitude-limited blue sample in DECam imaging footprint
MWS_MAIN_RED | 11 | MWS magnitude-limited red sample
MWS_MAIN_RED_NORTH | 12 | MWS magnitude-limited red sample in Bok/Mosaic imaging footprint
MWS_MAIN_RED_SOUTH | 13 | MWS magnitude-limited red sample in DECam imaging footprint
Table 6: The number of objects in different MWS target categories observed in SV1 and included in the VAC. Target class | Number
---|---
Program: dark
MWS_MAIN_BROAD | 15638
MWS_WD | 652
MWS_NEARBY | 71
MWS_BHB | 22
MWS_MAIN_FAINT | 3461
GAIA_STD_FAINT | 4
GAIA_STD_WD | 6
BACKUP_BRIGHT | 1
BACKUP_FAINT | 18
BACKUP_VERY_FAINT | 14
Program: bright
MWS_MAIN_BROAD | 32507
MWS_WD | 24
MWS_NEARBY | 112
MWS_BHB | 3
MWS_MAIN_FAINT | 4893
GAIA_STD_FAINT | 24
GAIA_STD_BRIGHT | 170
BACKUP_BRIGHT | 490
BACKUP_FAINT | 123
BACKUP_VERY_FAINT | 8
Program: other
MWS_MAIN_BROAD | 44504
MWS_WD | 272
MWS_NEARBY | 158
MWS_BHB | 116
MWS_MAIN_FAINT | 8028
GAIA_STD_FAINT | 657
GAIA_STD_WD | 22
GAIA_STD_BRIGHT | 1540
BACKUP_BRIGHT | 1045
BACKUP_FAINT | 2868
BACKUP_VERY_FAINT | 159
Program: backup
MWS_MAIN_BROAD | 59456
MWS_WD | 494
MWS_NEARBY | 180
MWS_BHB | 48
MWS_MAIN_FAINT | 289
GAIA_STD_FAINT | 133
GAIA_STD_WD | 34
GAIA_STD_BRIGHT | 131
BACKUP_FAINT | 1136
Table 7: The number of objects in different MWS target categories observed in
SV2 and included in the VAC. Program: dark
---
MWS_BROAD | 2
MWS_WD | 270
MWS_NEARBY | 30
MWS_MAIN_BLUE | 3315
MWS_MAIN_RED | 12
Program: bright
MWS_BROAD | 1381
MWS_WD | 255
MWS_NEARBY | 53
MWS_BHB | 7
MWS_MAIN_BLUE | 4727
MWS_MAIN_RED | 724
Program: backup
GAIA_STD_FAINT | 329
GAIA_STD_BRIGHT | 217
BACKUP_BRIGHT | 1261
BACKUP_FAINT | 1114
BACKUP_VERY_FAINT | 578
Table 8: The number of objects in different MWS target categories observed in SV3 and included in the VAC. Target class | Number
---|---
Program: dark
MWS_BROAD | 10
MWS_WD | 662
MWS_NEARBY | 40
MWS_BHB | 321
MWS_MAIN_BLUE | 10928
MWS_MAIN_RED | 26
Program: bright
MWS_BROAD | 87772
MWS_WD | 840
MWS_NEARBY | 355
MWS_BHB | 439
MWS_MAIN_BLUE | 99448
MWS_MAIN_RED | 25330
Program: backup
GAIA_STD_FAINT | 19476
GAIA_STD_BRIGHT | 14633
BACKUP_FAINT | 56663
BACKUP_VERY_FAINT | 27185
Table 9: Fraction of the targets in the input catalogue within the SV3/bright footprint that were observed in SV3 in bright program and have a measurement in the EDR VAC. Target class | Fraction
---|---
MWS_BROAD | 0.77
MWS_WD | 0.95
MWS_NEARBY | 0.87
MWS_BHB | 0.84
MWS_MAIN_BLUE | 0.83
MWS_MAIN_RED | 0.83

###  2.4 Secondary targets

Alongside the main survey target classes, a variety of sources were observed
as secondary targets. Some of these were defined to fill spare fibres, while
others correspond to targets of particular interest on specific tiles observed
during SV.

Table 10 lists the different secondary stellar target categories and the
corresponding SCND_TARGET bit values. The table also gives the number of
objects of each type included in the VAC. Note that many of these object
categories may have been observed as regular survey targets. We briefly
summarise these secondary classes here; more detailed descriptions are given
in Myers et al. (2023); DESI Collaboration et al. (2024a); Cooper et al.
(2023); Dey et al. (2023).

  * •

WD_BINARIES_DARK, and WD_BINARIES_BRIGHT select white dwarf binary candidates
using Gaia and GALEX photometry to assemble an unbiased, homogeneous sample
for studying the evolution of close white dwarf binaries.

  * •

M31_QSO, M31_STAR and M31_KNOWN are targets selected for the dedicated M31
observations described in detail by Dey et al. (2023).

  * •

MWS_DDOGIANTS was originally intended for selecting highly probable halo
giants using DDO photometry; however, this bit was not used for any DESI
observations.

  * •

BHB targets were selected as faint candidate horizontal branch stars,
potentially up to distances of 150 kpc from the Galactic Centre. This class
has a fainter magnitude limit than the regular survey MWS_BHB targets (see
above) and was observed in dark conditions only. The targets were selected
using Legacy survey $g-r$,$r-z$ colours selection analogous to Eqn. 6 in Li et
al. (2019) and include faint RR Lyrae from Gaia DR2 (Clementini et al., 2019)
and Sesar et al. (2017).

  * •

FAINT_HPM, BRIGHT_HPM and HPM_SOUM targets were selected to identify fast-
moving stars in the Milky Way based on parallax and proper motion. The goal of
this selection was to identify rare populations of hypervelocity stars and
SNIa remnants.

  * •

MWS_CLUS_GAL_DEEP is a target bit for a secondary program observing Milky Way
star clusters and dwarf galaxies. During SV, dedicated observations were taken
of the Draco and Ursa Major II dwarf spheroidal galaxies, the open cluster M44
(Beehive/Praesepe) and the globular cluster pair M53 and NGC 5053. Different
combinations of colour and magnitude from Legacy Survey DR9 and parallax and
proper motion from Gaia EDR3 were used in each case to select candidate
members of these objects. These targets were only included in dedicated tiles
observed in dark time and were given higher priority than all other MWS
targets on those tiles, except white dwarfs. Note that additional tiles in the
same areas were also observed under regular bright-time conditions (see
MWS_MAIN_CLUSTER_SV below).

  * •

MWS_MAIN_CLUSTER_SV has a similar purpose to the MWS_MAIN_CLUS_GAL_DEEP class.
It was used in a series of dedicated fields observed in SV1 to prioritise
likely member stars of clusters and dwarf galaxies. These targets are, on
average, brighter than those selected in MWS_MAIN_CLUS_GAL_DEEP, and the
selection was based on Gaia DR2. They were observed under both bright and dark
conditions. In addition to the fields listed for MWS_MAIN_CLUS_GAL_DEEP, this
class was used to target stars in the globular clusters M5, M13, M92 and NGC
2419. A handful of these targets also appear in the outskirts of one SV3 field
near M13.

  * •

MWS_RRLYR666Note that the main survey uses a different bit and different name
for the RR Lyrae selection (bit 37 of the secondary targeting mask with the
name MWS_RR_LYRAE)  are RR Lyrae variables that were selected from the Gaia
DR2 RRL catalogue (Clementini et al., 2019). Note that there is some overlap
with the RRL targeted with the BHB class. In the EDR, 42 stars out of 332
MWS_RRLYR targets are also BHB targets.

  * •

MWS_CALIB and BACKUP_CALIB are classes used during SV only to prioritise stars
with data from one or more previous surveys, including APOGEE (Abdurro’uf et
al., 2022), BOSS (Dawson et al., 2013), GALAH (De Silva et al., 2015) and
Gaia-ESO (Gilmore et al., 2022). The MWS_CALIB stars have Gaia $G$-band
magnitudes $16<G<19$ while the BACKUP_CALIB stars have $G<16$.

Table 10: Secondary targeting bits relevant to MWS and the number of objects with those bits in the catalogue. Detailed descriptions of these target classes are given in the text. Bit-name | Bit index | Number of objects
---|---|---
M31_KNOWN | 6 | 25
M31_QSO | 7 | 15
M31_STAR | 8 | 827
MWS_DDOGIANTS | 9 | 0
MWS_CLUS_GAL_DEEP | 10 | 4405
FAINT_HPM | 12 | 371
BHB | 18 | 249
HPM_SOUM | 27 | 31
MWS_CALIB | 36 | 2515
BACKUP_CALIB | 37 | 2540
MWS_MAIN_CLUSTER_SV | 38 | 4669
MWS_RRLYR | 39 | 332
BRIGHT_HPM | 40 | 5
WD_BINARIES_BRIGHT | 41 | 71
WD_BINARIES_DARK | 42 | 88

###  2.5 Dedicated fields

The DESI SV program observations used either a generic target selection
(preliminary versions of the target selection for the main survey) or field-
specific selections when observing various objects of particular value to
validate the DESI data reduction pipelines.

In Table 11, we provide a list of the SV fields that are of most interest for
the MWS. These include the fields designed for tests of the MWS strategy and
pipelines listed in Cooper et al. (2023) and several other fields of interest,
such as a field centred on M31 (Dey et al., 2023) and additional observations
of streams, clusters and dwarf galaxies. As mentioned above, the target
selection in these fields was customised by adding additional secondary target
classes with high priority for fibre assignment (see Section 2.4). Most of
these dedicated fields were observed in SV1, under the program ‘other’. Many
of these fields are centred on stellar streams and dwarf galaxies and contain
tens to hundreds of stars associated with those objects. These data will be
described in upcoming publications. The data obtained from SV fields covering
the Draco dwarf spheroidal are demonstrated in Section 5.6.

