$v\sin i$ | 12753 | merged with WG11
$r>0$ | 352 | …
derived
$\Delta W({\rm H}\alpha)_{\rm chr}$ | 2826 | 476
$\Delta W({\rm H}\beta)_{\rm chr}$ | … | 256
$F({\rm H}\alpha)_{\rm chr}$ | 2726 | 436
$F({\rm H}\beta)_{\rm chr}$ | … | 224

Working Group 12 made use of specialised methods for analyzing spectra of
young, low-mass stars, particularly not-embedded pre-main sequence stars. The
analysis took chromospheric activity and possible mass accretion into account.
Both UVES/U580 and GIRAFFE/HR15N spectra were analyzed.

Six nodes contributed to the analysis: INAF–Osservatorio Astrofisico di
Arcetri, Centro de Astrofisica de Universidade do Porto (CAUP), Università di
Catania and INAF–Osservatorio Astrofisico di Catania (OACT), INAF–Osservatorio
Astronomico di Palermo (OAPA), Universidad Complutense de Madrid (UCM), and
Eidgenössische Technische Hochschule Zürich (ETH).

The operation of Working Group 12 is described more fully in the article
”Gaia-ESO Survey: Analysis of pre-main sequence stellar spectra” Lanzafame et
al. (2015). Here, a brief summary and some updates are reported.

A summary of the parameters obtained by WG12 is reported in Table 14. A
distinction is made amongst raw, fundamental, and derived parameters. The
H$\alpha$ emission equivalent width ($W({\rm H}\alpha)$), the Li absorption
equivalent width ($W({\rm Li})$), the H$\alpha$ width at 10% of the line peak
(Natta et al. 2004), and the H$\alpha$ full width at zero intensity (FWZI) are
considered raw parameters as these are directly measured on the input spectra
and do not require any prior information. They are used to identify pre-main-
sequence (PMS) stars and optimise the evaluation of the fundamental parameters
in ROTFIT (Frasca et al. 2006), one of the two methods used. In addition to
$T_{\rm eff},\log g$, and [Fe/H], the fundamental parameters derived include
also micro-turbulence velocity ($\xi$), projected rotational velocity ($v\sin
i$), veiling ($r$, see, e.g., Hartigan et al. 1988), and a gravity-sensitive
spectral index ($\gamma$, see Damiani et al. 2014b). Finally, the derived
parameters are those whose derivation requires prior knowledge of the
fundamental parameters: chromospheric activity indices ($\Delta W({\rm
H}\alpha)_{\rm chr}$ and $\Delta W({\rm H}\beta)_{\rm chr}$) taken as
equivalent width difference with respect to an inactive template with the same
fundamental parameters, and chromospheric line fluxes ($F({\rm H}\alpha)_{\rm
chr}$ and $F({\rm H}\beta)_{\rm chr}$) evaluated from the chromospheric
activity index and the expected flux at the continuum.

Fundamental parameters were inferred by the OACT node using ROTFIT (Frasca et
al. 2006) and by the OAPA node using a spectral indices method (Damiani et al.
2014b). The spectral indices results are considered valid below a given
threshold of blending due to rotational broadening. In the case at hand, the
limits are $v\sin i<90$ km/s for $T_{\rm eff}$, $v\sin i<30$ km/s for $\log
g$, and $v\sin i<70$ km/s for [Fe/H]. Comparison with benchmark stars have
shown the ROTFIT application to HR15N spectra produces strong biases for
$T_{\rm eff}>6200$ K, and therefore all ROTFIT results are disregarded in this
range. When both methods produce valid results, it was imposed that
substantial agreement exists before averaging them. Comparison with benchmarks
and results from photometry have led to the following homogenisation criteria:

  * •

The spectral index method was taken as reference $T_{\rm eff}$. The two
methods were averaged when $\Delta T_{\rm eff}<\pm 220$ K for $T_{\rm
eff}<4000$ K, $\Delta T_{\rm eff}<\pm 360$ K for $4000<T_{\rm eff}<5000$ K,
and $\Delta T_{\rm eff}<\pm 300$ K for $T_{\rm eff}>5000$ K. Otherwise only
the spectral index value is recommended.

  * •

$\log g$ are averaged only if the two results differ by $<0.3$ dex. Otherwise,
only the ROTFIT results is given if $\log g_{\rm OACT}>4.2$ and $\log g_{\rm
OAPA}>5.0$. In all other cases, no $\log g$ is given.

  * •

When both methods produce [Fe/H] the values are averaged.

Besides the ($T_{\rm eff}$, $\log g$, [Fe/H]) triad, WG12(OACT) also
recalculated radial velocities for clusters badly affected by nebular lines
(NGC2264, NGC6530, Trumpler14, NGC2451) after applying an alternative
different sky background subtraction.

The projected rotational velocity $v\sin i$ from HR15N was also recalculated
by OACT using a cross-correlation technique and masking emission features in
young stars. Changes in the spectrograph resolution were also taken into
account. Upper limits are given when the error is larger than the $v\sin i$
value itself or when $v\sin i\leqslant 7$ km s-1, which is the limit imposed
by the GIRAFFE resolution.

###  9.4 Working Group 13: OBA stars

The operation of Working Group 13 is described more fully in the article ”The
Gaia-ESO Survey: The analysis of hot-star spectra” Blomme et al. (2022).

In Working Group 13 (WG13), eight Nodes worked on the analysis of the O-, B-
and A-type spectra. These were selected from observations made with the
HR3/4/5A/6/14A GIRAFFE setups and their corresponding UVES data. Some archive
data were also processed. The temperature range covered by WG13 is large, and
most of the Nodes cannot fully cover that range. Two types of analysis were
used by the Nodes. The first one was based on a carefully selected set of
diagnostic photospheric spectral lines, and radiative transfer calculations
were used to fit the profiles in detail. The second one used the full observed
spectrum, comparing it to theoretically generated ones. In the comparison,
weights can be used to stress the importance of certain spectral regions.
After the Nodes have derived the stellar parameters, their results are
homogenised and the abundances are determined. More details of the WG13 work
are given in Blomme et al. (2022).

Table 15: Overview of the WG13 Nodes. Listed are the effective temperature
range covered by the Node, the spectral analysis technique used, the stellar
parameters that are determined, the elements for which abundances are
determined, and the number of spectra processed.

Node |  $T_{\rm eff}$ range | Technique | Parameters determined | Abundances | # spectra
---|---|---|---|---|---
ROBGrid |  $\hphantom{1}3000\,-50\,000$ K |  $\chi^{2}$ minimisation with grid |  $T_{\rm eff}$, $\log g$, [M/H], $v_{\rm rad}$, $v\sin i$aa$a$What is listed here as $v\sin i$ is actually the total line-broadening parameter, which can include other effects, such as macroturbulence. | – | 8667
|  | of theoretical spectra |  |  |
ROB |  $\hphantom{1}6000\,-12\,000$ K | Fe - Fe+ ionisation balance |  $T_{\rm eff}$, $\log g$, [Fe/H], $\xi$, $v\sin i$aa$a$What is listed here as $v\sin i$ is actually the total line-broadening parameter, which can include other effects, such as macroturbulence. | C, O, Mg, Al, Sc, Fe | 517
|  | of diagnostic photospheric lines |  |  |
MGNDU |  $\hphantom{1}5000\,-15\,000$ K | PCA and SIR |  $T_{\rm eff}$, $\log g$, [M/H] , $v_{\rm rad}$, $v\sin i$ | – | 186
Liège |  $10\,000-32\,000$ K |  $\chi^{2}$ minimisation with grid |  $T_{\rm eff}$, $\log g$, $v_{\rm rad}$, $v\sin i$aa$a$What is listed here as $v\sin i$ is actually the total line-broadening parameter, which can include other effects, such as macroturbulence. | He, C, N, Ne, Mg, Si | 696
ON |  $14\,000-33\,000$ K | Non-LTE synthesis and |  $T_{\rm eff}$, $\log g$, $v\sin i$aa$a$What is listed here as $v\sin i$ is actually the total line-broadening parameter, which can include other effects, such as macroturbulence. | C, O, Si | 184
|  | Si ionisation balance |  |  |
IAC |  $22\,000-55\,000$ K |  $\chi^{2}$ minimisation with grid |  $T_{\rm eff}$, $\log g$, $v\sin i$, $v_{\rm macro}$ | He | 268
|  | of FASTWIND models |  |  |
Mntp |  $30\,000-45\,000$ K |  $\chi^{2}$ minimisation with grid |  $T_{\rm eff}$, $\log g$, $v\sin i$, $v_{\rm macro}$ | – | 55
|  | of CMFGEN models |  |  |
LiègeO |  $20\,000-45\,000$ K | CMFGEN |  $T_{\rm eff}$, $\log g$, $v\sin i$, $v_{\rm macro}$ | He, C, N | 293

