MNRAS 506, 150–201 (2021) https://doi.org/10.1093/mnras/stab1242
Advance Access publication 2021 May 6
The GALAH+ survey: Third data release
Sven Buder ,
1,2,3‹
Sanjib Sharma ,
2,4
Janez Kos,
5
Anish M. Amarsi ,
6
Thomas Nordlander ,
1,2
Karin Lind,
3,7
Sarah L. Martell ,
2,8
Martin Asplund,
9
Joss Bland-Hawthorn ,
2,4
Andrew R. Casey ,
10,11
Gayandhi M. De Silva,
12,13
Valentina D’Orazi,
14
Ken C. Freeman,
1,2
Michael R. Hayden,
2,4
Geraint F. Lewis ,
4
Jane Lin,
1,2
Katharine. J. Schlesinger,
1
Jeffrey D. Simpson ,
2,8
Dennis Stello,
4,8,15
Daniel B. Zucker,
13,16
Toma
ˇ
z Zwitter ,
5
Kevin L. Beeson,
5
Tobias Buck ,
17
Luca Casagrande ,
1,2
Jake T. Clark ,
18
Klemen
ˇ
Cotar ,
5
Gary S. Da Costa ,
1,2
Richard de Grijs,
13,16,19
Diane Feuillet ,
3,20
Jonathan Horner ,
18
Prajwal R. Kafle ,
21
Shourya Khanna,
22
Chiaki Kobayashi,
2,23
Fan Liu ,
24
Benjamin T. Montet,
8
Govind Nandakumar ,
1,2
David M. Nataf ,
25
Melissa K. Ness,
26,27
Lorenzo Spina ,
2,11,28
Thor Tepper-Garc
´
ıa,
2,4,29
Yuan-Sen Ting(),
1,30,31,32
Gregor Traven,
20
Rok Vogrin
ˇ
ci
ˇ
c,
5
Robert A. Wittenmyer ,
18
Rosemary F. G. Wyse ,
25
Maru
ˇ
sa
ˇ
Zerjal
1
and the GALAH Collaboration
Affiliations are listed at the end of the paper
Accepted 2021 April 27. Received 2021 April 26; in original form 2020 November 5
ABSTRACT
The ensemble of chemical element abundance measurements for stars, along with precision distances and orbit properties,
provides high-dimensional data to study the evolution of the Milky Way. With this third data release of the Galactic Archaeology
with HERMES (GALAH) survey, we publish 678 423 spectra for 588 571 mostly nearby stars (81.2 per cent of stars are within
<2 kpc), observed with the HERMES spectrograph at the Anglo-Australian Telescope. This release (hereafter GALAH+ DR3)
includes all observations from GALAH Phase 1 (bright, main, and faint survey, 70 per cent), K2-HERMES (17 per cent),
TESS-HERMES (5 per cent), and a subset of ancillary observations (8 per cent) including the bulge and >75 stellar clusters.
We derive stellar parameters T
eff
,logg, [Fe/H], v
mic
, v
broad
, and v
rad
using our modified version of the spectrum synthesis
code Spectroscopy Made Easy (
SME) and 1D MARCS model atmospheres. We break spectroscopic degeneracies in our spectrum
analysis with astrometry from Gaia DR2 and photometry from 2MASS. We report abundance ratios [X/Fe] for 30 different
elements (11 of which are based on non-LTE computations) covering five nucleosynthetic pathways. We describe validations
for accuracy and precision, flagging of peculiar stars/measurements and recommendations for using our results. Our catalogue
comprises 65 per cent dwarfs, 34 per cent giants, and 1 per cent other/unclassified stars. Based on unflagged chemical composition
and age, we find 62 per cent young low-α, 9 per cent young high-α, 27 per cent old high-α, and 2 per cent stars with [Fe/H]
≤−1. Based on kinematics, 4 per cent are halo stars. Several Value-Added-Catalogues, including stellar ages and dynamics,
updated after Gaia eDR3, accompany this release and allow chrono-chemodynamic analyses, as we showcase.
Key words: methods: data analysis – methods: observational – surveys – stars: abundances – stars: fundamental parameters.