Table 11: Dedicated observations in SV1 relevant to the Milky Way Survey. From left to right, columns give the name of the object, the central coordinates of the field, the total exposure time and the DESI tile number.  Object | R.A. [∘] | Decl. [∘] | $T_{\mathrm{exp}}(s)$ | Tile number
---|---|---|---|---
NGC 2419 | 114.221 | 38.469 | 9779.6 | 80618
NGC 2419 | 114.54 | 39.38 | 2986.9 | 80721
NGC 5053/M53 | 199.1 | 18.3 | 5245.3 | 80733
NGC 5053/M53 | 198.3 | 17.5 | 7200.4 | 80863
M 5 | 229.64 | 2.58 | 10112.7 | 80734
M 13 | 250.42 | 36.96 | 7242.2 | 80735
M 44 | 130.1166 | 19.4692 | 1200.1 | 80719
M 92 | 259.28 | 43.64 | 11076.0 | 80736
GD 1 | 128.5 | 0.8 | 4225.2 | 80722
GD 1 | 132.7 | 9.93 | 655.9 | 80724
GD 1 | 132.7 | 9.93 | 2400.2 | 80648
GD 1 | 163.736 | 47.858 | 8488.8 | 80658
GD 1 | 163.74 | 47.86 | 9754.7 | 80729
GD 1 | 173.55 | 52.86 | 7153.1 | 80730
Orphan | 159.08 | 7.5 | 2576.3 | 80727
Ursa Major 2 | 132.87 | 63.73 | 4239.3 | 80726
Ursa Major 2 | 132.87 | 63.73 | 1200.1 | 80720
Sextans 1 | 154.1 | -1.375 | 1431.2 | 80614
Sextans 1 | 153.26 | -1.11 | 3600.1 | 80737
Draco 1 | 260.07 | 58.42 | 7720.7 | 80738
Draco 1 | 260.07 | 57.42 | 6100.3 | 80862
M31 | 10.17 | 41.38 | 2700.1 | 80715
M33 | 24.027 | 31.39 | 3600.1 | 80615
Blank | 98.0063 | -4.8356 777The field was designed to point at the Rosette Nebula, but was mistakenly centred at a negative declination of $\delta=-4.8$ instead of $\delta=4.8$. | 1800.3 | 80718
Blank | 192.86 | 27.13 | 6663.5 | 80731
Blank | 198.04 | 0.0 | 5914.9 | 80732

##  3 MWS Processing

###  3.1 DESI data

The MWS VAC described in this paper is based on the processing of coadded
spectra of DESI targets grouped by sky area (HEALPix pixels with nside = 64;
Górski et al., 2005), survey and program. This VAC does not include the
processing of spectra from individual exposures or spectra coadded across
surveys and programs. Importantly, within a given survey and program, if an
object was observed multiple times on different nights or within a different
pointing, all the spectra of that object were coadded. This means that, even
within one HEALPix pixel, it is possible to have targets with very different
total exposure times and numbers of exposures. The details of the coaddition
process are provided in Section 4.1.1 of Guy et al. (2023).

The processing steps described below are all executed after the data reduction
and flux calibration by the main DESI pipeline (Guy et al., 2023), as well as
spectral classification and redshift determination by Redrock (Bailey et al.
in prep).

###  3.2 Stellar subset

The MWS pipelines are run on a subset of the targets observed in DESI to avoid
fitting stellar models to spectra of quasars or galaxies, which are much more
numerous among DESI targets. This VAC release is based on the subset of DESI
spectra that satisfy at least one of the following criteria:

  * •

The object was targeted as an MWS object (i.e. has the MWS_ANY bit set in the
DESI_TARGET ).

  * •

The object was targeted as a secondary target (i.e. has the SCND_ANY bit set
in DESI_TARGET).

  * •

The object was targeted as a standard star (i.e. it has one of the STD_ bits
set in DESI_TARGET ).

  * •

The object was classified by Redrock as a star.

  * •

The object has a Redrock redshift between $-1500\,\mathrm{km\,s^{-1}}$ and
$1500\,\mathrm{km\,s^{-1}}$ (irrespective of spectral type)888This selection
is expected to bring significant number of non-stellar objects, but ensures
that we do not miss possible stars with high velocities..

These criteria likely include the vast majority of stars observed by DESI,
only potentially missing stars targeted through one of the cosmological DESI
surveys and incorrectly classified by RedRock as something other than a star.
We also require that the object’s spectrum has a median (across all the
pixels) signal-to-noise ratio per pixel of at least two in one of the B, R and
Z arms and that the object has a good quality spectrum according to the
pipeline flag COADD_FIBERSTATUS=0.

###  3.3 RVS pipeline

The first stellar pipeline that DESI MWS runs is the RVSpecFit
code999https://github.com/segasai/rvspecfit, or RVS, which performs the
simultaneous least-squares modelling of the blue, red and infrared parts of
the DESI spectra by interpolating templates from PHOENIX spectral library. The
code is based on the approach presented in Koposov et al. (2011); Li et al.
(2019) and is described in more detail in Cooper et al. (2023). RVS provides
maximum likelihood estimates of stellar parameters surface gravity $\log\,g$,
effective temperature $T_{\mathrm{eff}}$, metallicity $\mathrm{\,[Fe/H]}$,
alpha abundance $\mathrm{\,[\alpha/Fe]}$, radial velocity and stellar rotation
velocity ($v\sin i$). The uncertainties for the stellar parameters come from
the Hessian matrix of the log-likelihood function, and the radial velocity
uncertainties are determined from its 1-D posterior. The RVS modelling only
relies on DESI spectral information; no priors from photometry or Gaia
astrometry are used.

###  3.4 SP pipeline

The second stellar pipeline for MWS is the SP101010An abbreviation of Stellar
Parameters. pipeline, which uses the FERRE
code111111http://github.com/callendeprieto/ferre; see also
http://github.com/callendeprieto/piferre for DESI-specific supporting code to
process the same set of stars as RVS. SP works with spectra that are shifted
to zero velocity, according to the velocity measurement obtained from RVS, and
are continuum-normalised by dividing the observed spectra by a running mean
filter with a width of 500 pixels.

The SP processing consists of two steps. In the first step, the code minimises
the $\chi$-square between the model and observations in the entire DESI
spectral range to find the main atmospheric parameters: $T_{\mathrm{eff}}$,
$\log\,g$, microturbulence $\xi$, metallicity $\mathrm{\,[Fe/H]}$, and alpha-
element abundance $\mathrm{\,[\alpha/Fe]}$ 121212For white dwarfs, only
$T_{\mathrm{eff}}$ and $\log\,g$ are measured. In the second step, the code
holds the atmospheric parameters fixed and infers individual elemental
abundances for C, Mg, Ca, and Fe131313When the abundances of C and Fe are
determined, the [Fe/H] of the model is allowed to change. When the abundances
of Mg and Ca are determined, the $\mathrm{\,[\alpha/Fe]}$ of the model is
allowed to change. by evaluating the $\chi$-square in regions dominated by
atomic and molecular transitions associated with each element. This
methodology is similar to that used in the APOGEE survey García Pérez et al.
(2016). FERRE was first used in Allende Prieto et al. (2006) and more details
are given in Cooper et al. (2023).

##  4 Data products

Here we describe the data products included in this publicly available
VAC141414https://data.desi.lbl.gov/doc/releases/edr/vac/mws/. These include:

  * •

The outputs of the RVS pipeline fitted to coadded spectra grouped by HEALPix
pixels on the sky, survey and program (3944 individual pixel, survey, program
combinations).

  * •

The outputs of the SP pipeline fitted to the same data.

For ease of use, we also combine the tables produced by RVS and SP pipeline
across HEALPix pixels, making 12 distinct SP and RVS catalogues corresponding
to different survey and program combinations listed in Table 1. Finally, we
also provide the main combined catalogue, which includes RVS and SP
measurements for all stars observed across all different surveys and programs.
This is the data product that will be of most interest to users.

We add that the original spectra that were used for the fits with all the
associated information are available as part of the main DESI EDR data
release.

The data model of all the products in this release is available at
https://desi-mws-edr-datamodel.readthedocs.io/. The following is a brief
overview.

###  4.1 RVS data products

We start by describing the individual RVS outputs of fitting results on
coadded DESI spectra. The output of RVS (for a given survey, program and
HEALPix pixel) consists of two files: an rvtab file that tabulates
measurements by RVSpecFit and a rvmod file that contains the best-fit model
for each spectrum.

The rvtab file is a FITS table with several extensions: RVTAB, FIBERMAP,
SCORES, EXP_FIBERMAP. The RVTAB extension contains the main measurements made
by RVSpecFit and basic quantities that identify the source. All other
extensions are copied directly from DESI data products described in Guy et al.
(2023)151515The FIBERMAP extension contains the targeting information,
including DESI Legacy Imaging Survey fluxes, Gaia DR2 parameters and targeting
bitmasks. The SCORES extension contains quality statistics for the spectra..
The full list of columns in the RVTAB extension is given in Table 13. The data
in these columns are as follows:

  * •

VRAD, VRAD_ERR, VRAD_SKEW, VRAD_KURT contain radial velocity measurements in
the heliocentric frame, together with the estimate of the uncertainty,
skewness and kurtosis of the radial velocity posterior.

  * •

VSINI the stellar rotation velocity $v\sin i$. In this release, the rotation
velocity measurements have been affected by a software bug and should not be
used.

  * •

LOGG, TEFF, ALPHAFE, FEH, LOGG_ERR, TEFF_ERR, ALPHAFE_ERR, FEH_ERR are stellar
parameters (surface gravity, effective temperature, $\mathrm{\,[\alpha/Fe]}$,
$\mathrm{\,[Fe/H]}$) and their uncertainty estimates from Hessian matrices of
the likelihood function.

  * •

NEXP is a deprecated column for the number of individual spectra used in the
fit.

  * •

CHISQ_B, CHISQ_R, CHISQ_Z, CHISQ_TOT are the $\chi^{2}$ of the best-fit models
in the blue, red, infrared arms and combined across all the arms,
respectively. Note that these $\chi^{2}$ are not divided by the number of
degrees of freedom.

  * •

CHISQ_C_B, CHISQ_C_R, CHISQ_C_Z, CHISQ_C_TOT  are the $\chi^{2}$ of the best-
fit continuum-only model, i.e. model using a smooth featureless stellar
spectrum template.