444

##### ROBGrid Node.

The ROBGrid Node used theoretical spectra from the literature and compared
them to the observations, to derive the stellar parameters ($T_{\rm eff}$,
$\log g$, and metallicity – if not too different from solar metallicity). Each
observed spectrum was compared to all theoretical spectra to find the best
fit. For each comparison, the radial and projected rotational velocities are
determined using a cross-correlation technique (David et al. 2014, their Eq.
7) with a rotationally broadened theoretical spectrum. To judge the goodness-
of-fit the $\chi^{2}$ for that comparison is calculated. The final stellar
parameters (as well as the radial and projected rotational velocity) are
determined by the theoretical spectrum having the minimum $\chi^{2}$. In a
larger loop around this fitting procedure, we also refined the normalisation
of the observed spectrum. ROBGrid does not determine errors of the stellar
parameters, but assigned errors that were derived in the homogenisation phase.
The Node also does not provide abundances of individual elements, because the
abundances that went into the literature models cannot be changed.

##### ROB Node.

The ROB Node used a three-step approach to determine the stellar parameters
and abundances. First the stellar parameters were estimated from a limited
number of diagnostic H Balmer, Fe, and Mg absorption lines. Next, the detailed
profiles of a more extensive set of diagnostic lines were fit by an iteration
over $T_{\rm eff}$, surface gravity ($\log g$), line-of-sight microturbulence
velocity ($\xi$), and metallicity ($[$M/H$]$). Finally, abundances were
measured from selected sets of sufficiently (medium-strong to) strong lines.
To explore the parameter space, we calculated a large homogeneous grid of
synthetic spectra with the LTE radiative transfer code
Scanspec555http://alobel.freeshell.org/scan.html (Lobel 2011). The error bars
range from $\sim\pm$150 K for $T_{\rm eff}<8500$ K to $\sim\pm$250 K for
$T_{\rm eff}>11\,000$ K. The metallicities and element abundances have error
bars ranging from $\pm$0.05 dex to $\pm$0.1 dex,

##### MGNDU Node.

The MGNDU node analysed data from the UVES 520 setup. The spectra were
renormalised as in Gebran et al. (2016), and the radial velocities
redetermined because a high radial velocity accuracy is important for the
MGNDU procedure (Paletou et al. 2015; Gebran et al. 2016). The procedure used
is a combination of Principal Component Analysis (PCA) and a Sliced Inverse
Regression (SIR). It starts by compiling a learning database using synthetic
spectra. The model atmospheres for this database were calculated using the
latest version of the ATLAS9 code (Kurucz 1992; Castelli & Kurucz 2003;
Sbordone et al. 2004). Synthetic spectra were calculated from these models,
using the SYNSPEC48 LTE code Hubeny & Lanz (1992) and the line list of Gebran
et al. (2016). Once the learning database has been constructed, it can be
applied to any observed spectrum to derive the stellar parameters (Kassounian
et al. 2019). The average uncertainties for the inverted parameters are around
150 K for $T_{\rm eff}$, 0.35 for $\log g$, 0.15 for [M/H], and 2 km s-1 for
$v\sin i$. No elemental abundances, other than Fe, are determined by MGNDU.

##### Liège Node

The Liège Node covers the temperature range 10 000 to 32 000 K, corresponding
to the range of B-type stars. A least-square minimisation was used to fit the
observed normalised spectra with a grid of solar-metallicity, synthetic
spectra computed with the SYNSPEC program on the basis of non-LTE TLUSTY (Lanz
& Hubeny 2007) and LTE ATLAS (Kurucz 1993) model atmospheres. We first
determined the radial velocity and projected rotational velocity
(macroturbulence is not included). The full wavelength domain was used to
determine the effective temperature and surface gravity. The typical
1-$\sigma$ uncertainties are $\sim$750 K for $T_{\rm eff}$, $\sim$0.15 for
$\log g$, $\sim$15 km s-1 for $v\sin i$ and $\sim$2 km s-1 for the radial
velocity. For the abundance determination, we considered He, C, N, Ne, Mg, and
Si. We found the best $\chi^{2}$ fit of a grid of rotationally-broadened
synthetic spectra to the observed line profiles of He i $\lambda$4471, C ii
$\lambda$4267, N ii $\lambda$4630, Ne i $\lambda$6402, Mg ii $\lambda$4481, Si
ii $\lambda$6371, and Si iii $\lambda$4568-4575, from which we derived the
abundances. The line profile calculations used the non-LTE code DETAIL/SURFACE
originally developed by Butler (1984). More details about the version of the
code currently used and the model atoms implemented are given in Morel et al.
(2006) and Morel & Butler (2008).

##### ON Node

The ON Node started by determining $v\sin i$ from the He i lines. Stellar
parameters and abundances were then determined using theoretical spectra
calculated with the fully non-LTE spectral synthesis code SYNSPEC and a new
grid of line-blanketed non-LTE model atmospheres calculated with TLUSTY
(Hubeny & Lanz 1995, 2017). The grid covers $T_{\rm eff}$ between $14\,000$
and $33\,000$ K, and surface gravity between 3.0 and 4.5. The Hydrogen lines
of the GIRAFFE spectra are compared to these theoretical spectra to find the
combinations of $T_{\rm eff}$ and $\log g$ that can reproduce the observed H
profiles. The effective temperature was then determined from the ionization
balance of Si ii-Si iii-Si iv. Next, a range of microturbulence velocity $\xi$
is explored and the corresponding abundances of C, O, and Si are determined.
By requiring the abundances to be independent of line strength, their final
values are derived. The uncertainties on the individual parameters are $1000$
K for $T_{\rm eff}$, $0.15$ for $\log g$, $15$ % for $v\sin i$, and $2$ km s-1
for $\xi$.

##### IAC Node

The IAC node analyses the O and early B-type stars using large grids of
synthetic spectra computed with the non-LTE FASTWIND stellar atmosphere code
(Santolaya-Rey et al. 1997; Puls et al. 2005). First, a Fourier transform plus
goodness-of-fit method determines the projected rotational velocity and the
macroturbulent broadening. A $\chi^{2}$ approach is then used to compare the
observed diagnostic lines (H$\alpha$, H$\gamma$, H$\delta$, He i
$\lambda$4387, He i $\lambda$4471, He i $\lambda$4713, He i+ii $\lambda$6678,
He ii $\lambda$4541 and He ii $\lambda$4686) to the theoretical spectra. From
this we determine effective temperature ($T_{\rm eff}$), surface gravity (log
$g$), Helium abundance ($N_{\rm He}/N_{\rm H}$), microturbulence ($\xi$),
wind-strength parameter666$Q$ is defined as $Q=\dot{M}/(v_{\infty}R)^{1.5}$
(Puls et al. 1996), where $\dot{M}$ is the mass-loss rate, $v_{\infty}$ the
terminal velocity of the wind, and $R$ the stellar radius. ($Q$) and the
exponent of the wind velocity-law777The velocity in the stellar wind is given
by $v(r)=v_{\infty}(1-R_{\ast}/r)^{\beta}$, where $R_{\ast}$ is the stellar
radius of the star. ($\beta$). Typical uncertainties are of the order of
$\pm$1000 K in $T_{\rm eff}$, $\pm$0.10 in log $g$, $\pm$0.15 in log $Q$ and
$\pm$0.03 dex in Helium abundance.