1 INTRODUCTION
During the history of the Milky Way, the abundances of the different
elements that make up the Galaxy’s stars and planets have continually
changed, as a result of the processing of the interstellar medium by
successive generations of stars. As a result, the study of the elemental
abundances in stars provides a direct record of the Galaxy’s history
of star formation and evolution – a fact that has, in recent years, given
birth to the science of Galactic Archaeology.
Until recently, however, observational limitations meant that the
data available to answer the questions of how the Milky Way formed
E-mail: sven.buder@anu.edu.au
and evolved was restricted to a few hundred or thousand stars
with high-quality element abundances in our Solar neighbourhood
(see e.g. Edvardsson et al. 1993; Nissen & Schuster 2010;Bensby,
Feltzing & Oey 2014). In the last decade, advances in multi-object
observations made by spacecraft (such as Gaia) and ground-based
facilities have brought about a revolution in the field of Galactic
Archaeology. Where once the field was forced to focus on single-star
population studies, it is now possible to carry out surveys that allow
large-scale structural analyses.
Due to the intrinsic difficulty in determining the distances of stars,
studies of the chemodynamical evolution of our Milky Way were
previously restricted to nearby stars which were mapped by the
Hipparcos satellite (ESA 1997;Perrymanetal.1997; van Leeuwen
2007). In the era of the Gaia satellite (Gaia Collaboration 2016a, b,
C
2021 The Author(s)
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The GALAH+ survey DR3 151
2018), we can now use astrometric and photometric observables and
their physical relations with spectroscopic quantities to improve the
analysis of spectra and thus the estimation of element abundances.
The connections between the chemical compositions and dynam-
ics of stars across the vast populations in our Galaxy are a topic of
significant ongoing research. Although we speak of the Milky Way in
terms of the thin and thick disc (Yoshii 1982; Gilmore & Reid 1983),
the bulge (Barbuy, Chiappini & Gerhard 2018), and the stellar halo
(Helmi 2020) as its main components (Bland-Hawthorn & Gerhard
2016), we understand that the Galaxy is more than a superposition of
independent populations. With the data now at hand, we can analyse
the Galaxy from a chemodynamical perspective, and use stars of
different ages as time capsules to trace back the formation history of
our Galaxy (see e.g. Rix & Bovy 2013; Bland-Hawthorn et al. 2019).
As one example, the most recent data release from Gaia has enabled
significant leaps in our understanding of the enigmatic Galactic halo
(for an overview see e.g. Helmi 2020). 6D phase space information
from Gaia has revealed a large population of stars in the Solar
neighbourhood that stand out against the smooth halo background as
a coherent dynamical structure, pointing to a significant accretion
event that is currently referred to as ‘Gaia–Enceladus–Sausage’
(GES) a combination of ‘Gaia–Enceladus’ (Helmi et al. 2018)and
‘Gaia Sausage’ (Belokurov et al. 2018). Additionally, while we
would expect the chemical composition of stars to be correlated
with their ages and formation sites (see e.g. Minchev et al. 2017),
observations can now clearly demonstrate these connections (see e.g.
Feuillet et al. 2018; Buder et al. 2019), and can also demonstrate that
stars within our Solar neighbourhood have experienced significant
radial migration through their lifetimes (see e.g. Frankel et al. 2018;
Hayden et al. 2020).
Despite these significant advances, the full detail of our Galaxy’s
formation and history still elude us. Many of the pieces that make
up that puzzle are presently missing, or remain contentious. As a
result, a number of questions still remain to be answered. These
include the discrete merger history of our Milky Way, the (non-
)existence of an in situ halo and the reason for the sharp transition
from formation of stars with high α-element abundances in what
has historically been called the ‘thick disc’ to younger stars with
Solar-like α-element abundances in the ‘thin disc’. We have learnt a
great deal about contributions of supernovae to element abundances,
starting from the foundational work by Burbidge et al. (1957),
and how we can use diagrams displaying element abundances, e.g.
in [Fe/H]versus [α/Fe]diagrams, as diagnostic tools of stellar and
Galactic evolution. These advances in our understanding are largely
thanks to the pioneering and seminal studies by Tinsley (1979, 1980)
and others, building on the trail-blazing observational achievements
of Wallerstein (1962) and others. To honour the fundamental contri-
butions by Beatrice M. Tinsley and George Wallerstein, connecting
the contributions of SNIa and SNII with element abundances in the
[Fe/H]versus [α/Fe]diagrams, we will hereafter refer to these as the
‘Tinsley–Wallerstein diagrams’.