  * •

RVS_WARN is an integer bitmask set to zero if there are no warnings during the
RVS fit. The first bit (or a bit number zero) of the bitmask is set if the
CHISQ_TOT \- CHISQ_C_TOT $<$ 50, which means that the stellar fit provides
only a small improvement over the continuum-only fit. The second bit of
RVS_WARN is set to one if the best-fit velocity is within 5
$\mathrm{\,km\,s^{-1}}$ of the radial velocity grid edges ($\pm$1500
$\mathrm{\,km\,s^{-1}}$); the third bit is set to one if the radial velocity
error value is larger than 100 $\mathrm{\,km\,s^{-1}}$. The remaining bits of
the bitmask are unused in this VAC.

  * •

REF_CAT and REF_ID are the external catalogue name and the source identifier
if a catalogue other than the DESI Legacy Imaging Survey was used for
targeting. REF_CAT can be T2 for Tycho-2, G2 for Gaia DR2, L3 for the Siena
Galaxy Atlas (SGA) 2020 (Moustakas et al.,
2023)161616https://www.legacysurvey.org/sga/sga2020/, or F1 (for sources
prepared for DESI first light observations). For most Gaia sources, the REF_ID
is the Gaia DR2 source_id and REF_CAT is G2171717We do not recommend using the
REF_ID from this table, but instead recommend using the cross-match with Gaia
DR3 provided in the combined main MWS catalogue (Section 4.3). This is because
REF_ID is incorrect for some secondary targets due to a software bug with the
fibre assignment code..

  * •

TARGET_RA, TARGET_DEC are the coordinates of objects.

  * •

TARGETID is the unique DESI target identifier181818We remark that different
objects observed by DESI are guaranteed to have different TARGETID, but it is
possible for the same source to have more than one TARGETID..

  * •

SN_B, SN_R, SN_Z are the median signal-to-noise ratios in the blue, red, and
infrared arms of the spectrum of the object.

  * •

SUCCESS is a boolean flag that is set to true when the spectrum has no
warnings from RVSpecFit (i.e. RVS_WARN=0).

The rvmod FITS files contain the best-fit stellar model spectra corresponding
to the individual objects in the rvtab file. The files contain three B_MODEL,
R_MODEL, Z_MODEL extensions for the best-fit model in each wavelength arm. The
rvmod files also include B_WAVELENGTH, R_WAVELENGTH and Z_WAVELENGTH
extensions with the wavelength arrays for the different arms (copied from the
original DESI data products).

For each combination of survey and program, in addition to catalogues of
parameters measured from spectra grouped by HEALPix pixels on the sky, we also
provide a combined catalogue of all objects observed that given survey and
program. For example, rvpix-sv2-backup.fits includes one row for every target
in the ‘backup’ program of the SV2 survey. These catalogues are concatenated
rvtab files from different HEALPix pixels and thus have the same structure as
the individual rvtab tables.

###  4.2 SP data products

As for RVS, the SP output consists of two files per input spectral file with
coadded DESI spectra. One output is the sptab file, which is the table of
measurements from FERRE, and the other is the spmod file, which contains the
best-fit model for each spectrum. The spmod files, unlike the RVS outputs,
provide continuum-normalised observed spectra, along with their uncertainties,
the continuum-normalised best-fit models, and the model absolute fluxes.

The sptab file is a FITS table with four extensions: SPTAB, FIBERMAP, SCORES,
and AUX. The SPTAB extension contains the main measurements from FERRE and
several additional quantities helpful for cross-matching with other data
products. The FIBERMAP and SCORES extensions are copied directly from the main
DESI data products – they are identical to the extensions with the same names
in the rvtab files. Table 14 gives a complete description of all the fields
included in the SPTAB extension, but we provide a summary of the most relevant
ones below:

  * •

FEH, ALPHAFE, LOG10MICRO, TEFF, and LOGG are the main atmospheric parameters:
$\mathrm{\,[Fe/H]}$, $\mathrm{\,[\alpha/Fe]}$, micro-turbulence, effective
temperature and surface gravity. These values are also stored, in this order,
in the array PARAM (see below), and their covariance matrix is provided in
COVAR.

  * •

ELEM and ELEM_ERR are arrays with the Fe, Ca, C and Mg abundances and their
estimated uncertainties, respectively. See Section 6 for caveats on the Ca, C
and Mg abundances.

  * •

CHISQ_TOT is the reduced $\chi^{2}$ of the best-fit model for the entire
(continuum-normalised) spectrum.

  * •

REF_CAT and REF_ID are the source catalogue names and the corresponding
identifiers in those catalogues used for targeting – the same as in the RVTAB
extension.

  * •

TARGET_RA, TARGET_DEC are the coordinates of the object.

  * •

TARGETID is the DESI target identifier.

  * •

SNR_MED gives the median signal-to-noise of the spectrum across B, R and Z
arms.

  * •

PARAM is the vector of atmospheric parameters ($\mathrm{\,[Fe/H]}$,
$\mathrm{\,[\alpha/Fe]}$, $\log_{10}$ of microturbulence velocity,
$T_{\mathrm{eff}}$, $\log\,g$).

  * •

COVAR Covariance matrix for the parameter vector given in PARAM column.

  * •

SRCFILE is the file name with the DESI spectrum that was fitted.

  * •

BESTGRID is the stellar model grid that provided the best fit to the spectrum.
Possible values are ’m_rdesi1’ to ’m_rdesi5’ for Kurucz models, ’m_rdesi6’ to
’m_rdesi9’ for Koester white-dwarf models, or ’s_rdesi1’ for PHOENIX models.

  * •

RV_ADOP, RV_ERR are the radial velocity and its uncertainty (adopted from the
RVS fits) when shifting the spectrum to the rest frame.

The AUX extension of the sptab files includes two string arrays: the p array
gives the names of the parameters in the order included in the PARAM array and
the COVAR matrix, and the e array gives the names of the chemical elements
included in the ELEM and ELEM_ERR arrays.

The model spmod files are FITS files with nine extensions: B_WAVELENGTH,
R_WAVELENGTH and Z_WAVELENGTH, which store the wavelengths arrays, the
B_MODEL, R_MODEL, Z_MODEL, which hold the model spectra and related
quantities, the FIBERMAP and SCORES extensions, already mentioned in the
description of the sptab files, and a FILTER extension, which contains the
concatenated arrays of weights for the B, R and Z channels used for
determining elemental abundances.

The model extensions (B_MODEL, R_MODEL, and Z_MODEL) contain multiple columns:

  * •

The OBS column in the tables gives the continuum-normalised observed spectrum
resampled to rest-frame.

  * •

The ERR column stores the uncertainties on the continuum normalised resampled
spectrum.

  * •

The FIT column contains the best fit continuum-normalised stellar model
obtained from the first step described in §3.4, corresponding to the
determination of the stellar parameters.

  * •

The ABU column is similar to the FIT column, but for the fitting obtained in
the second step (abundances determination). There is only one ABU array but
multiple elements, so the data are overwritten in the order given in the ELEM
array, chosen to ensure that values corresponding to elements with fewer
spectral lines are written last. The FLX column stores the un-normalised
absolute flux model corresponding to the best-fit parameters of the star.

Similar to the RVS outputs, we provide combined catalogues of objects observed
in a given survey and program, for example sppix-sv2-dark.fits. These are
constructed by concatenating sptab files and thus have a similar structure as
the individual sptab files, but without the AUX extension.

###  4.3 Main combined catalogue

In addition to the catalogues of measurements for targets grouped by DESI
survey/program and HEALPix pixel, we also provide a combined catalogue of
measurements from RVS and SP pipelines, including all the surveys and programs
that are part of the DESI EDR. This main catalogue is a FITS table named
mwsall-pix-fuji.fits. The main catalogue also includes additional Gaia DR3
cross-match information. We expect the main combined catalogue to be the most
frequently used data product in the DESI MWS VAC. The catalogue contains
625,588 rows.

The combined table contains the following extensions: RVTAB, SPTAB, FIBERMAP,
SCORES, GAIA. The RVTAB extension includes all the columns listed in Section
4.1, as well as a few additional columns:

  * •

PROGRAM identifies the program from which the star came from (i.e. dark,
bright, backup or other).

  * •

SURVEY identifies the survey from which the star came from (i.e. SV1, SV2, SV3
or CMX).

  * •

HEALPIX is the HEALPix pixel (at nside = 64) of the star.

  * •

PRIMARY is the flag that is set to true if this specific measurement was
labelled as primary (because the same star could have been observed in
different surveys and programs). The observation with the largest signal-to-
noise in the R arm is selected as primary.

  * •

RR_Z is the redshift from Redrock.

  * •

RR_SPECTYPE is the spectroscopic type of the object, such as STAR, QSO, GALAXY
as provided by Redrock.

The SPTAB extension in the catalogue contains measurements from the SP
pipeline as described in Section 4.2. The FIBERMAP and SCORES extensions store
information relevant to targeting and spectral quality, respectively (see Guy
et al., 2023). Finally, the GAIA extension in the file contains Gaia DR3
measurements for each source in the catalogue (we provide all the columns from
the gaia_source table). Gaia sources have been associated with DESI targets by
on-sky position (using a radius of one arcsecond).

##  5 Catalogue overview

In this section, we provide an overview of the contents of the MWS VAC
catalogue, demonstrate how it could be used for science, and discuss some of
its limitations.

###  5.1 Selecting clean stellar samples

Table 12: The number of objects in the value-added catalogue with different Redrock spectroscopic types, separated according to whether the RVS_WARN=0 condition is true or false. RR_SPECTYPE | RVS_WARN=0 | Number
---|---|---
STAR | True | 511,809
STAR | False | 4,454
GALAXY | True | 13,911
GALAXY | False | 61,677
QSO | True | 10,336
QSO | False | 23,401
Figure 1: The distribution of line of sight velocities for different
subsamples in the VAC. The distributions highlight alternative ways of
selecting stellar objects from the catalogue. The black line shows the
distribution of objects with RVS_WARN=0. These comprise mostly stars, with a
few per cent contamination by non-stellar sources that produce flat tails in
the distribution out to $\pm$1500$\mathrm{\,km\,s^{-1}}$. The red line shows
the distribution of objects with RVS_WARN$\neq$0\. These are almost uniformly
distributed in velocity, confirming that they are not stars. The pink curve
shows the distribution of radial velocities for those objects with RVS_WARN=0
that were targeted for the MWS survey; the green curve shows the distribution
for objects with RVS_WARN=0 that were classified as star by Redrock (i.e.
RR_SPECTYPE=STAR). The latter sample has the least contamination; these
criteria should be used to select stars in the MWS VAC unless stars with
$|V_{\text{los}}|>600$$\mathrm{\,km\,s^{-1}}$ are of interest.