##### Mntp Node

The Mntp node relied on a pre-computed grid of synthetic spectra, calculated
with the non-LTE code CMFGEN (Hillier & Miller 1998). The projected rotational
velocity of each star was derived from the Fourier transform of Si iii
$\lambda$4552 and/or He i $\lambda$4713\. By convolving the theoretical
spectra with the instrumental profile, the rotational profile and a radial-
tangential macroturbulence profile, the value for the macroturbulence was
determined. A $\chi^{2}$ approach was used to find the best-fit spectrum,
concentrating on those spectral lines that are sensitive to effective
temperature and surface gravity. Typical uncertainties are 2500 K on $T_{\rm
eff}$ and 0.15 on $\log g$.

##### LiègeO Node

The LiègeO Node used the CMFGEN non-LTE atmosphere code (Hillier & Miller
1998) to analyze the O- and early B-type stars. A grid of CMFGEN models was
constructed covering the range $T_{\rm eff}=27,000$ to $36,000$ K, and surface
gravity 3.0 to 4.3. These models include the effect of the stellar wind. The
spectra are convolved with a rotation profile and a radial-tangential
macroturbulence profile. For the O-type stars, the ionization balance between
He i and He ii is used to determine the effective temperature. For B-type
stars, we used Si iii and Si iv, with He i $\lambda$ 4471 and Mg ii $\lambda$
4481 as a secondary diagnostic. Carefully selected lines of Carbon and
Nitrogen were used to determine the abundances.

##### Homogenisation

Once the different nodes have delivered the stellar parameters, the results
were homogenised. Node results for the benchmark stars are used to introduce
corrections, but this could only be applied for the ROBGrid results as the
other nodes did not analyse a sufficient number of benchmark stars. The
recommended values for the stellar parameters are then determined as a
weighted sum of the different node results (for details, see (Blomme et al.
2022)). Contrary to the practice in other Working Groups, these recommended
stellar parameters values are not used in determining the abundances; instead
the Node stellar parameters are used. Abundances are determined for only a
small number of stars, with little overlap between the nodes. Where there was
overlap, the recommended abundances are determined by a straight average of
the node abundances.

###  9.5 Working Group 14: Non standard objects, quality flags

The aim of Working Group 14 (WG14) was to identify and characterise outlier
objects. The majority of outlier objects were initially steps up the data
processing and verification learning curve, and after improvement and
iteration became obsolete. This was an invaluable contribution to survey
quality control. For further information see Van Eck & et al. (2022). WG14
scanned all Gaia-ESO Survey spectra, using a mix of goodness-of-fit spectrum-
matching outputs, and diagnostic analyses such as the shape of cross-
correlation functions, looking for outlying data. the most significant are
outlined below. In order to detect and characterise an anomaly most
efficiently, a dedicated dictionary has been defined. This dictionary is
briefly described in Sect. 9.5.1. WG14 could then systematically investigate
specific types of outlying features potentially affecting all Gaia-ESO data
(i.e. whatever the object spectral type is), namely emission line objects and
binary/multiple objects, as described below (Sect. 9.5.2 and Sect. 9.5.3,
respectively). Science verification illustrations of the value of the work of
WG14 are available in: ”The Gaia-ESO Survey: Catalogue of H$\alpha$ emission
stars” (Traven et al. 2015), ”The Gaia-ESO Survey: double-, triple-, and
quadruple-line spectroscopic binary candidates” (Merle et al. 2017), and ”The
Gaia-ESO Survey: detection and characterisation of single-line spectroscopic
binaries” (Merle et al. 2020).

####  9.5.1 Gaia-ESO Survey dictionary

##### The complete dictionary

During analyses many nodes and Working Groups were flagging various types of
outliers but with idiosyncratic naming conventions. It seemed desirable to use
a common dictionary. This dictionary should convey information that is at the
same time useful, exact and exhaustive for Gaia-ESO Survey-consortium data-
processing users, but also for later use external to the survey consortium,
without demanding too-much specialist project-specific knowledge. It was
primarily the task of WG14 to flag outliers in such a way.

This dictionary has been defined with three main sections. First, a technical
section (TECH) listing mainly data and data-product issues: data reduction,
S/N, analysis issues and result quality issues, separately for parameter and
abundance determination issues. These flags have been very useful to trace
back data reduction problems or main analysis problems encountered by the
nodes, and so in many cases the problematic flagged data has been able to be
replaced by improved-quality data. Second, a peculiarity (PECULI) section,
listing peculiarities possibly affecting the spectra, was introduced, listing
for example binarity or emission line characterisation, as well as anomalous
line and molecular absorption. Third, a comment (Remark) section was included,
listing specific classes of stars, so that if any WG and nodes, in the course
of their analysis, recognised a peculiar type of object, they could flag it as
such. This section was however not used as much as the two other ones. A
3-level confidence flag (A,B,C), complementing each flag, has been introduced.
For each data release, a first-pass analysis was performed beforehand,
flagging most prominent issues, so that this information could be conveyed to
the various nodes during their analysis phase.

##### The simplified dictionary

In the final data release of recommended results, a significant number of
flags are potentially available. A compromise is needed between providing too-
much information to be helpful for an end-user, and failing to flag important
information. In the event WG15 compromised with a simplified set of flags.
These focus on the processing and quality-control issues noted above, and some
astrophysically-interesting products, discussed below. The larger set of
information is available in the full node-level information also made
available.

####  9.5.2 Emission line detection and classification

Automated fits to the profile of detected emission lines are feasible. The
most relevant for Gaia-ESO is H$\alpha$. The fitting used two independent
Gaussian profiles and a third component accounting for nebular emission
allowed distinction of several morphological types of H$\alpha$ line profiles
with the introduction of a simplified classification scheme. The spectra were
sorted into eight distinct morphological categories: single component
emission, emission blend, sharp emission peaks, double emission, P-Cygni,
inverted P-Cygni, self-absorption, and emission in absorption. A quantitative
discussion of the degree of variability of H$\alpha$ emission profiles, which
is expected for young, active objects, was also possible thanks to multiple
observations. As proof of principle of the utlity of this approach, Traven et
al. (2015) discussed the properties of H$\alpha$ emission stars across the
sample of 22035 spectra from a Gaia-ESO Survey internal data release after 22
months of observation. These are observed with the GIRAFFE instrument and
largely to stars in young open clusters. A catalogue of stars with properties
of their H$\alpha$ emission line profiles, morphological classification,
analysis of variability with time and the supplementary information from the
SIMBAD, VizieR, and ADS databases was published. The relevant flags and
parameters are published.

####  9.5.3 Binarity and radial-velocity variability detection

##### SB1

Although Gaia-ESO was not designed as a radial-velocity survey, most stars are
observed more than once, with the subsequent radial velocity determinations
being checked, and as appropriate flagged, for variability at the pipeline
processing stage (Sect 8. In WG14 a more careful analysis was carried out. The
aim, apart from intrinsic interest, is to identify multi-component spectra and
to alert other WG/nodes analyses that atmospheric parameters should not be
used for these system components; SB-specific pipelines should be used
instead. This proceeded in two stages. The first involved analysis of the
shape of the pipeline-generated cross-correlation functions (CCF). The CCF
will be distorted from symmetry if stacking spectra uncorrected for
unrecognised but true radial-velocity variations. This prompted an
investigation of the individual spectra radial velocity determinations,
looking for evidence for spectroscopic binaries with one visible component
(SB1). The Gaia-ESO Survey internal data releases were investigated to
identify and characterise SB1. A statistical $\chi^{2}$-test was performed on
the CCF’s for stars characterised by at least two observations and a signal-
to-noise ratio larger than three. The resulting sample of candidate RV
variables was cleaned from contamination by pulsation/convection-induced
variables using Gaia DR2 parallaxes and photometry. Monte-Carlo simulations
using the NASA/HEASARC SB9 catalogue of spectroscopic orbits allowed
estimation of the detection efficiency. Thus we could correct the SB1 rate to
evaluate the Gaia-ESO SB1 binary fraction and its dependence on effective
temperature and metallicity. This analysis remains to be completed for the
full Gaia-ESO dataset, but for a proof of principle analysis using part of the
data (Merle et al. 2020) found 641 (resp., 803) FGK SB1 candidates at the
5$\sigma$ (resp., 3$\sigma$) level. The orbital-period distribution was
estimated from the RV standard-deviation distribution. After correcting for
the detection efficiency and selection biases, the SB1 frequency could be
estimated, and its dependence on metallicity and spectral type could be
constrained .