Previous and ongoing spectroscopic surveys by collaborations like
RAVE (Steinmetz et al. 2020a,b), Gaia–ESO (Gilmore et al. 2012),
SDSS-IV APOGEE (Ahumada et al. 2020), and LAMOST (Cui
et al. 2012; Xiang et al. 2019) have certainly shed light on several of
these outstanding questions. Answering them completely, requires
more and/or better data to map out the correlations between stellar
ages, abundances, and dynamics. Upcoming surveys like SDSS-
V (Kollmeier et al. 2017), WEAVE (Dalton et al. 2018), 4MOST
(de Jong et al. 2019), and PFS (Takada et al. 2014) will certainly
continue to broaden our capabilities and understanding surrounding
our Galaxy’s physical and chemical evolution. The data currently
at hand, derived from spectroscopy, photometry, astrometry, and
asteroseismology, provide high-dimensional information, and we
must develop methods to extract the most accurate and precise
information from them (for reviews on this see e.g. Nissen &
Gustafsson 2018;Jofr
´
e, Heiter & Soubiran 2019).
The recent growth in the quantity of available spectroscopic
stellar data has delivered a new technique to galactic archaeologists
– namely ‘Chemical Tagging’, which allows the identification of
stars that formed together using their chemical composition and
an understanding of the astrophysics driving the dimensionality of
chemical space. This technique is proving a vital tool, enabling us
to observationally isolate and characterize the building blocks of our
Galaxy. As a result, it remains a major science driver for the GALactic
Archaeology with HERMES
1
(GALAH) collaboration
2
(De Silva
et al. 2015). With the large variety of nucleosynthetic channels that
can enrich the birth material of stars (see e.g. Kobayashi, Karakas &
Lugaro 2020), the hypothesis is that we should be able to disentangle
stars with different enrichment patterns, provided we observe enough
elements with different enrichment origins. The success of some
chemical tagging experiments (see e.g. Kos et al. 2018; Price-Jones
et al. 2020) is challenged by the broad similarities in chemical
abundance in populations like the low-α disc (see e.g. Ness et al.
2018), and by the small but real inhomogeneities even within star
clusters (Liu et al. 2016a, b). To put detailed chemical tagging into
action, we will need a massive data set (see e.g. Ting, Conroy & Rix
2016) consisting of measurements made with outstanding precision
(Ting & Weinberg 2021).
The publication of the previous second data release of the GALAH
survey (Buder et al. 2018), entirely based on observations as part of
GALAH Phase 1 with the HERMES spectrograph at the Anglo-
Australian Telescope, has provided for the community abundance
measurements of 23 elements based on 342 682 spectra. Observations
for this phase have continued and we are able to publish all 476 863
spectra for 443 843 stars of the now finished Phase 1 observations
as part of this third data release. In parallel, spectroscopic follow-
up observations of K2 and TESS targets have been performed with
HERMES and we are able to also include these observations (112 943
spectra for 99 152 K2-HERMES stars and 34 263 spectra for 26 249
TESS-HERMES stars) in our release. We further include ancillary
observations of 54 354 spectra for 28 205 stars in fields towards
the bulge and more than 75 stellar clusters. Given the significant
contribution from these programmes to this GALAH release, we
will hereafter refer to the release as GALAH+ DR3.