Because the list of criteria used to identify potential objects for fitting by
the stellar pipeline (described in Section 3) is deliberately broad and
designed not to miss any stellar targets, the MWS catalogue contains a
significant number of sources that are not stars.

In this section, we provide several methods to identify mostly clean samples
of stars using values provided in the VAC tables. These criteria are not meant
to be exhaustive or optimal because different science cases may have different
requirements for purity and completeness.

  * •

RVS_WARN = 0 selects objects for which RVSpecFit obtained a satisfactory fit
to the DESI spectrum. This selects most stellar spectra, but this is not a
particularly pure selection because it accepts a fraction of QSOs and
galaxies.

  * •

RR_SPECTYPE = STAR selects on the Redrock classification of an object as a
star. This is a robust classification, but it is limited by the condition
imposed by Redrock that stars have radial velocities between -600 and 600
$\mathrm{\,km\,s^{-1}}$.

  * •

(DESI_TARGET & MWS_ANY)$>0$191919Where MWS_ANY=$2^{61}$ as specified in Table
3. selects objects with the MWS_ANY targeting bit set, which means that the
source was targeted for the Milky Way Survey. This removes a large number of
non-stellar objects but also retains a significant number of QSOs that fall in
the MWS MAIN-BLUE and BROAD selections.

In Table 12, we provide the number of objects in the catalogue with different
spectroscopic classifications from Redrock, depending on whether RVS_WARN = 0
is true or not. The table shows that the number of non-stellar objects in the
MWS VAC EDR catalogue is $\sim$ 20 per cent.

Figure 1 shows the distribution of radial velocities as measured by RVS and
reported in the MWS VAC. We show separately the distributions for objects with
RVS_WARN$=0$ and RVS_WARN $\neq 0$ (black and red lines respectively),
illustrating that objects with RVS_WARN $\neq 0$ do not concentrate at
$V_{\mathrm{los}}=0\mathrm{\,km\,s^{-1}}$. This indicates that objects with
RVS_WARN $\neq 0$ are either not stars or unlikely cases of a bad fit by the
RVS pipeline. The figure also shows that an additional constraint on the
spectroscopic type from Redrock (green line) or on selection as an MWS target
(pink line) results in a velocity distribution concentrated mostly within
$|V_{\mathrm{los}}|<600$$\mathrm{\,km\,s^{-1}}$, implying a cleaner stellar
selection.

For the rest of this paper, we will use the RVS_WARN=0 and RR_SPECTYPE=STAR
selection to identify stellar targets as well as the PRIMARY=True flag to
exclude duplicate observations. This leaves 421,403 individual sources out of
625,588 in the combined catalogue. Additional cuts will be noted where
relevant.

A few further selection cuts are recommended for restricting the sample to the
highest-quality measurements. These are described in Appendix B for SP and in
Appendix C for RVS.

Figure 2: The density on the sky of stellar targets in the value-added
catalogue. The density is shown in units of stars per square degree. The
dashed line shows the Galactic plane. Individual DESI pointings are easily
discernible on the map as 3-degree diameter circles.

###  5.2 Spatial and colour-magnitude distribution

Figure 3: Hess diagrams of stellar targets in the VAC observed in different
surveys and programs. The bright stars with $g-r\sim 0.4$ are those used by
the DESI survey as flux calibration standards. As the main MWS survey targets
are only observed in bright time, the main $16<r<19$ selection is only seen in
the bright SV programs (as well as in SV1/other, which was used for some
dedicated MWS tiles). In the dark program, stellar targets are observed either
as standards, secondary targets or stellar contaminants from other
cosmological target selections.  Figure 4: Spatial distribution of stars from
the DESI EDR value-added catalogue in Galactocentric coordinates $x-y$ (left)
and $x-z$ (right), in $200\times 200$ bins. The panels are colour-coded by the
numbers of stars per bin (top) and mean $\mathrm{\,[Fe/H]}$ (bottom) from the
RVS pipeline. The solid contours show iso-potentials of the barred Milky Way
model of Hunter, Sormani+ 2023 (in prep.). The distribution is based on
distances from Bailer-Jones et al. (2021) and thus shows only the nearby part
of the DESI sample.

The distribution of VAC stars on the sky is shown in Figure 2. The colour on
this map shows the density of observed targets, which varies from a few
hundred to a few thousand per square degree, depending on which surveys (SV1,
SV2 or SV3) and programs covered a particular area. The density is highest in
dedicated MWS fields (described in Section 2.5) and SV3 areas, which have high
completeness for all target classes. In areas of the sky that were not
observed by the bright SV programs, the target density is typically small.
Stars in these fields were only observed as flux standards or secondary
targets or as contaminants to other DESI target selections.

The colour-magnitude diagram of stars from all the surveys and programs
included in the VAC is shown in Figure 3. This figure highlights several
aspects of target selection discussed in Section 2. A common feature of all
these colour-magnitude distributions is the area populated by DESI flux
calibration standard stars, with $g-r\sim 0.4$ and $15\lesssim r\lesssim 19$.
These targets are included in both dark and bright observations. In the bright
program observations, the colour-magnitude distribution shows the main MWS
selection in the magnitude range $16<r<19$ and fainter extensions to $r<20$
explored during SV.

Figure 4 illustrates the distribution of stars from the VAC in Galactocentric
coordinates using photo-geometric distance estimates from Bailer-Jones et al.
(2021)202020We assume the distance to the Galactic centre $R_{0}=8.277$ kpc
(GRAVITY Collaboration et al., 2022), and the solar height with respect to the
Galactic plane $z_{0}=20.8$ pc (Bennett & Bovy, 2019).. The top two panels
show the 2-dimensional number density of stars in Galactocentric $x$ vs $y$
and $x$ vs $z$. The distribution shown in this figure is only valid within
$\sim 10$ kpc of the Sun because the distances from Bailer-Jones et al. (2021)
are not robust enough for more distant targets. The bottom row of the figure
shows the distribution of stars in the same coordinates, but now colour-coded
by mean [Fe/H] from the RVS pipeline. The solid contours show the iso-
potentials of the barred Milky Way model of Hunt, Sourmani et al. (in prep.),
built with the Agama library (see Vasiliev, 2019) – see also Sormani et al.
(2022). The figure illustrates the Galactic volume probed by the majority of
the DESI EDR stellar sample, with most stars at high galactic latitudes.
Although a significant part of the DESI EDR sample shown in the figure is
within a Galactocentric distance of $\sim 15$ kpc, a large number of stellar
targets in the VAC sample of the Milky Way stellar halo out to distances of
$\sim 100$ kpc. This is not visible in the Figure due to the distance
estimator used (better distance estimators are currently being developed). The
bottom panels of the figure also illustrate that we recover the expected
decrease in mean [Fe/H] further from the disk mid-plane ($|z|$), with
high-$|z|$ halo stars typically having $\mathrm{\,[Fe/H]}\lesssim-1$.

###  5.3 Stellar parameter distribution

Figure 5: The distribution of surface gravity and effective temperatures for
stars in the MWS VAC as determined by the RVS and SP pipelines. The parameter
range shown here is smaller than the actual range of measured parameters; the
RVS maximum temperature extends to $15,000$ K and SP to $40,000$ K. The SP
pipeline also models white dwarfs, which can have $\log\,g$ up to 9.5 and are
not shown here.

All of the stars in the MWS VAC have stellar parameter measurements from both
the SP and RVS pipelines. We provide measurements of $\log\,g$,
$T_{\mathrm{eff}}$ and $\mathrm{\,[Fe/H]}$ (as well as other parameters
described in Sections 4.1 and 4.2).

In Figure 5, we show the distributions of surface gravity and effective
temperature (Kiel diagrams) for stars in the VAC, as measured by the RVS
(left) and SP pipelines (right). The distributions are similar and show
features that are expected, such as the red giant branch (extending from
$\log\,g=4$, $T_{\mathrm{eff}}=5500$ K to $\log\,g=1$, $T_{\mathrm{eff}}=4000$
K), the blue horizontal branch ($\log\,g$ $\sim$3, $T_{\mathrm{eff}}>6,500$ K)
and the main sequence ($\log\,g$ <4, $T_{\mathrm{eff}}$ <6,000 K). The shape
of the main sequence below the turn-off, however, is not very realistic and is
likely dominated by systematic effects from the stellar atmospheric grids used
in our pipelines. Both pipelines show clustering around grid points in
$\log\,g$ and $T_{\mathrm{eff}}$. In addition, the SP pipeline also shows
problematic clustering at $T_{\mathrm{eff}}=5000$ K and $\log\,g=5$. Some of
these SP artefacts mostly disappear when using stricter selections described
in Appendix B.

In Figure 6 we show the distribution of iron abundances for all the unique
DESI stars. We display the distribution for abundances from both RVS and SP
pipelines. The metallicity distribution function (MDF) shows that the majority
of stars is in the thick disk with $\mathrm{\,[Fe/H]}\approx-0.5$, but with
about 25% of stars having metallicities below $-1$. The metallicity
distribution for metal-poor stars shows the peak at
$\mathrm{\,[Fe/H]}\approx-1.2$ which would be the GSE structure Belokurov et
al. (2018); Helmi et al. (2018). At low metallicities the MDF extends like a
power-law down to $\mathrm{\,[Fe/H]}=-3.5$ as expected for the Milky Way
stellar halo (Youakim et al., 2020). The metallicity distributions from RVS
and SP do agree, although due to issues highlighted earlier, we see some
discrepancies for very metal-rich stars, as well as accumulation at the edges
near $\mathrm{\,[Fe/H]}=-4$ for RVS and $\mathrm{\,[Fe/H]}=-3.5$ for SP.