##### SB2

For double-lined and higher-multiplicity spectroscopic binaries one similarly
can detect and flag binarity/multiplicity (SBX, X$\geq 2$) for stars from the
cross-correlation function (CCFs) of the Gaia-ESO spectra with spectral
templates. Due to the large number of spectra, WG14 automated the task
analysing the successive derivatives of the CCF. The use of successive
derivatives allowed the de-blending of multicomponent CCFs. The code written
to perform this task provides: i) the number of peaks detected in the CCF, ii)
radial velocities corresponding to the peak maxima.

As a proof of principle this method was applied (Merle et al. 2017) on the
fourth Gaia-ESO internal data release and allowed the detection of 354 SBn
candidates (342 SB2, 11 SB3, and even one SB4), of which only nine candidates
were known in the literature. This implies that about 98% of these SBn
candidates are new, illustrating the known incompleteness of such studies at
faint magnitudes. Among the SB2 candidates, detailed analysis (including
follow-up observation on the SALT telescope) of the unique SB4 (four peaks in
the CCF) reveals that CNAME 08414659-5303449 (HD 74438) in the open cluster IC
2391 is a physically bound stellar quadruple system. This very rare discovery
indicates the success of teh approach.

Building on this success, Van der Swaelmen & et al. (2022) (see also Van der
Swaelmen et al. 2018) improved the detection by designing specific cross-
correlation masks (called NACRE masks) which produce more narrow CCFs, and
therefore allow detection of SB2 with a smaller radial velocity difference.
Our investigation showed that the HR21 wavelength range, around the near-
infrared Ca II triplet, tends to host numerous strong and saturated lines
(compared to the HR10 wavelength range) that broaden the CCFs. The new NACRE
masks exclude these strong features to keep only weak, mildly-blended atomic
lines and produce more narrow CCFs. We therefore re-computed the CCFs for the
approximately 150000 individual HR10 and HR21 spectra and analysed them with
DOE. The resulting flags are in the released dataset.

####  9.5.4 t-SNE flagging

The t-SNE node employed a semi-automatic approach to classification of
spectra. This means that we manually assigned labels to groups of spectra that
share a similar morphology, where the groups of spectra are revealed by the
dimensionality reduction (clustering) technique t-SNE (for more details see
Traven et al. 2017). Our intention was not to provide highly reliable
classification labels for every spectrum, rather we identified most of the
common outstanding peculiarities in the whole dataset, with spectra labelled
up to the limits of our semi-automatic procedure. The benefit of this method
is the ability to discover different kinds of unexpected
peculiarities/features/issues as well as provide a clear overview of the
structure of the whole spectral dataset (distinction between dwarfs/giants,
hot/cool stars, etc.).

We produced classification results for the four Gaia-ESO setups (HR14A, HR15N,
HR21, UVES-U580) that displayed at least a few well defined and interesting
groups of spectra after analysis. We emphasise that this is not an exhaustive
classification, since a person is needed to identify and name the different
peculiar groups, however apart from that the t-SNE framework developed allows
for a very efficient inspection and also updates to the classification as the
dataset grows with time.

One of the strongest and most abundant features that we recognised in the
t-SNE spectral classification are incorrectly subtracted nebular emission
lines (HR14A, HR15N), along with other reduction issues. Some interesting
objects with intrinsic emission in the H$\alpha$ and CaII lines are likewise
revealed by t-SNE. We also provided an interactive interface to the t-SNE maps
dubbed the “GES Explorer”, which is available internally to the Gaia-ESO
collaboration, and can serve as a powerful exploratory tool for the Gaia-ESO
dataset. In that way this t-SNE effort provides flags of value, particularly
for analyses of young stars.

##  10 Survey parameter homogenisation WG15

Fuller information on the work of WG15 can be found in Hourihane & et al,
(2022). The core team members were Patrick Francois, Anais Gonneau, Anna
Hourihane, Laura Magrini and Clare Worley.

The aim of this WG was to ensure that the data products generated by the
spectrum analyses are coherent, the resulting stellar atmospheric parameters
and abundances homogenous, the parameters are calibrated onto an identified
(set of) external calibrator objects, and the process is fully documented,
supporting the data releases. This homogenisation process is a key aspect of
analysis quality control, and proceeds through a double iteration. The first
part of this homogenisation process is done inside each Working Group, where
in particular the outputs of the different node-based analysis tools are
compared and combined. Bringing together a final “best” parameter set
requires, in addition to the internal analyses, careful analysis of
calibration targets, and targets observed in more than one setting and
instrument. In general systematic calibration scale and zero offsets between
analysis groups, and between target classes and fundamental benchmark stars
were found to be small by the end of the survey, for the final processing
iteration. during the survey teams identified many causes of systematics and
mitigated them. Some remain, as for example in radial velocity zero point
offset between setups as discussed in Sect 8 and illustrated in Fig 11 and Fig
12. All systematic remaining offsets are corrected. The sanity check on the
outcome of this on the HRD scatter pre- and post-processing is shown in Fig 4
above.

The homogenisation process is based on the homogenised results issued by the
working groups WG10, WG11, WG12 and WG13 and the flags from WG14. During the
first internal release, the results released by the WGs were obtained per
spectrograph setup and not per individual object (cname). We verified that the
benchmark star results (stellar parameters) coming from the different setups
gave consistent results.

For later data releases the homogenisation process was performed in two steps.
First we homogenised the stellar parameters, namely $T_{\rm eff}$, $\log g$,
${\rm[Fe/H]}$ and $\xi$, and delivered to the WGs a file containing all the
targets with an homogenised set of stellar parameters. From that parameter
set, updated abundances were computed by the nodes of the different WGs and
the resulting abundances were set on the same scale by the WGs. The resulting
updated abundance files then came back to WG15 to perform the element by
element abundance homogenisation. Two more steps are needed to produce the
final WG15 file, radial velocity homogenisation and the homogenisation of the
flags.

###  10.1  Stellar parameter homogenisation

The aim of the homogenisation process is to put the astrophysical parameter
results of the analyses of the different Working Groups WG10, WG11, WG12 and
WG13 on a common scale. To allow a calibration of the work of the different
WGs, Gaia-ESO has been designed to have several sets of stars in common
amongst the analysis WGs. A set of FGK benchmark stars is analysed by all WGs
except WG13 (hot stars) for which a special set of benchmark stars has been
used. The benchmark stars have their atmosphere fundamental parameters
determined by independent means. They are used as reference for the parameter
scale. In addition to using the stars in common between WGs to perform the
homogenisation process, we also used the objects (open and globular clusters)
that have been observed and processed by several WGs. In particular, a set of
clusters referenced as calibration clusters has been observed in a range of
instrumental setups to aid in inter-setup and inter-WG calibration. For
example, the stars in NGC 6705 are observed in all setups, and are in common
to all WGS, thus making this cluster a fundamental inter-calibrator for the
whole survey.

####  10.1.1 Absolute parameter scale

The first step in the homogenisation process is to map the results of each WG
on to the absolute external scale represented by the benchmark stars and to
ensure internal consistency of the WG results with this scale. For the WG10
results, checks are performed to ensure that good agreement is found between
the joint analysis of the HR10-HR21 setups and the literature results for the
benchmark stars. Care has also been taken to verify that the other WG10 setups
(HR21 alone, HR15N and HR09B) are mapped onto the HR10-HR21 scale. For WG11,
we checked the agreement of the results for both setups U520 and U580. We also
searched for potential corrections among the three setups U520 U580 and HR15N
that have been used by WG12 for the analysis of the benchmark stars. Several
warm benchmark stars added during the project have been used by WG13 to set
their scale on the literature data. Stars in common amongst the WG13 setups
were also considered to check the internal consistency of the WG13 results. A
priority order has been recommended by WG13, based on their evaluation of the
reliability of the outcomes, and adopted by WG15 for their results. The
adopted sequence is” HR3—HR5A—HR6—HR9B—HR14”,
”HR5A—HR6—HR9B—HR14A”,”HR3—HR15A—HR6”,”HR14”,”HR9B” and ”U520”.