For the previous (second) data release of the GALAH survey
(Buder et al. 2018), we made use of the data-driven tool The Cannon
(Ness et al. 2015) to improve both the speed and the precision of the
spectroscopic analysis. This was performed almost entirely without
non-spectroscopic information for individual stars, using a ‘training
set’ of stars with careful by-hand analysis. Although the data-driven
approaches were successful for the majority of GALAH DR2 stars,
we know that these approaches can suffer from signal aliasing (e.g.
moving outliers closer to the main trends), can learn unphysical
correlations between the input data and the output stellar labels,
and that the results are not necessarily valid outside the parameter
space of the training set. As part of this study, we aim to assess
how accurately and precisely the stellar parameters and abundances
were estimated by the data-driven approaches. We have therefore
adjusted our approach to the analysis of the whole sample and
1
High Efficiency and Resolution Multi-Element Spectrograph
2
https://www.galah-survey.org
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152 S. Buder et al.
now restrict ourselves to smaller wavelength segments of the four
wavelength windows observed by HERMES per star with reliable
line information for spectrum synthesis instead and include even
more grids for an accurate computation of line strengths when the
conditions depart from local thermodynamic equilibrium (LTE; e.g.
Mihalas & Athay 1973; Asplund 2005;Amarsietal.2020).
The publication of Gaia DR2 (Gaia Collaboration 2018; Lindegren
et al. 2018) provided phase space information up to 6 dimensions
(coordinates, proper motions, parallaxes, and sometimes also radial
velocities) for 1.3 billion stars, and having this information available
for essentially all (99 per cent) stars in GALAH has allowed us
to make major improvements to our stellar analysis. By combining
our knowledge of the (absolute) photometry and spectroscopy of
stars, we can break several of the degeneracies in our standalone
spectroscopic analyses, which arise due to the fact that absorption
lines do not always change to a detectable level as a function
of stellar atmospheric parameters. The data analysis process for
this third data release from the GALAH collaboration makes use
of fundamental correlations, and this quantifiably improves the
accuracy and precision of our measurements.
As large Galactic Archaeology-focused surveys continue to collect
data (like GALAH in its ongoing Phase 2), the overlap between them
increases. This enables us to compare results when analysing stars
in the overlap, which have the same stellar labels (stellar parameters,
abundances, or other non-spectroscopic stellar information), and it
also allows us to propagate labels from one survey on to another
(see e.g. Casey et al. 2017;Hoetal.2017; Xiang et al. 2019;
Nandakumar et al. 2020; Wheeler et al. 2020). This label propagation
makes it possible to combine these complementary surveys for global
mapping of stellar properties and abundances, and we show an
example of this in Section 8, placing GALAH+ DR3dataincontext
with the APOGEE and LAMOST surveys.
This paper is structured as follows: We describe our target
selection, observations, and reductions in Section 2. While the
target selection and observation of the several projects like K2-
HERMES and TESS-HERMES were slightly different from the main
GALAH survey, we have reduced and analysed all data (combined
under the term GALAH+) in a consistent and homogeneous way.
The analysis of the reduction products is described in Section 3,
focusing on the description of the general workflow of the analysis
group and highlighting changes with respect to the previous release
(GALAH DR2). Sections 4 and 5 address the validation efforts
for stellar parameters and element abundances, respectively. These
address the accuracy and precision of these labels as well as our
algorithms to identify and flag peculiar measurements or peculiar
stars. Based on experience with the data set, we stress the importance
of the flags, but also how complex the flagging estimates are, with
several examples of peculiar abundance patterns. We also highlight
possible caveats (and possibly peculiar physical correlations) of our
analysis in Section 6. We present the contents of the main catalogue
of this data release in Section 7. In this section, we also present
the Value-Added-Catalogues (VACs) that accompany this release,
namely a VAC (see Section 7.3.1) created by cross-matching our
targets with Gaia eDR3 (Gaia Collaboration 2021) and the distance
estimates by Bailer-Jones et al. (2021), a VAC (see Section 7.3.2)
with estimates (such as stellar ages and masses) from isochrone
fitting, a VAC (see Section 7.3.3) with stellar kinematic and dynamic
estimates, a VAC (see Section 7.3.4) with radial velocity estimates
based on different methods, and a VAC (see Section 7.3.5) on
parameters of binary systems. While we made use of data from
Gaia DR2 (Gaia Collaboration 2018) for our spectroscopic analysis,
we also provide a second version of each of our catalogues with
new cross-matches and VACs with updated data making use of Gaia
eDR3 (Gaia Collaboration 2021), which was published shortly after
our data release and supersedes Gaia DR2. We describe all changes
of the catalogues between version 1 (based on Gaia DR2) and version
2 (with VACs now based on Gaia eDR3) in Section 7.1. We highlight
the scientific potential of the data in this release in context by using
the combination of dynamic information and ages together with the
element abundances of the main catalogue in Section 8. We focus
on Galactic Archaeology on a global scale and the chemodynamical
evolution of our Galaxy. Along with the main and value-added-
catalogues of this release, we publish the observed optical spectra
for each of the arms of HERMES on the DataCentral
3
and provide
the scripts used for the analysis as well as post-processing online
in an open-source repository
4
GALAH+ DR3 was timed to allow
the scientific community to directly use abundances together with
the latest Gaia eDR3 information. We have not yet incorporated the
latter into our abundance analysis, but plan to do so in future data
releases, as we outline in Section 9. In this section, we conclude
and give an outlook to future observations as part of the ongoing
observations of the GALAH survey (called phase 2 with an adjusted
target selection) and our next data release.