Figure 6: The distribution of iron abundances for stars in the MWS VAC as
determined by the RVS and SP pipelines. The distribution shows the peak
associated with thick disk stars at $\mathrm{\,[Fe/H]}$ =-0.5, the peak
associated with GSE at $\mathrm{\,[Fe/H]}=-1.2$ and metal-poor stellar halo at
lower metallicities. The distributions are shown with the bin size of 0.1 dex.
Due to gridding issue associated with the RVS measurements, would the plot be
made with much smaller bins (i.e. 0.01 dex), we would see peaks at location of
template grid with the step of 0.25 dex.

###  5.4 Parameter validation with APOGEE

Figure 7: Comparison of RVS and SP parameters to APOGEE measurements is shown
as a function of APOGEE effective temperature. Each panel shows in greyscale
shows the 2D histograms of $\delta X=X_{DESI}-X_{APOGEE}$ (where X is $[Fe/H]$
for the top panel, $\log g$ for the second panel $T_{\rm eff}$ for the third
panel and $[\alpha/Fe]$ for the bottom panel) vs effective temperature. Solid
curves show the median residual with respect to APOGEE, and dashed curves show
the 16th and 84th percentiles of the distribution. Inset boxes report the
median value of the offset with respect to APOGEE for the whole sample.

Here, we compare our stellar parameter measurements with the parameters from
the APOGEE survey in more detail than Cooper et al. (2023). The APOGEE results
are of high quality (high-resolution and high signal-to-noise) and based on
$H$-band spectroscopy. Jönsson et al. (2018) has shown good agreement between
the APOGEE results and high-resolution optical studies in the literature,
although systematic differences are apparent for some elements. We
specifically take the APOGEE DR17 measurements from Abdurro’uf et al. (2022)
and cross-match them with the MWS VAC. In the APOGEE catalogue, we exclude
measurements with the STAR_BAD flag set. In the MWS VAC, we use the selection
given in Section 5.1. After these cuts, we have 507 stars in common between
APOGEE and the MWS VAC.

Figure 7 shows the residuals between RVS and SP measurements of $\log\,g$,
$T_{\mathrm{eff}}$, $\mathrm{\,[\alpha/Fe]}$ and $\mathrm{\,[Fe/H]}$ and the
corresponding measurements from APOGEE. The residuals are plotted as a
function of the effective temperature from APOGEE. We also show the median
(solid line) and the 16/84 percentiles of the residuals (dashed lines). The
annotation in each panel gives the median and 16/84 percentiles of the
residuals for the whole DESI$\times$APOGEE dataset.

The figure shows that, for both RVS and SP, the typical scatter in [Fe/H] with
respect to the high-resolution APOGEE measurements is $\sim$ 0.15-0.2 dex with
a small bias. There are no significant trends of [Fe/H] with temperature, but
the metallicities seem more robust for warmer stars with $T_{\mathrm{eff}}$
$>4800$ K. The surface gravity measurements show strong systematic trends with
temperature. At $T_{\mathrm{eff}}$ $\sim 4500$ K the RVS $\log\,g$ deviation
reaches $-0.5$, and at a similar temperature, the SP systematics in $\log\,g$
switch from positive to negative bias with respect to APOGEE. The effective
temperatures for RVS and SP show good agreement with APOGEE for cool stars but
show $\sim$ 200 K bias for hotter stars. With $\mathrm{\,[\alpha/Fe]}$ the
typical agreement with APOGEE for both RVS and SP is around 0.1 dex, although
the dynamic range of $\mathrm{\,[\alpha/Fe]}$ in the DESI/APOGEE overlap
sample is quite small ($\sim$ 0.4 dex). The $\mathrm{\,[\alpha/Fe]}$
measurements from RVS seem more biased than SP and there is a subset of stars
with large $\mathrm{\,[\alpha/Fe]}$ deviation at $T_{\mathrm{eff}}=4000$
K212121This particular feature was caused by a few problematic stellar
atmospheres in the PHOENIX grid. Those stellar atmospheres have since been
removed, and the problematic feature disappears in more recent reductions of
the DESI data.

Although Figure 7 shows systematic trends with temperature, the trends with
surface gravity are also important. For example, there is an indication that
PHOENIX-based measurements are biased for $\log\,g$ $<2$. However, since the
EDR dataset does not overlap enough with high-resolution surveys like APOGEE
to investigate systematics as a function of multiple variables, we postpone a
more detailed analysis of these systematics to future data releases.

The comparison of abundance and stellar parameter measurements in APOGEE
presented here is mostly demonstrating the systematic offsets of DESI measured
parameters as opposed to random errors. As shown in Cooper et al. (2023)
(Figure 14) the $\mathrm{\,[Fe/H]}$ uncertainty is a strong function of
magnitude and colour, reaching values of $\sim$ 0.3 dex for blue stars at the
faint $r=19$ limit.

Figure 8: The histogram of differences between DESI RVS and APOGEE radial
velocity measurements. The distribution shows a small bias, such that
velocities from DESI are $\sim 0.93$ $\mathrm{\,km\,s^{-1}}$ larger than those
from APOGEE.

####  5.4.1 Radial velocity comparison with APOGEE

In the overview paper, we analysed the overall quality of radial velocities
and radial velocity errors (Figure 11 in Cooper et al. (2023)). The analysis
of repeated observations showed that the velocity errors provided by RVSpecFit
correctly characterise the velocity uncertainties if we take into account the
systematic error floor of 0.9$\mathrm{\,km\,s^{-1}}$. We do not repeat this
analysis here because it should also apply to the data in the EDR. However, it
is worthwhile to check for any systematic velocity offset with respect to
other datasets.

In Figure 8, we compare the distribution of radial velocity differences
against APOGEE using the same sample as our investigation of the stellar
parameters above. This shows that DESI radial velocities have a small bias of
$0.93$$\mathrm{\,km\,s^{-1}}$ (i.e. DESI radial velocities are larger than
APOGEE velocities by 0.93 $\mathrm{\,km\,s^{-1}}$). We do not apply this
correction in the VAC, so users should consider subtracting
0.93$\mathrm{\,km\,s^{-1}}$ from DESI velocities to bring them to the same
frame as APOGEE.

###  5.5 Chemo-dynamic studies with DESI stars

Here, we look at the spectroscopic abundance measurements in combination with
the orbital properties of stars to demonstrate the power of data included in
the MWS VAC for chemical analyses of the Milky Way disc and stellar halo
(similar to e.g. Mackereth et al., 2019).Figure 9 shows the
$\mathrm{\,[Fe/H]}$ vs $\mathrm{\,[\alpha/Fe]}$ distributions for stars in the
VAC. The top panels show the density of stars in $\mathrm{\,[Fe/H]}$ vs.
$\mathrm{\,[\alpha/Fe]}$ space, while in the bottom panels, we colour each
pixel by the average orbital eccentricity of stars in those pixels. The left
and right panels show measurements from the RVS and SP pipelines,
respectively.

Looking at the distribution of abundances in the top left panel of the figure,
we notice very clear gridding effects in RVS parameters, with steps of 0.25
dex in $\mathrm{\,[Fe/H]}$ and 0.2 dex in $\mathrm{\,[\alpha/Fe]}$ 222222The
gridding in measured parameters is caused by the use of multi-linear
interpolation over a rectangular grid in the space of parameters. This
interpolation is continuous but does not produce continuous first derivatives,
leading to clumping at the edges of interpolation grid cells. This will be
addressed in future DESI MWS data releases.. Despite this gridding, the
overall distribution closely resembles that obtained by higher resolution
spectroscopic surveys (e.g. APOGEE – see Abdurro’uf et al., 2022), with the
region $\mathrm{[Fe/H]}<-1$ being typically populated by halo stars and the
region $\mathrm{[Fe/H]}>-1$ being typically split into an [$\alpha$/Fe]-rich
track ($\mathrm{[\alpha/Fe]}\gtrsim 0.2$) and an [$\alpha$/Fe]-poor track
($\mathrm{[\alpha/Fe]}\lesssim 0.2$). The $\mathrm{\,[\alpha/Fe]}$-poor and
$\mathrm{\,[\alpha/Fe]}$-rich sequences can be seen in the figure separated at
$\mathrm{\,[\alpha/Fe]}$ of 0.15 at $\mathrm{\,[Fe/H]}$ =-0.5. The halo stars
are seen as less dense part of the distribution at $\mathrm{\,[Fe/H]}<-1$ and
$\mathrm{\,[\alpha/Fe]}\sim 0.5$. The distribution of SP abundances (top right
panel) does not show gridding effects but resembles less closely the
halo/$\mathrm{\,[\alpha/Fe]}$-rich/$\mathrm{\,[\alpha/Fe]}$-poor distribution
of APOGEE. We also note a clustering of stars at $\mathrm{\,[Fe/H]}$ =0 and
$\mathrm{\,[\alpha/Fe]}$ $\sim 0$ that is not physical.

The bottom panels of Figure 9 show the same distribution as the top panels but
are colour-coded by the average eccentricity of orbits for these stars. To
compute orbits, we use Gaia DR3 astrometry and the photo-geometric distances
from Bailer-Jones et al. (2021) (without applying any quality cuts on the
distances). Orbits were integrated for 10 Gyr in the Milky Way potential of
McMillan (2017) with the same parameters for the position of the Sun in the
Galaxy as in Section 5.2 and assuming $U_{\odot}=9.3$ km/s,
$V_{\odot}+V_{c}(R_{0})=251.5$ km/s and $W_{\odot}=8.59$ km/s, from (Reid &
Brunthaler, 2020; GRAVITY Collaboration et al., 2022).

Overall, both the RVS and SP chemical maps show the same chemo-dynamical
correlations observed in other spectroscopic surveys in combination with Gaia:
intermediate metallicity halo stars dominated by the Gaia-Enceladus-Sausage
merger (Belokurov et al., 2018; Helmi et al., 2018) typically have
eccentricities $\gtrsim 0.8$, while the $\mathrm{\,[\alpha/Fe]}$-rich
($\mathrm{\,[\alpha/Fe]}$-poor) stars have intermediate (low) eccentricities,
in close connection to the thick and thin disc, respectively – see Figure 4.
While in this data we may also expect to see separation of the GSE from other
parts of the accreted halo Feuillet et al. (2021); Hasselquist et al. (2021),
given the current quality of $\mathrm{\,[\alpha/Fe]}$ measurements we can not
make such a separation. We also note that in our analysis did not take into
account the survey selection function which can affect Figure 9. One reason it
may play a role is because the MWS_MAIN_BLUE sample, which constitutes a
substantial fraction of the catalogue is colour selected (hence is biased
towards more metal-poor stars), but has no astrometric selection. The
complementary red MWS_MAIN_RED sample is selected using colour, but also uses
astrometry to exclude more nearby stars. This will affect significantly the
distribution shown on the Figure by down-weighting the metal-rich disk
population.