####  10.1.2 Relative parameter scale

Once the WGs results have been mapped onto the benchmark scale, the results of
each WG for the whole sample of stars need be checked with respect to each
other. As the full set of stars observed by Gaia-ESO covers a larger range of
stellar parameters than the benchmark stars, it is mandatory to perform a
series of checks. To evaluate any possible offsets between WGs we use the
stars in common between different WGs that give us a direct estimate of any
differences between WGs’ results.

A second test is to plot the Hertzprung Russell diagram of Milky Way stars
from the different WGs in the same metallicity range and compare these
distribution with the theoretical : see Fig 4 above for an example.

A third test uses the member stars in open and globular clusters, which are
both considered to be composed of chemically homogenous populations. Clusters
are particularly important in the process of homogenisation as they allow us
to put stars that are not common between WGs on a common scale as hot, massive
cluster stars and pre-main sequence stars.

Globular clusters are important as they cover a wide range in metallicity for
which both GIRAFFE and UVES observations were completed. They were
investigated for $T_{\rm eff}$, $\log g$, ${\rm[Fe/H]}$ offsets between U580
and HR10+HR21 samples.

Open clusters analysed in Gaia-ESO can be divided into two categories,
intermediate-age/old clusters (with ages $\leq$ 100 Myr up to several Gyr) and
young clusters with younger ages that may have massive stars (mass $\leq$ 8
$M_{\odot}$). To allow a comparison of the results of the different WGs and a
final homogenisation of the whole Gaia-ESO Survey results, several open
clusters are observed in more than one setup and are analysed by several WGs.
These so-called intercalibration clusters give a solid basis to perform the
comparison of the results between different WGs and different setups. The best
example is the open cluster NGC 6705 analysed by the four WGs as noted above.
For both open and globular cluster, an important check is to estimate
qualitatively the agreement with a theoretical isochrone (PARSEC).

The homogenisation and the offsets applied to the different WGs results follow
a sequence described in detail in Hourihane & et al, (2022). If a star has
results in more than one WG, then the selection of the recommended set of
parameters is based on the setup used and/or the competence of each on the
analyses on that type of star. An example is that the results for stars with
$T_{eff}\geq 7000K$ all come from WG13. Another example is to give priority to
the results coming from UVES over GIRAFFE spectra. For multiple exposures of
the same benchmark stars, we have selected the one with the highest signal-to-
noise ratio (S/N).

###  10.2  Abundance homogenisation

The task of the abundance homogenisation is based on the determination of the
offsets in the abundances of the individual elements between the different
WGs. Stars in common between WGs or cluster member stars observed by several
WGs are used to evaluate these offsets.

The abundances were determined by the analysis nodes and WGs based on the
recommended parameters that were the product of the WG15 homogenisation. As
the large wavelength range and the high resolution of the UVES spectrograph
permit a more precise determination of the stellar parameters and abundances
than GIRAFFE spectra, the homogenisation process for the WG10 abundances uses
the WG11 results as the baseline. WG13 results are treated separately as the
hot stars are suspected to have abundance anomalies and do not follow the
general trends found in FGK stars. The lithium abundance is also treated
separately. For the other elements, the first step is to check the solar and
the benchmark abundances and look for possible offsets with respect to the
literature values. The abundances measured in the stars in common between WGs
are then compared to check for possible offsets. The abundance ratios vs
$[Fe/H]$ determined in the open clusters (both calibration and science open
clusters) are then computed for the different WGs to check for anomalies. The
same work is also performed on globular clusters. Median elemental differences
between WG10 and WG11 are determined and used to find offsets and/or trends as
a function of metallicity. The Milky Way stars abundances are also compared to
literature data (Bensby et al. 2014; Battistini & Bensby 2015, 2016; Reddy et
al. 2006; Pereira et al. 2017; Takeda et al. 2016) and checked for offsets and
trends as a function of metallicity.

###  10.3  Radial velocity homogenisation

The Gaia Radial Velocity standard stars have been investigated as part of the
homogenisation of the Gaia-ESO radial velocities and the Gaia-ESO HR10 RVs
have been found to have zero offset from the reference values for these stars
(Soubiran et al. 2018). Therefore, HR10 RVs were taken as the zero point for
the Gaia-ESO RVs. The HR10 RVs come from the pipeline of Sect 8. Only RVs from
the stacked, singlespec spectra from which the stellar parameters and
abundances were determined are considered here. For investigation of the RV
variations between individual or nightly stacked spectra, see Jackson et al.
(2015) and Sect 9.5.3.

Offsets were calculated between each of the setups and the zeropoint of the
Gaia-ESO RV scale, HR10. The offsets were then applied to put the other setups
onto the HR10 scale. For WG13, RVs have been based on a combination of WG13
setups.

Where the HR10 setup was not available, the radial velocity was sourced from
the same setup as that from which the parameters were selected. Potential
offsets were applied when the RVs were taken from a different setup to HR10.
These offsets are calculated on the basis of stars in common between the
setups. The order of priority in which RVs from the different setups were
selected was: HR10, HR15N, U580, HR21, HR9B, HR3, HR5A, HR6, HR14A, HR14B,
U520, HR4, HR5B.

###  10.4 Flag homogenisation

The homogenisation is based on the dictionary of flags produced by WG14. We
compared the flags produced by the different WGs and searched for possible
conflicts. In general, all the flags from the WG Recommended files are
included with any duplicates removed. Details can be found in Hourihane & et
al, (2022).

##  11 Survey progress monitoring and data publication (WG16, WG17, WG18)

Survey progress monitoring is a major task, sufficiently critical to efficient
survey progress that the relevant WG (WG16) was led by the Co-PIs directly,
ably supported by the dedicated Project Office team. Survey progress is a
complex mix of management, communications, and book-keeping. Management
involves monitoring the progress of all WGs involved in data preparation,
processing, and analysis. The key aspect for this WG was the book-keeping.
Reliable and quantitative information must track, for every target star, the
number of observations attempted, achieved, and still awaited. The processing
status of each observation must be tracked, and updated as each WG deposits
data in the operational database. SNR data for each spectrum, SNR data for
each object, including repeats and different settings, and necessary
additional information prior to object readiness for science analysis, must
all be maintained for all $10^{5}$ targets. For the clusters the same
information must be maintained both for the individual targets and for the
clusters as a whole. In particular, the fractional completion of data taking
and processing for each cluster must be tracked.

Internal data management of data products was designed around the ESO FITS raw
data structure. The extracted, wavelength calibrated and sky subtracted
spectra are in a 2d “image” with the corresponding fibre information in binary
table extensions. Processing and quality control information is propagated
through the FITS header. The outputs from all later stages of processing are
incorporated in further binary table extensions. This model, where all
relevant information about an observation is kept in the same container file,
was developed by the Cambridge Astronomical Survey Unit (CASU) team for the
VISTA pipelines, and cuts bookkeeping tasks down to a minimum.

###  11.1 Operational database (WG17)

The Gaia-ESO project utilised both an operational database, based in Cambridge
at CASU, and a dedicated archive, based in Edinburgh, to hold all relevant
information, to complement the primary public stable archive, which is that of
ESO. This approach builds on the operational VISTA (and other projects)
systems hosted at CASU, Cambridge, and at the Wide Field Astronomy Unit
(WFAU), Edinburgh. It is a proven and successful model, and proved essential
to support the survey data flow. In particular, this approach kept separate
the day-to-day processing, the spectrum analyses, and all activities in which
data are being determined or updated, from all those science activities which
should be based on readily accessible static information.