2 TARGET SELECTION, OBSERVATION,
REDUCTION
While our previous data release (Buder et al. 2018) contained only
stars from the main GALAH survey, the current catalogue combines
data from multiple projects with differentscience goals, all conducted
with the HERMES spectrograph (Sheinis et al. 2015) and the 2dF
fibre positioning system (Lewis et al. 2002) at the 3.9-metre Anglo-
Australian Telescope. All the spectra have therefore been processed
through the same data reduction pipeline. The collection into a single
catalogue, which includes the K2-HERMES (S. Sharma et al., in
preparation) and TESS-HERMES (S. Sharma et al., in preparation)
surveys, was chosen for ease of use. Full details of these additional
surveys are presented in their corresponding data release papers and
users are advised to refer to those when using data from these surveys.
The column survey
name in the catalogue denotes the survey
each star belongs to. Data from four main projects, plus a number
of smaller observing programmes, are included in GALAH+ DR3.
Fig. 1 shows their on-sky distribution. The majority of the stars are
nearby, with a median distance of 826 pc (see Fig. 2a), and cover
a large variety of stellar types and evolutionary stages, as can be
seen in the colour–magnitude diagrams both with Gaia (Fig. 2b) and
2MASS (Fig. 2c) bandpasses. Below, we describe the target selection
for each of the four main projects.
2.1 Target selection
The GALAH input catalogue was made by combining the 2MASS
(Skrutskie et al. 2006) catalogue of infrared photometry with
the UCAC4 (Zacharias et al. 2013) proper motion catalogue. We
only included stars with reliable 2MASS data, as captured in
their data quality flags (Q=‘A’, B=‘1’, C=‘0’, X=‘0’, A=‘0’,
prox≥6 arcsec). We also rejected any star that had a nearby bright
neighbour, with a rejection radius dependent on the bright star’s V
magnitude, such that the potential target is rejected if the bright star
is closer than (130 − [10 × V]) arcsec. The APASS photometric
3
https://docs.datacentral.org.au/galah/
4
http://github.com/svenbuder/GALAH DR3
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The GALAH+ survey DR3 153
Figure 1. Overview of the distribution of stars included in this data release in Galactic coordinates with the centre of the Galaxy at the origin. Shown are the
GALAH main (blue) and faint (orange) targets, which avoid the Galactic plane. The targets of the K2-HERMES follow-up (green) fall within with the K2
campaigns along the ecliptic and show the characteristic tile-pattern of the Kepler telescope. The TESS-HERMES observations (red) are focused on the TESS
Southern Continuous Viewing Zone. Other HERMES targets (purple) are distributed across the sky and were observed during independent programmes.
Figure 2. Overview of distances and photometric information corresponding to the spectra (including repeats for some stars) observed as part of GALAH+ DR3
up to 2019 February 25. Panel (a) shows the Bailer-Jones et al. (2018) distances of stars in GALAH+ DR3. Due to the magnitude limited selection of stars,
the majority of stars are not only dwarfs but also nearby; that is, within 1 kpc. Only 5.8 per cent of stars are beyond 4 kpc. Panel (b) shows a colour–absolute
magnitude diagram in the optical Gaia passbands. Panel (c) shows an analogous diagram made with the infrared 2MASS passbands.
catalogue (Henden et al. 2012) was not complete in the Southern sky
at the start of GALAH observations in 2013, so we use a synthetic
V
JK
magnitude calculated from 2MASS photometry: V
JK
= K +
2(J − K + 0.14) + 0.382e
((J − K − 0.2)/0.5)
. Sharma et al. (2018)
demonstrate by using PARSEC isochrones (Marigo et al. 2017)that
this is a reasonable approximation for the V magnitude for the types
of stars observed in GALAH.