Figure 9: Chemical map ([Fe/H]-[$\alpha$/Fe]) from the RVS (left) and SP
(right) pipelines, in $150\times 150$ bins. The panels are colour-coded by the
number of stars per bin (top) and by the eccentricity (bottom)
$(r_{\mathrm{apo}}-r_{\mathrm{per}})/(r_{\mathrm{apo}}+r_{\mathrm{per}})$,
where $r_{\mathrm{apo}}$ and $r_{\mathrm{per}}$ are the apo- and pericenter
radii. Orbits were integrated for $10$ Gyr in the potential model of McMillan
(2017), using photo-geometric distances from Bailer-Jones et al. (2021).

###  5.6 Draco dwarf galaxy dedicated fields

Figure 10: The distribution of stars in the MWS VAC around Draco dSph in
$\mathrm{\,[Fe/H]}$ and line of sight velocity. Here, we use only stars with
membership probabilities based on Gaia data, computed by Pace et al. (2022).
Greyscale shows the 2-D histogram of all the stars with membership measurement
from Pace et al. (2022). The stars with high membership probability $P>0.5$
are shown in red. The total number of likely Draco members is $\sim$ 260
stars.

As mentioned in Section 2 and shown in Table 11, this VAC includes not only
observations that follow the regular DESI MWS strategy but also multiple
dedicated fields placed on stellar streams, star clusters and dwarf galaxies.
While a detailed exploration of the data from these observations is beyond the
scope of this paper, here we provide a demonstration of measurements from
fields centred on the Draco dwarf spheroidal galaxy.

Figure 10 shows the distribution of stars in the MWS VAC in [Fe/H] and radial
velocity space in the vicinity of Draco. We only plot stars with Draco
membership probability estimates from Pace et al. (2022), which were computed
based on Gaia DR3 astrometry. The total number of stars in common with the
membership catalogue is $\sim$ 950\. The greyscale in the figure shows the 2D
distribution of velocity and metallicity for these potential member stars.
There is a broad distribution from halo contaminants and a peak associated
with Draco stars near $V_{\text{los}}\sim-290\mathrm{\,km\,s^{-1}}$ and
$\mathrm{\,[Fe/H]}\sim-2$. Red points in the figure show stars with a
membership probability from Pace et al. (2022)
$P_{\mathrm{mem\\_fixed\\_complete}}>0.5$. Almost all high-probability member
stars are clustered around Draco’s radial velocity and metallicity, indicating
that they are very likely Draco members.

By looking at the metallicity distribution of Draco member stars in DESI, we
find a median metallicity of $\mathrm{\,[Fe/H]}\approx-2.25\pm 0.03$, which at
face value seems to differ significantly (by $\sim 0.3$ dex) from the median
metallicity of $-1.93\pm 0.01$ reported by Kirby et al. (2011). However, Kirby
et al. (2011) measured a significant metallicity gradient in Draco of
$\approx-0.73$ dex deg-1. The median angular distance of the member stars in
the DESI sample from the centre of Draco is $\sim$ 0.25 degrees, as opposed to
$\sim$ 0.1 degrees for the literature sample, owing to the large field of view
of DESI. This metallicity gradient can, therefore, explain at least $\sim 0.1$
dex of the discrepancy between Kirby et al. (2011) and the DESI measurement.
In fact, the median metallicity of the DESI members within 0.1 degrees is
$-1.97$ dex perfectly consistent with previous measurements.

The total number of Draco members detected in DESI is $\sim 260$, which is
comparable in size to the existing literature datasets on Draco (see e.g.
Kirby et al., 2010; Walker et al., 2015). However, our sample extends much
further in radius, highlighting the power of the DESI survey for observations
of the outskirts of dwarf galaxies.

###  5.7 Wide binaries

A final demonstration of the catalogue utilises an astrometrically selected
set of wide binaries.

Since wide binaries are expected to share almost the same chemical
compositions (Duchêne & Kraus, 2013) and have similar kinematics, these pairs
should provide an additional internal consistency check on our stellar
parameter measurements. To perform this test, we cross-match a subset of EDR
VAC stars, selected to have good-quality measurements in radial velocities and
metallicities, to the wide binary catalogue from Gaia EDR3 by El-Badry et al.
(2021). In addition to the selection criteria introduced in Section 5.1, we
impose the following requirements:

  1. 1.

CHISQ_TOT $<5$

  2. 2.

SNR_MED $>5$

  3. 3.

$\sigma(V_{\mathrm{rad}})<20\,\mathrm{km\,\,s}^{-1}$

  4. 4.

$[\mathrm{Fe/H}]_{\mathrm{RVS}}>-3.9$ dex

  5. 5.

$[\mathrm{Fe/H}]_{\mathrm{SP}}>-4.9$ dex

  6. 6.

$R_{\mathrm{chance\\_align}}<0.1$.

These criteria select $477$ high-confidence wide-binary pairs. We take the
definition of “primary" (P) and “secondary" (S) from El-Badry et al. (2021),
which designate the brighter and fainter member in each pair, respectively, in
the Gaia $G$ band. In a majority of pairs, both primary and secondary stars
are on the main sequence and are K or M dwarfs. In the MWS VAC, all of these
pairs have radial velocity and metallicity measurements from RVS, while
metallicities measured from the SP pipeline are available for $453$ pairs.

Figure 11 presents a comparison of stellar parameters from the RVS and SP
pipelines. Panel (a) shows the distribution of $\Delta
V_{\mathrm{rad}}=V_{\mathrm{rad,\,S}}-V_{\mathrm{rad,\,P}}$, the velocity
difference between secondary and primary. Panel (b) presents the distribution
of the metallicity difference,
$\Delta[\mathrm{Fe/H}]=[\mathrm{Fe/H}]_{\mathrm{S}}-[\mathrm{Fe/H}]_{\mathrm{P}}$,
while panels (c) and (d) are scatter plots of individual metallicity
measurements, colour-coded by the differences in effective temperatures, i.e.,
$\Delta T_{\mathrm{eff}}=T_{\mathrm{eff,S}}-T_{\mathrm{eff,P}}$.

The figure shows no significant difference between the radial velocity
measurements of primary and secondary stars, with a small standard deviation
of $1.84\mathrm{\,km\,s^{-1}}$ (measured from the 16th to 84th percentile
range of the distribution). Some of that dispersion can be attributed to
orbital motions and some to the individual radial velocity uncertainties,
which are $\sim 2$ km s-1 for this sample.

Panels (b)-(d) in Figure 11 show that the distributions of
$\Delta\mathrm{[Fe/H]}$ in both the RVS and SP pipelines have noticeable
scatter between the measurements for primary and secondary stars. Notably,
however, the $\Delta\mathrm{[Fe/H]}$ values measured from the RVS pipeline
have a standard deviation of $0.16$ dex. This is comparable with the spread we
obtained when comparing DESI measurements to APOGEE measurements. The
distribution of $\Delta\mathrm{[Fe/H]}$ from the SP pipeline, however, shows a
significantly wider spread with a dispersion of $0.31$ dex. A similar picture
can be seen in panels (c) and (d), where we plot the metallicity of the
primary against the metallicity of the secondary for RVS and SP measurements.
Here, larger scatter in SP metallicities is mostly caused by issues associated
with cool stars, discussed in Section 5.3 and the next section.

Figure 11: Comparison of stellar parameters from the RVS and SP pipelines
between the primary and secondary star for $477$ wide-binary pairs from El-
Badry et al. (2021) with high confidence of being physical binaries
($R_{\mathrm{chance\\_align}}<0.1$). The panels show the distribution of
differences in the radial velocities (a), the distribution of differences in
metallicity measurements (b), and scatter plots (c and d) displaying
metallicities colour-coded by temperature differences. Note that SP
metallicities are available for $453$ pairs.

##  6 Known limitations

In previous sections, we have described the contents of the MWS VAC,
demonstrated the quality of DESI measurements and provided examples of
scientific applications of the data. This section focuses on known issues and
limitations in the catalogue that users should know.

  * •

The distribution of parameters measured by RVSpecFit shows concentrations at
the edges and nodes of the underlying grid of stellar atmospheres. Since the
code relies on multi-linear interpolation of the over-sampled PHOENIX grid,
the $\mathrm{\,[Fe/H]}$ grid step of 0.25 dex leads to concentrations of
measurements at $\mathrm{\,[Fe/H]}=-4,-3.75$ and so forth. Concentration at
the grid points is more likely for bright stars. Despite this gridding, the
overall accuracy of the [Fe/H] abundances is $\sim 0.1-0.2$ dex, as
demonstrated in Section 5.4. The concentration of measurements around grid
points is also observed for $\log\,g$, $T_{\mathrm{eff}}$ and
$\mathrm{\,[\alpha/Fe]}$. Because of this, we believe that the uncertainties
on stellar parameters in the RVS catalogue are likely underestimated and not
well described by a Gaussian.

  * •

Both RVS and SP metallicities display an excess of objects at the lower limit
of their metallicity grids, $\mathrm{\,[Fe/H]}=-4$ and $-5$, respectively. At
least some of these measurements are caused by the spectra that cannot be fit
by regular spectral models (i.e. they are non-stellar or otherwise unusual).

  * •

The SP parameter distributions exhibit artificial concentrations at the
boundary of the PHOENIX grid used for cool stars (see Figure 9). For this
reason, we recommend avoiding using the SP parameters for stars analysed with
that grid, as described in Appendix B.

  * •

The [$\alpha$/Fe] abundances for dwarfs cooler than about 4000 K as measured
by SP show concentration at values in the range
$-0.1<\mathrm{\,[\alpha/Fe]}<0.0$ and $-0.6<\mathrm{\,[Fe/H]}<-0.3$. We
believe this is an artefact and discourage using the SP results for those
stars, as described in Appendix B.