The operational database held all data while it remained incomplete, or
subject to change. All observation preparation WGs submitted all relevant data
associated with target selection, up to and including OB preparation, to the
operational database [WG1-WG6]. Raw data from ESO were added. Pipelines
[WG7-WG9] operated on the raw data, generated pipeline reduced and extracted
spectra, with associated variances, quality control (QC) info, RVs and
classification outputs, and wrote these back to the operational database. An
aspect of the QC is the quantitative SNR data, also supplied to ESO through
the regular survey progress reports. Spectrum analysis groups [WG10-WG15]
read, but do not modify, these spectra, carry out their analyses, and deliver
back FITS-table results to the operational database. These tables are attached
to each relevant spectrum, allowing the progress monitor to be updated.

Subsequent processing stages collated and incorporated available existing
information such as photometric indices, proper motions, and also appended
derived radial velocities and stellar atmosphere parameters in additional FITS
table extensions. Further processing stages involving common tasks such as
continuum estimation and normalisation to unity are readily incorporated by
including extra FITS extensions. The philosophy is to keep the same file
architecture throughout and minimise bookkeeping and versioning issues by
always attaching information directly to the files. This also has the
advantage of removing direct dependency on availability of access to external
databases. To minimise resampling of spectra we also maintained extracted
spectra in natural units (e.g. pixel-based fluxes) and use FITS table
extensions and/or FITS header information to specify conversions to physical
units, should this ever be appropriate and required, and/or to zero-velocity
systems.

We use the Gaia DPAC model - all data are stored in the central repository,
taken out for use/analysis, and have parameters returned. No process adjusts
the spectra, except by resetting the master spectrum if needed when the whole
process restarts from scratch. No data processing WG talks directly to the
archive. When the spectrum teams agree their job is converged for some source,
all relevant data become fixed.

At this stage all data are copied to the permanent Gaia-ESO archive, hosted at
WFAU Edinburgh, and become available internally to the survey consortium to
begin science quality control.

###  11.2 Internal survey archive (WG18)

After pipeline processing, including atmospheric parameter and abundance
determination etc., the spectroscopic survey data were made available to the
consortium for quality control, science verification and preliminary analysis
via a bespoke archive system. This system acts not only as the ‘internal’
archive system for the consortium, but also as a publicly accessible portal
that provides enhanced database–driven products to facilitate world community
exploitation of prepared static releases of survey data. Metadata associated
with the products available in the archive complies with the corresponding VO
Data Models, in particular with the VO Spectrum Data Model. This follows the
tried–and–tested Vista Data Flow System (VDFS) model developed by Cambridge
and Edinburgh, for UKIRT–WFCAM, VISTA-VIRCam and VST survey data, and supports
both internal team science verification, and provides a global archive system
complementary to that provided by ESO.

The Gaia-ESO Survey archive design follows proven models, and includes the
following features:

  * •

back–end relational database management system store

  * •

a near–normalised relational design to track all data, metadata and provenance
through the pipeline and subsequent analysis stages

  * •

simple interface applications for the novice user

  * •

tabular data (i.e. catalogues) available through ConeSearch protocols

  * •

SQL interface and relational model exposed to users through interactive web
forms for more complicated usage scenarios

  * •

integration of multi–wavelength catalogue data and image thumbnails

  * •

publication of spectroscopic data to the VO through the Simple Spectral Access
Protocol

  * •

publication of all tabular data (e.g. input catalogues, derived physical
quantities for targets etc.) to the VO through the Table Access Protocol
(thereby making them accessible to sophisticated client–side exploration and
analysis utilities such as TOPCAT)

  * •

finally, cross-linking to the Gaia EDR3 star identifications is provided in
the ESO SAF, while updated cross-matching to the Gaia-ESO CNAME identifiers
will be provided as a Gaia data product with future Gaia data releases.

The internal CASU Gaia-ESO archive is hosted at Cambridge/CASU. 888
http://casu.ast.cam.ac.uk/gaiaeso/

The public Gaia-ESO Survey archive is Edinburgh/WFAU. 999
http://ges.roe.ac.uk/

The primary public data archive is the ESO Science Archive Facility. 101010
http://archive.eso.org/cms.html

##  12 Gaia-ESO Survey public data products

This section introduces the final data release content. A fuller description
of this is provided in the companion paper (Randich et al. 2022), while the
detailed final set of homogenisation processes is described in the WG15 paper
Hourihane & et al, (2022).

All spectra and derived parameters and abundances are available through the
ESO Science Archive Facility 111111http://archive.eso.org/cms/data-portal.html
and the dedicated WFAU portal. 121212 http://ges.roe.ac.uk/pubs.html

Preliminary data releases have been available through these archives during
the survey. The set of reduced spectra, apart from data for 592 objects now
also released, have been available through the ESO SAF since December 2020.

Summary survey statistics are provided here in Table 16 for the relative
numbers of survey and archive spectra, Table 17 for the total number of
spectra by spectrograph setup, and Table 18 for the cross-matches between
setups.

Table 16: Gaia-ESO Survey Final Data Set: numbers of spectra  | Gaia-ESO | ESO Archive | Total Spectra
---|---|---|---
UVES | 14484 | 1954 | 16438
GIRAFFE | 178698 | 7097 | 185795
TOTAL | 193182 | 9051 | 202233
Table 17: Gaia-ESO Survey Final Data Set: numbers of unique sources by setup  Total | U580 | U520 | HR10 | HR21 | HR15N | HR3 | HR4 | HR5A | HR5B | HR6 | HR9B | HR14A | HR14B
---|---|---|---|---|---|---|---|---|---|---|---|---|---
115614 | 6641 | 500 | 59722 | 66542 | 40973 | 2228 | 1253 | 2072 | 107 | 2121 | 3873 | 2252 | 107
Table 18: Gaia-ESO Survey Final Data Set: number of cross-matches by setup | U520 | HR10 | HR21 | HR15N | HR3 | HR4 | HR5A | HR5B | HR6 | HR9B | HR14A | HR14B
---|---|---|---|---|---|---|---|---|---|---|---|---
U580 | 110 | 171 | 249 | 869 | 31 | 9 | 26 | 1 | 26 | 162 | 70 | 1
U520 | - | 55 | 61 | 77 | 22 | 0 | 22 | 1 | 22 | 104 | 22 | 1
HR10 | - | - | 58354 | 3834 | 181 | 167 | 181 | 1 | 181 | 253 | 211 | 1
HR21 | - | - | - | 3805 | 181 | 167 | 181 | 1 | 181 | 253 | 211 | 1
HR15N | - | - | - | - | 259 | 245 | 259 | 1 | 259 | 794 | 286 | 1
HR3 | - | - | - | - | - | 1250 | 2069 | 107 | 2114 | 468 | 2045 | 107
HR4 | - | - | - | - | - | - | 1198 | 106 | 1249 | 289 | 1185 | 106
HR5A | - | - | - | - | - | - | - | 57 | 2066 | 469 | 2046 | 57
HR5B | - | - | - | - | - | - | - | - | 107 | 55 | 57 | 107
HR6 | - | - | - | - | - | - | - | - | - | 471 | 2047 | 107
HR9B | - | - | - | - | - | - | - | - | - | - | 481 | 55
HR14A | - | - | - | - | - | - | - | - | - | - | - | 57
Table 19: Gaia-ESO Survey Final Data Set: elemental abundances determined  He1 | C1 | C2 | C3 | C-C2 | N2 | N3
---|---|---|---|---|---|---
N-CN | O1 | O2 | Ne1 | Na1 | Mg1 | Mg2
Al1 | Al2 | Si1 | Si2 | Si3 | Si4 | S1
Ca1 | Ca2 | Sc1 | Sc2 | Ti1 | Ti2 | V1
Cr1 | Cr2 | Mn1 | Co1 | Ni1 | Cu1 | Zn1
Sr1 | Y2 | Zr1 | Zr2 | Mo1 | Ba2 | La2
Ce2 | Pr2 | Nd2 | Sm2 | Eu2 |  |

The survey yielded 185795 GIRAFFE spectra for 108473 unique stars and 16438
UVES spectra for 7141 stars (see Table 16) , most of which were observed at
two different epochs. The products delivered at the end of the survey include
all the extracted spectra, with relevant ancillary information, and value
added deliverables.

Each source has a unique CNAME identifier, in standard coordinate format, and
is cross-matched to the Gaia EDR3 identifiers. The Gaia cross-match will be
maintained as part of future Gaia data releases.

The final data release includes, for all targets with completed observations:

Advanced Data Products - reduced data  These are the outputs of Working Groups
0-9.

  * •

One-dimensional, wavelength calibrated, sky-subtracted, normalised, UVES
and/or GIRAFFE spectra for each survey target. Where no RV variability is
detected, co-added sum spectra are provided, in addition to single-epoch
spectra. UVES spectra are provided as sets of single echelle orders and a
merged spectrum

  * •

The associated variance spectrum

  * •

Associated quality control information

  * •

Supplementary value-added data include:

  * •

The photometry (and additional membership information for clusters) used to
select the targets

  * •

The class of target - cluster, standard, etc

  * •

Selected matched multi-wavelength photometric data where available

  * •

Object classification

  * •

Radial velocity and its error distribution function

  * •

Analysis for RV variability

  * •

Projected rotational velocity and error estimate (where relevant)

Advanced Data Products - astrophysical parameters Advanced data products from
expanded and refined spectral analysis, calibrated using the current Gaia-ESO
calibrations. These are the outputs of iterative homogenisation and quality
control involving Working Groups 10-15, and survey consortium science
verification analysis.

  * •

Whenever possible stellar astrophysical parameters: effective temperature,
surface gravity

  * •

Whenever possible stellar metallicity [Fe/H]

  * •

Whenever possible [$\alpha$/Fe] ratios

  * •

Measurements of stellar activity or mass accretion/ejection rates, for cluster
members (where relevant)

  * •

Quantitative mass loss estimates, for early-type stars

  * •

Elemental abundances, with the specific elements depending on target type.

  * •

Quantitative uncertainties on the delivered quantities, derived from the
multiple reduction systems implemented

The final data-release includes homogenised values for all deliverables listed
above for all stars, calibrated onto the final Gaia-ESO calibration system.
Halpha, tSNE and BIN flags are allocated to 20,019, 17,408 and 1865 sources
respectively. Elemental abundance determinations are provided as listed in
Table 19. Note that in the final abundance release, we do not provide Fe1 and
Fe2 abundances and we leave only [Fe/H] to indicate the iron abundance. The
[Fe/H] parameter is homogenised together with the other stellar parameters
($T_{\rm eff}$, $\log g$, [Fe/H], $\xi$, $v\sin i$), while Fe1 and Fe2 are re-
computed by Nodes keeping the input stellar parameters fixed. Since the
stellar parameters, including [Fe/H], are homogenised using external
calibrators, in some cases, the recomputed abundances do not completely
fulfill the ionization balance, and there might be differences in Fe1 and Fe2
(in some specific areas of the parameter space, in particular at high
metallicity). To avoid misleading results, we provide only [Fe/H].

Discussion of the astrophysical quality and value of the parameters is
described in Randich et al. (2022), while an analysis of the precision of the
parameters, and external comparisons is in Hourihane & et al, (2022). An
example of the power of combining Gaia-ESO and Gaia information in
astrophysical analyses is provided by Jackson et al. (2022), who provide
membership analysis for 63 open clusters and 7 globular clusters.

##  13 Conclusions

The Gaia-ESO Public Spectroscopic Survey has been a large ambitious VLT
spectroscopic survey of representative samples of the main Galactic stellar
populations. One primary aim was to provide high-quality astrophysical
parameters, radial velocities to complement Gaia kinematics, and elemental
abundances for stars of all accessible ages and abundances, with parameters
firmly anchored to open and globular clusters. This links field star
properties to tested isochrones, establishing a basis to map temporal as well
as spatial evolution in the Galaxy. A second aim was to provide consistent-
quality spectroscopic studies of a large sample of open clusters, mapping
accessible age-metallicity-location space. For each cluster a wide mass-range
is accessed, linking the complementary analysis approaches required for hot
and cool stars, (pre-)main sequence and evolved, ensuring consistency. A third
aim was to provide a large sample, of order $10^{5}$, field stars, sufficient
to map populations across chemical abundance and kinematic properties. A
fourth aim was to establish a robust set of calibration stars, suitable for
use both in Gaia calibration, and for cross-calibrations between the several
recent, current or planned stellar spectroscopic surveys and asteroseismic
projects. Another ambition was to bring together the many European stellar
spectroscopy groups into successful partnership, providing a robust community
foundation for those forthcoming survey projects. Finally, a requirement is
that all the data, calibrated spectra and derived parameters, be available in
a free public archive for future uses.

Gaia-ESO succeeded in all these ambitions. The ambitious target of $10^{5}$
field stars and $\sim 60$ open clusters were surveyed, with 114325 stars and
62 open clusters newly surveyed. A very wide range of parameter space was
mapped. Substantial scientific advances have been made, with over 100 team
science verification refereed papers already published. The spectra are
already widely accessed from the ESO SAF, with the astrophysical parameters
now also available. The community did come together, learning how to
homogenise data from very many different analysis pipelines in a way
consistent with the primary Gaia Benchmark Star calibrations. A community of
some 400 scientists from more than 110 institutions came together to deliver
the survey. Significant scientific results have already been delivered, some
of which are presented in the companion paper Randich et al. (2022).

###### Acknowledgements.

Based on data products from observations made with ESO Telescopes at the La
Silla Paranal Observatory under programme ID 188.B-3002. These data products
have been processed by the Cambridge Astronomy Survey Unit (CASU) at the
Institute of Astronomy, University of Cambridge, and by the FLAMES/UVES
reduction team at INAF/Osservatorio Astrofisico di Arcetri. Public access to
the data products is via the ESO SAF, and the Gaia-ESO Survey Data Archive,
prepared and hosted by the Wide Field Astronomy Unit, Institute for Astronomy,
University of Edinburgh, which is funded by the UK Science and Technology
Facilities Council. This work was partly supported by the European Union FP7
programme through ERC grant number 320360 and by the Leverhulme Trust through
grant RPG-2012-541. We acknowledge the support from INAF and Ministero dell’
Istruzione, dell’ Università’ e della Ricerca (MIUR) in the form of the grant
”Premiale VLT 2012”. The project presented here benefited in development from
discussions held during the Gaia-ESO workshops and conferences supported by
the ESF (European Science Foundation) through the GREAT Research Network
Programme. This research has made use of the SIMBAD database, operated at CDS,
Strasbourg, France. R.Smiljanic acknowledges support from the National Science
Centre, Poland (2014/15/B/ST9/03981). This work was partly supported by the
INAF grant for mainstream projects: ”Enhancing the legacy of the Gaia-ESO
Survey for open cluster science”. F.J.E. acknowledges financial support from
the Spanish MINECO/FEDER through the grant AYA2017-84089 and MDM-2017-0737 at
Centro de Astrobiología (CSIC-INTA), Unidad de Excelencia María de Maeztu, and
from the European Union’s Horizon 2020 research and innovation programme under
Grant Agreement no. 824064 through the ESCAPE - The European Science Cluster
of Astronomy and Particle Physics ESFRI Research Infrastructures project. T.B.
was funded by the “The New Milky Way” project grant from the Knut and Alice
Wallenberg Foundation. S.R.B. acknowledges support by the Spanish Government
under grants AYA2015-68012-C2-2-P and PGC2018-093741-B-C21/C22
(MICIU/AEI/FEDER, UE). W. J. S. acknowledges CAPES for a PhD studentship.
J.M.A. acknowledges support from the Spanish Government Ministerio de Ciencia
e Innovación through grants AYA2013-40611-P, AYA2016-75931-C2-2-P, and
PGC2018-095049-B-C22. T.M. and others from STAR institute, Liege, Belgium are
grateful to Belgian F.R.S.-FNRS for support, and are also indebted for an
ESA/PRODEX Belspo contract related to the Gaia Data Processing and Analysis
Consortium and for support through an ARC grant for Concerted Research Actions
financed by the Federation Wallonie-Brussels.. This research has been
partially supported by the following grants: MIUR Premiale ”Gaia-ESO survey”
(PI S. Randich), MIUR Premiale ”MiTiC: Mining the Cosmos” (PI B. Garilli), the
ASI-INAF contract 2014-049-R.O: ”Realizzazione attività tecniche/scientifiche
presso ASDC” (PI Angelo Antonelli), Fondazione Cassa di Risparmio di Firenze,
progetto: ”Know the star, know the planet” (PI E. Pancino), and Progetto Main
Stream INAF: ”Chemo-dynamics of globular clusters: the Gaia revolution” (PI E.
Pancino). V.A.acknowledges the support from Fundação para a Ciência e
Tecnologia (FCT) through Investigador FCT contract nr.
IF/00650/2015/CP1273/CT0001. AJK acknowledges support by the Swedish National
Space Agency (SNSA). AB acknowledges support by ANID, – Millennium Science
Initiative Program – NCN19_171, and FONDECYT regular 1190748. E. M.
acknowledges financial support from the Spanish State Research Agency (AEI)
through project MDM-2017-0737 Unidad de Excelencia ”Maria de Maeztu” - Centro
de Astrobiología (CSIC-INTA). T.Z. acknowledges financial support of the
Slovenian Research Agency (research core funding No. P1-0188) and the European
Space Agency (Prodex Experiment Arrangement No. C4000127986). P.J.
acknowledges support FONDECYT Regular 1200703. The work of I.N. is partially
supported by the Spanish Government Ministerio de Ciencia, Innovación y
Universidades under grant PGC2018-093741-B-C21 (MICIU/AEI/FEDER, UE). Funding
for this work has been provided by the ARC Future Fellowship FT160100402. CAP
acknowledges financial support from the Spanish Government through research
grants MINECO AYA 2014-56359-P, MINECO AYA2017-86389-P, and MICINN
PID2020-117493GB-I00. S.F. was supported by the grants 2011-5042 and 2016-
03412 from the Swedish Research Council and the project grant ”The New Milky
Way” from the Knut and Alice Wallenberg Foundation. CASU is supported through
STFC grants: ST/H004157/1, ST/J00541X/1, ST/M007626/1, ST/N005805/1,
ST/T003081/1. Work reported here benefited from support through the GREAT-ITN
FP7 project Grant agreement ID: 264895. DKF acknowledges funds from the
Alexander von Humboldt Foundation in the framework of the Sofja Kovalevskaja
Award endowed by the Federal Ministry of Education and Research and the grant
2016-03412 from the Swedish Research Council. A.H. acknowledges support from
the Spanish Government Ministerio de Ciencia e Innovación and ERD Funds
through grants PGC-2018-091 3741-B-C22 and CEX2019-000920-S. X.F. acknowledge
the support of China Postdoctoral Science Foundation 2020M670023. M. L. L.
Dantas acknowledges the Polish NCN grant number 2019/34/E/ST9/00133. Part of
this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German
Research Foundation) – Project-ID 138713538 – SFB 881 (“The Milky Way System”,
subproject A09). MZ acknowledge support from the National Agency for Research
and Development (ANID) grants: FONDECYT Regular 1191505, Millennium Institute
of Astrophysics ICN12-009, BASAL Center for Astrophysics and Associated
Technologies AFB-170002. R.B. acknowledges support from the project PRIN-INAF
2019 ”Spectroscopically Tracing the Disk Dispersal Evolution”. HMT
acknowledges financial support from the Agencia Estatal de Investigación of
the Ministerio de Ciencia, Innovación y Universidades through projects
PID2019-109522GB-C51,54/AEI/10.13039/501100011033, and the Centre of
Excellence “María de Maeztu” award to Centro de Astrobiología (MDM-2017-0737).
JIGH acknowledges financial support from the Spanish Ministry of Science and
Innovation (MICINN) project AYA2017-86389-P, and also from the Spanish MICINN
under 2013 Ramon y Cajal program RYC-2013-14875. V.P.D. is supported by STFC
Consolidated grant ST/R000786/1. N.L. acknowledges financial support from
“Programme National de Physique Stellaire” (PNPS) and the “Programme National
Cosmology et Galaxies (PNCG)” of CNRS/INSU, France. A. R. C. is supported in
part by the Australian Research Council through a Discovery Early Career
Researcher Award (DE190100656). Parts of this research were supported by the
Australian Research Council Centre of Excellence for All Sky Astrophysics in 3
Dimensions (ASTRO 3D), through project number CE170100013. PSB is Supported by
the Swedish Research Council through individual project grants with contract
Nos. 2016-03765 and 2020-03404. AM acknowledges funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and
innovation programme (grant agreement No. 772293 - project ASTEROCHRONOMETRY).
JP was supported by the project RVO: 67985815. E.D.M. acknowledges the support
from FCT through the research grants UIDB/04434/2020 & UIDP/04434/2020 and
through Investigador FCT contract IF/00849/2015/CP1273/CT0003. This work was
(partially) supported by the Spanish Ministry of Science, Innovation and
University (MICIU/FEDER, UE) through grant RTI2018-095076-B-C21, and the
Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de
Excelencia ’María de Maeztu’) through grant CEX2019-000918-M. SLM acknowledges
the support of the UNSW Scientia Fellowship program and the Australian
Research Council through Discovery Project grant DP180101791. GT acknowledges
financial support of the Slovenian Research Agency (research core funding No.
P1-0188) and the European Space Agency (Prodex Experiment Arrangement No.
C4000127986). S.G.S acknowledges the support from FCT through Investigador FCT
contract nr. CEECIND/00826/2018 and POPH/FSE (EC). H.G.L. acknowledges
financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation) – Project-ID 138713538 – SFB 881 (“The Milky Way System”,
subproject A04). This work was (partially) supported by the Spanish Ministry
of Science, Innovation and University (MICIU/FEDER, UE) through grant
RTI2018-095076-B-C21, and the Institute of Cosmos Sciences University of
Barcelona (ICCUB, Unidad de Excelencia ’María de Maeztu’) through grant
CEX2019-000918-M. T.K. is supported by STFC Consolidated grant ST/R000786/1.
MV acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG,
project number: 428473034). T.M. is supported by a grant from the Fondation
ULB. We acknowledge financial support from the Universidad Complutense de
Madrid (UCM) and by the Spanish Ministerio de Ciencia, Innovación y
Universidades, Ministerio de Economía y Competitividad, from project
AYA2016-79425-C3-1-P and PID2019-109522GB-C5[4]/AEI/10.13039/501100011033.
U.H. acknowledges support from the Swedish National Space Agency
(SNSA/Rymdstyrelsen). D.G. gratefully acknowledges support from the Chilean
Centro de Excelencia en Astrofísica y Tecnologías Afines (CATA) BASAL grant
AFB-170002. D.G. also acknowledges financial support from the Dirección de
Investigación y Desarrollo de la Universidad de La Serena through the Programa
de Incentivo a la Investigación de Académicos (PIA-DIDULS). A. Lobel
acknowledges support in part by the Belgian Federal Science Policy Office
under contract No. BR/143/A2/BRASS. We acknowledge financial support from the
Universidad Complutense de Madrid (UCM) and by the Spanish Ministerio de
Ciencia, Innovación y Universidades, Ministerio de Economía y Competitividad,
from project AYA2016-79425-C3-1-P and
PID2019-109522GB-C5[4]/AEI/10.13039/501100011033. AM acknowledges the support
from the Portuguese Fundação para a Ciência e a Tecnologia (FCT) through the
Portuguese Strategic Programme UID/FIS/00099/2019 for CENTRA. TM acknowledges
financial support from the Spanish Ministry of Science and Innovation (MICINN)
through the Spanish State Research Agency, under the Severo Ochoa Program
2020-2023 (CEX2019-000920-S). EJA acknowledges funding from the State Agency
for Research of the Spanish MCIU through the Center of Excellence Severo Ochoa
award to the Instituto de Astrofisica de Andalucia (SEV-2017-0709).

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