Four main projects are included in the GALAH+ DR3 cat-
alogue (GALAH-main, GALAH-faint, K2-HERMES, and TESS-
HERMES), each of which has its own selection function. We have
attributed each possible pointing of the major sub-surveys to a
specific field
id, as listed in Table 1. The main GALAH survey
takes as potential targets all stars with 12.0 < V
JK
< 14.0, δ<+10
◦
and |b| > 10
◦
in regions of the sky that have at least 400 targets in
π square degrees (the 2dF field of view). We then segment this data
Tab le 1. Field selection (field id) for the programmes included in this
data release. Note the gaps between different TESS-HERMES fields are
caused by other HERMES programmes in between them.
Programme field id Nr. Spectra survey name
GALAH Main 0...6545 462045 galah main
GALAH Faint 6831...7116 14818 galah
faint
K2-HERMES 6546...6830 112943 k2
hermes
TESS-HERMES 7117...7338 34263 tess
hermes
7358...7365 – –
7426...7431 – –
HERMES other other 54354 other
Total – 678423 –
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154 S. Buder et al.
Figure 3. Overview and distribution of parallax uncertainty and S/N for different types of stars (not spectra as in Fig. 2). Panel (a) Parallax ( ) uncertainty
provided by Gaia DR2. We note that the median uncertainty has decreased from 2.7 per cent to 1.5 per cent between Gaia DR2 and Gaia eDR3. 561 229
(95 per cent) stars sit below 20 per cent in fractional uncertainty, and 27 243 (5 per cent) stars fall above 20 per cent. Panel (b) Distribution of Gaia DR2 fractional
parallax uncertainty across the stellar parameters T
eff
and logg derived by GALAH+ DR3. Local cool dwarfs have the most reliable parallax information, while
giants, and especially luminous giants have the worst. Panel (c) Distribution of S/N per pixel for the green channel (CCD2) across the stellar parameters T
eff
and
log g. Hot dwarfs (brighter than cool stars in the green channel) and luminous giants (brightest within the magnitude limited cohort) have the highest S/N in the
green channel. The S/N for hot stars is typically better in the blue and green CCDs (relative to cool stars), whereas it is higher in the red and IR CCDs for the
cool stars (relative to hot stars).
set into 6546 ‘fields’ with a fixed centre and radius between 0.7
◦
and 1
◦
. Fields containing more than 400 stars are observed multiple
times with separate target lists. The GALAH-faint programme was
aimed at extending survey observations to regions with low target
density. The target selection was shifted to 12 < V
JK
< 14.3 as a
way to maintain at least 400 stars per field. The GALAH survey also
includes a few other extensions. The GALAH-bright programme
targets bright stars (9.0 < V
JK
< 12.0) to be observed in twilight
or poor observing conditions. For bright stars, we use the same field
centres as in regular survey observing, and require at least 200 stars
per field. The GALAH-ultrafaint programme targets very faint stars
14 < V
JK
< 16. This was aimed at extending the survey into regions
further away from the Sun. These fields were only observed under
dark conditions.
The K2-HERMES survey leverages the excellent match between
the two degree diameter of the 2dF fibre positioner and the five
square degrees covered by each detector in the Kepler spacecraft
to create an efficiently observed spectroscopic complement for red
giants in the K2 campaign fields. The K2-HERMES programme has
both ‘bright’ (10 < V
JK
< 13) and ‘faint’ (13 < V
JK
< 15, J −
K
S
> 0.5) target cohorts, to complement the asteroseismic targets
that are the focus of the K2 Galactic Archaeology Program (Stello
et al. 2015, 2017). Analysis of asteroseismic and spectroscopic data
together is key for GALAH+ DR3, and enables in-depth exploration
of the structure and history of the Milky Way (e.g. Sharma et al.
2016, 2019). The spectroscopic data also provide essential insights
for the planet hosting stars identified in K2 data (Wittenmyer et al.
2018, 2020).
The TESS-HERMES survey collected spectra for stars in the range
10.0 < V
JK
< 13.1 in the TESS Southern Continuous Viewing Zone,
within 12
◦
of the Southern ecliptic pole. TESS-HERMES aimed
to provide accurate stellar parameters for candidate TESS input
catalogue stars (Stassun et al. 2019), to better focus TESS target
selection on the most promising asteroseismic targets. The results of
the TESS-HERMES project are publicly available, and the project
and outputs are described in Sharma et al. (2018). 54 354 in the
‘HERMES other’ programme are from targeted observations of stars
in open clusters, the GALAH Pilot Survey (Martell et al. 2017), or
targets from other HERMES observing that were not part of any of
these surveys.
Since GALAH observes stars mainly nearby stars (81.2 per cent
of the sample is within 2 kpc, as shown in Fig. 2), almost all GALAH
targets have well measured 5D (99 per cent) or even 6D (45 per cent)
information from Gaia (Gaia Collaboration 2018; Lindegren et al.
2018). An overview of the astrometric and spectroscopic quality for
the observed stars can be found in Fig. 3(a). The median fractional
parallax error for GALAH stars is 2.7 per cent, and 95 per cent
(561 229) of GALAH stars have parallax errors below 20 per cent
(see panel a). A total of 588 571 of our observations are of stars with
matched Gaia parallax measurements. When dividing the sample into
giants (T
eff
<5500 K and M
K
S
< 2 mag) and dwarfs (T
eff
≥5500 K or
M
K
S
≥ 2 mag), 96 per cent (369 227/383 088) of the observed dwarf
stars have parallax uncertainties below 10 per cent and 70 per cent
(140 840/200927) of the observed giant stars have parallax uncertain-
ties below 10 per cent. The inferred distance estimates from Bailer-
Jones et al. (2018), used for the spectroscopic analysis in this release,
are crucial for the small fraction of GALAH+DR3 stars with parallax
uncertainties above 20 per cent.
Additionally, the available asteroseismic information is growing
steadily as the analysis of data from the K2 campaigns progresses.
The overlap between GALAH targets and K2 targets from campaign
C1-C8 and C10-C18 has increased to more than 10 000 stars
with measured asteroseismic ν
max
values (Zinn et al. 2020)and
spectroscopic information, and covers almost the entire red giant
branch (log g ∼ 1.5−3.0 dex) and helium-core burning red clump.
The magnitude limited selection of the GALAH survey (see
the magnitude distribution in Fig. 4a) causes a strong correlation
between increasing distance (and decreasing parallax quality) with
increasing luminosity. This tradeoff between luminosity and parallax
uncertainty was also visible for the stars in common between
Gaia DR1 and GALAH DR2 (Buder et al. 2019) and is still present
with the use of Gaia DR2, as we illustrate in Kiel diagrams in
Fig. 3(b), showing that especially giants with larger distances suffer
from large parallax uncertainties.
MNRAS 506, 150–201 (2021)
Downloaded from https://academic.oup.com/mnras/article/506/1/150/6270919 by University of Southern Queensland user on 20 July 2021
![Table 6. Comparison of GALAH DR3 element abundances of dwarfs and giants in M67 and Ruprecht 147. Values are only listed if more than five pairs with unflagged [X/Fe] were available.](/figures/table-6-comparison-of-galah-dr3-element-abundances-of-dwarfs-148hpf2n.webp)
![Figure 16. Element abundances [X/Fe] as a function of stellar parameters [Fe/H], Teff, and log g for a selection of elements X from the four clusters with information from both GALAH+ DR3 (unflagged in black, flagged in red) and the OCCAM survey (unflagged data, blue Donor et al. 2020) in Fig. 11. Horizontal bars indicate the mean abundances of the clusters from GALAH in grey (estimated from unflagged measurements for stars with 4500 < Teff < 6500 K) and the OCCAM survey (blue).](/figures/figure-16-element-abundances-x-fe-as-a-function-of-stellar-3gl5bda3.webp)