  * •

The carbon and magnesium abundances reported by the SP pipeline are
unreliable. The reason carbon abundances are not trustworthy is because of an
issue with the coating of the DESI blue collimators, which creates a
throughput artefact in many spectra around 4300Å (see e.g. Figure 26 in DESI
Collaboration et al., 2022). This is the region of the CH absorption band,
which is the most important indicator of carbon abundance in late-type stars
at the resolution of DESI. While the abundances of iron and calcium show a
good correlation with the $\mathrm{\,[Fe/H]}$ and $\mathrm{\,[\alpha/Fe]}$
parameters derived by the same pipeline, that is not the case for magnesium.
We, therefore, recommend not using the abundance of carbon or magnesium
included in the EDR MWS VAC.

  * •

When looking for high-velocity stars, the spectral classification by Redrock
cannot be used because Redrock assumes stars have velocities
$|V_{\text{los}}|<600\mathrm{\,km\,s^{-1}}$. That means that other criteria
described in Section 5.1 should be used instead. However, these selections are
more contaminated by non-stellar sources (QSOs and galaxies) than the criteria
we recommend for general use.

  * •

$v\sin i$ measurements of stellar rotation velocity provided by RVSpecFit are
problematic in this release. The rotational broadening measurement sometimes
compensates for the inability of the model to fit some objects. For example,
measurements of white dwarfs by the RVSpecFit tend to have high inferred
$v\sin i$. In addition, due to a software bug in the processing discovered
during the preparation of this manuscript, $v\sin i$ measurements are biased
to higher values for stars with higher line of sight velocities,
$V_{\text{los}}>50\mathrm{\,km\,s^{-1}}$ especially at low signal to noise.
Thus, we recommend not to use $v\sin i$ values in this release.

  * •

Although the SP and RVS pipelines fit spectra of white dwarfs, these pipelines
are not fully optimised for that purpose. SP uses both hydrogen-rich
atmosphere (DA) and helium-rich atmosphere (DB) white dwarf model grids
provided by Koester (2010), whereas RVS does not have dedicated white dwarf
models and instead uses regular stellar models and compensates for the
pressure-broadened absorption features by increasing $v\sin i$ as noted above.
While the majority of white dwarfs tend to have pure hydrogen DA-type spectra,
many have more complex spectral features such as metal pollution, strong
magnetic fields or molecular carbon from core dredge-up (see figure 2 of
Cooper et al. 2023). Dealing with these complications is beyond the scope of
the SP and RVS pipelines. The DESI EDR white dwarf systems will be described
separately in a future paper Manser et al. (2024).

##  7 Conclusions

This paper presents the first value-added catalogue of stellar measurements
based on the DESI Early Data Release, published in June 2023. The MWS VAC
dataset includes over half a million spectra for more than 420,000 unique
stars with a typical brightness $16\lesssim r\lesssim 20$. We have described
the different target types for the main DESI stellar survey and reviewed
additional targets and special fields that were observed during survey
validation and included in the EDR data set.

As described in Cooper et al. (2023), two stellar analysis pipelines have been
developed for the DESI Milky Way Survey. These pipelines were run on the EDR
dataset to obtain the radial velocities, chemical abundances and stellar
parameters included in the MWS VAC. By comparing these measurements with
earlier high-quality reference catalogues, we conclude that our reported
radial velocities are accurate to approximately $1\mathrm{\,km\,s^{-1}}$,
metallicities to 0.15 dex, effective temperatures to 200 K, surface gravities
to about 0.3 dex, and [$\alpha$/Fe] to about 0.1 dex.

We have identified a number of limitations in this release of the MWS VAC, and
provided a recommended set of criteria that can be used to select a high-
quality subset of the data. At this point, the quality of the parameters
measured by the RVS and SP pipelines is similar, each having its own benefits
and drawbacks. We, therefore, do not provide a definite recommendation to use
either set of parameters beyond the caveats listed in Section 6.

Despite some limitations in the value-added catalogue, it can already be used
in many studies, including chemo-dynamical studies of the Milky Way halo and
analyses of stellar streams and dwarf galaxies within the DESI EDR footprint.

As of the writing of this paper, DESI is progressing into its third year of
observations, with regular data releases planned over the coming years. The
stellar data included in the DESI EDR is a very small fraction of the total
expected data set. Future data releases will include spectra and results for
several million stars. Ongoing development of the analysis pipelines is
expected to address the issues described in this paper, although few changes
are expected in the data model and the target selection criteria. Therefore,
we believe this value-added catalogue based on the EDR data set should be
useful for the community, in particular to prepare for the scientific
exploration of the full DESI Milky Way Survey.

## Acknowledgments

SK acknowledges support from the Science & Technology Facilities Council
(STFC) grant ST/Y001001/1. CAP acknowledges financial support from the Spanish
Ministry MICINN projects AYA2017-86389-P and PID2020-117493GB-I00. APC
acknowledge support from a Taiwan Ministry of Education Yushan Fellowship and
the Taiwan National Science and Technology Council grants
109-2112-M-007-011-MY3 and 112-2112-M-007-017-MY3. T.S.L. acknowledges
financial support from Natural Sciences and Engineering Research Council of
Canada (NSERC) through grant RGPIN-2022-04794. LBS and MV acknowledge support
from NASA-ATP award 80NSSC20K0509.

This material is based upon work supported by the U.S. Department of Energy
(DOE), Office of Science, Office of High-Energy Physics, under Contract No.
DE–AC02–05CH11231, and by the National Energy Research Scientific Computing
Center, a DOE Office of Science User Facility under the same contract.
Additional support for DESI was provided by the U.S. National Science
Foundation (NSF), Division of Astronomical Sciences under Contract No.
AST-0950945 to the NSF’s National Optical-Infrared Astronomy Research
Laboratory; the Science and Technology Facilities Council of the United
Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation;
the French Alternative Energies and Atomic Energy Commission (CEA); the
National Council of Science and Technology of Mexico (CONACYT); the Ministry
of Science and Innovation of Spain (MICINN), and by the DESI Member
Institutions: https://www.desi.lbl.gov/collaborating-institutions. Any
opinions, findings, and conclusions or recommendations expressed in this
material are those of the author(s) and do not necessarily reflect the views
of the U. S. National Science Foundation, the U. S. Department of Energy, or
any of the listed funding agencies.

The authors are honoured to be permitted to conduct scientific research on
Iolkam Du’ag (Kitt Peak), a mountain with particular significance to the
Tohono O’odham Nation.

This work used high-performance computing facilities operated by the Center
for Informatics and Computation in Astronomy (CICA) at National Tsing Hua
University. This equipment was funded by the Taiwan Ministry of Education, the
Taiwan National Science and Technology Council, and National Tsing Hua
University. The authors thankfully acknowledge the technical expertise and
assistance provided by the Spanish Supercomputing Network (Red Española de
Supercomputación), as well as the computing resources available at the
Instituto de Astrofísica de Canarias, including the LaPalma Supercomputer and
the compute servers of the Research Division.

For the purpose of open access, the author has applied a Creative Commons
Attribution (CC BY) licence to any Author Accepted Manuscript version arising
from this submission.

This paper made use of the Whole Sky Database (wsdb) created and maintained by
Sergey Koposov at the Institute of Astronomy, Cambridge, with financial
support from STFC and the European Research Council (ERC).

Software: astropy(Astropy Collaboration et al., 2013, 2018, 2022)

## Data Availability

All the data shown in the published graphs or necessary to reproduce them are
available in a machine-readable form on Zenodo
https://doi.org/10.5281/zenodo.10498911. The value-added catalogue itself is
available at https://data.desi.lbl.gov/doc/releases/edr/vac/mws/.

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##  Appendix A Selection criteria of the main MWS targets in SV3

In this appendix we provide the selection criteria for the SV3 targets. The
selection code is also available at
https://desitarget.readthedocs.io/en/latest/_modules/desitarget/sv3/sv3_cuts.html.
We remark that the EDR covers DESI observations prior to the main survey.
These targets were selected using Gaia DR2 photometry and astrometry, while
the main survey selection uses Gaia DR3 astrometry. These selection criteria
are slightly different from the main survey selection criteria described in
Cooper et al. (2023).

All the columns used for selection of each target are available for each
source in the VAC in the FIBERMAP file extension (see Section 4). In the
following selections $g$,$r$ refer to extinction corrected magnitudes obtained
from Legacy Survey fluxes, $r_{obs}$ is the $r$ magnitude not corrected for
extinction. Other variables are columns in the Legacy Survey table.
$\pi,\sigma_{\pi},\mu$ are Gaia parallax, parallax uncertainty and proper
motion respectively. All the main DESI classes, such as MWS_MAIN_BLUE,
MWS_MAIN_RED, MWS_BROAD are selected using this common set of criteria:

  * •

$16<r<19$

  * •

$r_{obs}<20$

  * •

type = PSF

  * •

gaia_astrometric_excess_noise < 3

  * •

gaia_duplicated_source = False

  * •

brick_primary = True

  * •

nobs_g,r > 0

  * •

g,r_flux > 0

  * •

fracmasked_g,r < 0.5

Definition of MWS_MAIN_BLUE:

  * •

$g-r<0.7$

  * •

astrometric_params_solved=31

Definition of MWS_MAIN_RED:

  * •

$g-r>=0.7$

  * •

astrometric_params_solved =31

  * •

$\pi<max(3\sigma_{\pi},1)$

  * •

$|\mu|<7$

Definition of MWS_BROAD:

  * •

$g-r>=0.7$

  * •

astrometric_params_solved$<$ 31 or $\pi>max(3\sigma_{\pi},1)$ or $|\mu|>7$

Name | Units | Description
---|---|---
VRAD | $\mathrm{\,km\,s^{-1}}$ | Radial velocity
VRAD_ERR | $\mathrm{\,km\,s^{-1}}$ | Radial velocity error
VRAD_SKEW |  | Radial velocity posterior skewness
VRAD_KURT |  | Radial velocity posterior kurtosis
LOGG |  | Logarithm of surface gravity
TEFF | K | Effective temperature
ALPHAFE |  |  $\mathrm{\,[\alpha/Fe]}$ from template fitting
FEH |  |  $\mathrm{\,[Fe/H]}$ from template fitting
LOGG_ERR |  | Log of surface gravity uncertainty
TEFF_ERR | K | Effective temperature uncertainty
ALPHAFE_ERR |  |  $\mathrm{\,[\alpha/Fe]}$ uncertainty from template fitting
FEH_ERR |  |  $\mathrm{\,[Fe/H]}$ uncertainty from template fitting
VSINI | $\mathrm{\,km\,s^{-1}}$ | Stellar rotation velocity
NEXP |  | Number of exposures (unused)
CHISQ_TOT |  | Total chi-square for all arms
CHISQ_C_TOT |  | Total chi-square for all arms for polynomial only fit
CHISQ_B |  | Chi-square in the B arm
CHISQ_C_B |  | Chi-square in the B arm after fitting continuum only
CHISQ_R |  | Chi-square in the R arm
CHISQ_C_R |  | Chi-square in the R arm after fitting continuum only
CHISQ_Z |  | Chi-square in the Z arm
CHISQ_C_Z |  | Chi-square in the Z arm after fitting continuum only
RVS_WARN |  |  RVSpecFit warning flag
REF_ID |  | Tyc1*1,000,000+Tyc2*10+Tyc3 for Tycho-2; source_id for Gaia DR2
REF_CAT |  | Reference catalog source for star: T2 for Tycho-2
|  | G2 for Gaia DR2, L2 for the SGA, empty otherwise
TARGET_RA | deg | Target right ascension
TARGET_DEC | deg | Target declination
TARGETID |  | Unique DESI target ID
SN_B |  | Median S/N in the B arm
SN_R |  | Median S/N in the R arm
SN_Z |  | Median S/N in the Z arm
SUCCESS |  | Did RVSpecFit succeed (RVS_WARN=0) or fail
Table 13: The list of columns in the RVTAB extension with measurements from RVSpecFit. Name | Units | Description
---|---|---
SUCCESS |  | Boolean flag indicating whether the code has likely produced useful results
TARGETID |  | Unique DESI target ID
TARGET_RA | deg | Target right ascension
TARGET_DEC | deg | Target declination
REF_ID |  | Tyc1*1,000,000+Tyc2*10+Tyc3 for Tycho-2; source ID for Gaia DR2
REF_CAT |  | Reference catalog source for star: T2 for Tycho-2
|  | G2 for Gaia DR2, L2 for the SGA, empty otherwise
SRCFILE |  | DESI data file
BESTGRID |  | Model grid that produced the best fit
TEFF | K | Effective temperature
LOGG |  | Logarithm of surface gravity in $cm/s^{2}$
FEH |  | Metallicity $\mathrm{\,[Fe/H]}$
ALPHAFE |  |  $\alpha$-to-iron ratio $\mathrm{\,[\alpha/Fe]}$
LOG10MICRO |  | Log10 of Microturbulence (km/s)
PARAM |  | Array of atmospheric parameters ($\mathrm{\,[Fe/H]}$, $\mathrm{\,[\alpha/Fe]}$, log10micro, $T_{\mathrm{eff}}$, $\log\,g$)
COVAR |  | Covariance matrix for ($\mathrm{\,[Fe/H]}$, $\mathrm{\,[\alpha/Fe]}$, log10micro, $T_{\mathrm{eff}}$, $\log\,g$)
ELEM |  | Elemental abundance ratios to hydrogen [elem/H]
ELEM_ERR |  | Uncertainties in the elemental abundance ratios
CHISQ_TOT |  | Total $\chi^{2}$
SNR_MED |  | Median signal-to-ratio
RV_ADOP | $\mathrm{\,km\,s^{-1}}$ | Adopted Radial Velocity
RV_ERR | $\mathrm{\,km\,s^{-1}}$ | Uncertainty in the adopted Radial Velocity
HEALPIX |  | HEALPix pixel containing this location at NSIDE=64 in the NESTED scheme
Table 14: The list of columns in the SPTAB extension with measurements from
FERRE.

##  Appendix B Recommended selection for the SP parameters

In the case of the results of the SP pipeline, included in the SPTAB
extensions of various files, we recommend the following cuts to obtain a
science-grade sample:

  * •

${\tt SNR\\_MED}>5$, a minimum median signal-to-noise ratio of 5,

  * •

${\tt CHISQR\\_TOT}<5$, a reduced $\chi^{2}$ smaller than 5,

  * •

${\tt TEFF}>4000$ K, to avoid M-type stars,

  * •

${\tt FEH}>-4.9$, avoiding the stars that accumulate the edge of the grids,
and

  * •

${\tt BESTGRID}\neq$ s_rdesi1, avoiding the stars that were fitted with the
PHOENIX grid.

The use of the PHOENIX grid for stars with effective temperatures between 2300
K and 5000 K was adopted to supplement the Kurucz (2005) grids since the
latter only cover $T_{\rm eff}\geq 3500$ K. For stars in the overlapping range
of temperatures between two grids the results with the lowest $\chi^{2}$ were
adopted. We find that this led to spurious results when the PHOENIX grid was
used. One such example is the accumulation of stars near the grid edge at 5000
K illustrated in Figure 5, the presence of systematic errors in the surface
gravity of cool dwarfs, or large scatter in the abundances for cool dwarfs
seen in Figure 11.

##  Appendix C Recommended selection for the RVS parameters

On top of the selection listed in Section 5.1, for a more reliable subset of
RVS parameters, we recommend applying the following cuts.

  * •

FEH>-3.9 removes objects clumped at the bottom edge of the metallicity grid.

##  Appendix D Author affiliations

1 Institute for Astronomy, University of Edinburgh, Royal Observatory,
Blackford Hill, Edinburgh EH9 3HJ, UK
2 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge
CB3 0HA, UK
3 Kavli Institute for Cosmology, University of Cambridge, Madingley Road,
Cambridge CB3 0HA, UK
4 Instituto de Astrofísica de Canarias, C/ Vía Láctea, s/n, E-38205 La Laguna,
Tenerife, Spain
Universidad de La Laguna, Dept. de Astrofísica, E-38206 La Laguna, Tenerife,
Spain
5 Institute of Astronomy and Department of Physics, National Tsing Hua
University, 101 Kuang-Fu Rd. Sec. 2, Hsinchu 30013, Taiwan
6 Department of Astronomy & Astrophysics, University of Toronto, Toronto, ON
M5S 3H4, Canada
7 Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA
8 Steward Observatory, University of Arizona, 933 N, Cherry Ave, Tucson, AZ
85721, USA
9 Institute for Computational Cosmology, Department of Physics, Durham
University, South Road, Durham DH1 3LE, UK
10 NSF’s NOIRLab, 950 N. Cherry Ave., Tucson, AZ 85719, USA
11 Astrophysics Group, Department of Physics, Imperial College London, Prince
Consort Rd, London, SW7 2AZ, UK
12 Department of Physics, University of Warwick, Gibbet Hill Road, Coventry,
CV4 7AL, UK
13 Physics Department, Yale University, P.O. Box 208120, New Haven, CT 06511,
USA
14 Department of Astronomy and Astrophysics, UCO/Lick Observatory, University
of California, 1156 High Street, Santa Cruz, CA 95064, USA
15 Department of Astronomy and Astrophysics, University of California, Santa
Cruz, 1156 High Street, Santa Cruz, CA 95065, USA
16 University of California Observatories, 1156 High Street, Sana Cruz, CA
95065, USA
17 University of Michigan, Ann Arbor, MI 48109, USA
18 Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA
94720, USA
19 Physics Dept., Boston University, 590 Commonwealth Avenue, Boston, MA
02215, USA
20 Department of Physics & Astronomy, University College London, Gower Street,
London, WC1E 6BT, UK
21 Instituto de Física, Universidad Nacional Autónoma de México, Cd. de México
C.P. 04510, México
22 Department of Physics & Astronomy and Pittsburgh Particle Physics,
Astrophysics, and Cosmology Center (PITT PACC), University of Pittsburgh, 3941
O’Hara Street, Pittsburgh, PA 15260, USA
23 Departamento de Física, Universidad de los Andes, Cra. 1 No. 18A-10,
Edificio Ip, CP 111711, Bogotá, Colombia
24 Observatorio Astronómico, Universidad de los Andes, Cra. 1 No. 18A-10,
Edificio H, CP 111711 Bogotá, Colombia
25 Institut d’Estudis Espacials de Catalunya (IEEC), 08034 Barcelona, Spain
26 Institute of Cosmology & Gravitation, University of Portsmouth, Dennis
Sciama Building, Portsmouth, PO1 3FX, UK
27 Institute of Space Sciences, ICE-CSIC, Campus UAB, Carrer de Can Magrans
s/n, 08913 Bellaterra, Barcelona, Spain
28 Sorbonne Université, CNRS/IN2P3, Laboratoire de Physique Nucléaire et de
Hautes Energies (LPNHE), FR-75005 Paris, France
29 Departament de Física, Serra Húnter, Universitat Autònoma de Barcelona,
08193 Bellaterra (Barcelona), Spain
30 Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of
Science and Technology, Campus UAB, 08193 Bellaterra Barcelona, Spain
31 Institució Catalana de Recerca i Estudis Avançats, Passeig de Lluís
Companys, 23, 08010 Barcelona, Spain
32 Department of Physics and Astronomy, Siena College, 515 Loudon Road,
Loudonville, NY 12211, USA
33 National Astronomical Observatories, Chinese Academy of Sciences, A20 Datun
Rd., Chaoyang District, Beijing, 100012, P.R. China
34 IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
35 Department of Physics and Astronomy, University of Waterloo, 200 University
Ave W, Waterloo, ON N2L 3G1, Canada
36 Perimeter Institute for Theoretical Physics, 31 Caroline St. North,
Waterloo, ON N2L 2Y5, Canada
37 Waterloo Centre for Astrophysics, University of Waterloo, 200 University
Ave W, Waterloo, ON N2L 3G1, Canada
38 Department of Physics, Kansas State University, 116 Cardwell Hall,
Manhattan, KS 66506, USA
39 Department of Physics and Astronomy, Sejong University, Seoul, 143-747,
Korea
40 CIEMAT, Avenida Complutense 40, E-28040 Madrid, Spain
41 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD
21218, USA
42 Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA