11institutetext: Dipartimento di Fisica e Astronomia, Universita’ di Padova,
vicolo Osservatorio 2, 35122, Padova, Italy
11email<EMAIL_ADDRESS><EMAIL_ADDRESS>22institutetext: INAF-Osservatorio Astronomico di Padova, vicolo Osservatorio
5, 35122, Padova, Italy 33institutetext: INAF-Osservatorio di Astrofisica e
Scienza dello Spazio di Bologna, via P. Gobetti 93/3, 40129, Bologna, Italy
44institutetext: INAF-Osservatorio Astrofisico di Catania, Via S. Sofia 78,
95123, Catania, Italy 55institutetext: Fundación Galileo Galilei - INAF,
Rambla José Ana FernÃ¡ndez Pérez 7, 38712, BreÃ±a Baja, Tenerife, Spain
66institutetext: INAF-Osservatorio Astronomico di Roma, Via Frascati 33,
00078, Monte Porzio Catone, Italy 77institutetext: Dipartimento di Fisica e
Astronomia, UniversitÃ degli Studi di Firenze, Via G. Sansone 1, 50019, Sesto
Fiorentino, Firenze, Italy 88institutetext: KIAA-The Kavli Institute for
Astronomy and Astrophysics at Peking University, Beijing 100871, China
99institutetext: INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi, 5,
50125, Firenze, Italy

# Stellar Population Astrophysics (SPA) with TNG††thanks: Based on
observations made with the Italian Telescopio Nazionale Galileo (TNG) operated
on the island of La Palma by the FundaciÃ³n Galileo Galilei of the INAF
(Istituto Nazionale di Astrofisica) at the Observatorio del Roque de los
Muchachos. This study is part of the Large Program titled SPA Stellar
Population Astrophysics: the detailed, age-resolved chemistry of the Milky Way
disc (PI: L. Origlia), granted observing time with HARPS-N and GIANO-B echelle
spectrographs at the TNG.

Atmospheric parameters of members of 16 unstudied open clusters

R. Zhang 1122 S. Lucatello 22 A. Bragaglia 33 R. Carrera 22 L. Spina 22 J.
Alonso-Santiago 44 G. Andreuzzi 5566 G. Casali 7788 E. Carretta 33 A. Frasca
44 X. Fu 8833 L. Magrini 99 L. Origlia 33 V. D’Orazi 22 A. Vallenari 22

(Received )

###### Abstract

Context. Thanks to modern understanding of stellar evolution, we can
accurately measure the age of Open Clusters (OCs). Given their position, they
are ideal tracers of the Galactic disc. Gaia data release 2, besides providing
precise parallaxes, led to the detection of many new clusters, opening a new
era for the study of the Galactic disc. However, detailed information on the
chemical abundance for OCs is necessary to accurately date them and to
efficiently use them to probe the evolution of the disc.

Aims. Mapping and exploring the Milky Way structure is the main aim of the
Stellar Population Astrophysics (SPA) project. Part of this work involves the
use of OCs and the derivation of their precise and accurate chemical
composition. We analyze here a sample of OCs located within about 2 kpc from
the Sun, with ages from about 50 Myr to a few Gyr.

Methods. We used HARPS-N at the Telescopio Nazionale Gaileo and collected very
high-resolution spectra (R = 115 000) of 40 red giant/red clump stars in 18
OCs (16 never or scarcely studied plus two comparison clusters). We measured
their radial velocities and derived the stellar parameters
($T_{\textrm{eff}}$, log g, vmicro, and [Fe/H]) based on equivalent width
measurement combined with 1D - LTE atmospherical model.

Results. We discussed the relationship between metallicity and Galactocentric
distance, adding literature data to our results to enlarge the sample and
taking also age into account. We compared the result of observational data
with that from chemo-dynamical models. These models generally reproduce the
metallicity gradient well. However, at young ages we found a large dispersion
in metallicity, not reproduced by models. Several possible explanations are
explored, including uncertainties in the derived metallicity. We confirm the
difficulties in determining parameters for young stars (age ¡ 200 Myr), due to
a combination of intrinsic factors (activity, fast rotation, magnetic fields,
etc) which atmospheric models can not easily reproduce and which affect the
parameters uncertainty.

###### Key Words.:

Stars: abundances – stars: evolution – open clusters and association: general
– open clusters and associations: individual (ASCC 11, Alessi 1, Alessi-
Teutsch 11, Basel 11b, COIN-Gaia 30, Collinder 463, Gulliver 18, Gulliver 24,
Gulliver 27, NGC 2437, NGC 2509, NGC 2548, NGC 7082, NGC 7209, Tombaugh 5, UPK
219, Collinder 350, NGC 2682)

##  1 Introduction

Most of our knowledge of stellar physics, on the ages of stars and on their
evolution has been acquired thanks to the study of star clusters, of their
formation and evolution and of their stellar populations. This knowledge has
very general bearing in our insight on a variety of astrophysical processes.
Age dating stellar populations is necessary to investigate the formation of
the Milky Way (e.g. Ness et al., 2016). The interpretation of light from
simple stellar systems allows the building of population synthesis models used
in studying the star formation history in other galaxies. The rate and timing
of mass loss is a crucial ingredient in probing the chemical evolution and
feedback processes in galaxies. For all this, stellar clusters are privileged
probes and test cases.

Open Clusters (OCs hereafter), in particular, provide key insight on the
Galactic disc. These objects are made of coeval, chemically homogeneous, and
dynamically bound groups of stars born from the same molecular cloud. With
metallicities not too far off from that of the Sun ($-0.5<$[Fe/H$]<0.5$, see
e.g. Netopil et al. 2016; Donor et al. 2020; Casali et al. 2019), they are
generally young (most of them are $<1$ Gyr, but there are clusters with ages
as high as 8–10 Gyr, see e.g. Kharchenko et al. 2013; Cantat-Gaudin et al.
2020) and range greatly in size: from loose associations with just a handful
of stars to super star clusters, with as many as 104 members. As they form and
evolve into (or in close proximity to) the Galactic disc, they are prone to
stripping and disruption, and are in fact thought to be one of the main
sources of field stars (e.g. Lada & Lada, 2003).

Stellar populations in OCs cover stars from low to high mass, and different
evolutionary stages, making each cluster a snapshot of stellar evolution at a
given age and composition. With ages covering the entire lifespan of the thin
disc, OCs trace the young, intermediate-age, and old thin disc components. Age
can be measured for OCs with much more accuracy than for Galactic field stars,
making them the ideal tool to probe the Galaxy formation and evolution,
through the age-metallicity relation, radial gradients and the comparison with
theoretical models.

The Gaia results have brought a veritable revolution in our knowledge of OCs.
High probability memberships based on proper motions and parallaxes have been
derived for very large samples of stars, leading also to the discovery of a
substantial number of new OCs (see e.g. Cantat-Gaudin et al., 2018; Castro-
Ginard et al., 2019, 2020; Liu & Pang, 2019; Sim et al., 2019).

Presently, only a minority of OCs has been studied with high quality
spectroscopic data, implying not only a lack of information on the composition
for the vast majority of OCs, but also possibly inaccurate ages. In fact, a
precise metallicity is a key ingredient for the derivation of ages from
photometry (see e.g. Bossini et al., 2019). Moreover, the sample is likely to
be affected by bias towards larger clusters, where, in the years before Gaia
membership information, it was easier to successfully target actual members.

While Gaia will, at end of mission, provide distances and proper motions with
a precision $<10\%$ for almost all known clusters, its spectroscopic
capabilities are rather limited. The crucial third kinematic dimension (radial
velocity, RV) and detailed chemical composition will need to be largely
provided by ground-based complementary observations.

Recently completed and ongoing large stellar surveys, such as Gaia-ESO (GES),
GALAH and APOGEE (Gilmore et al., 2012; De Silva et al., 2015; Majewski et
al., 2017, respectively), have provided composition and RVs for a few
thousands of stars in some hundred of OCs based on high-resolution
($\sim$20-40K) spectroscopy. This sample will be further increased by WEAVE
(Dalton et al., 2020), which has the study of OCs as one of the primary goals
of its Galactic Archeology Survey, and likely by 4MOST (de Jong et al., 2019).

The key feature of GES and of the future surveys resides in their ability to
study multiple members of the clusters in all evolutionary phases, with tens
to many hundred of stars observed in each cluster (while APOGEE, for instance,
relies generally on much smaller samples). This is in fact a crucial step in
the understanding of the formation of the clusters (see e.g. Jeffries et al.,
2014; Mapelli et al., 2015) and the evolution of the stars properties
following changes in rotation, activity, surface abundances, all key
constraints to modern stellar evolutionary models (see e.g. Bertelli Motta et
al., 2018; Smiljanic et al., 2016; Lagarde et al., 2019, on diffusion and
extra mixing). However, because of their spectral coverage and/or resolution,
these surveys provide an incomplete chemical characterization of OC stars. An
accurate determination of chemical abundances requires spectra with a very
high resolution and a wide spectral coverage, to measure the full set of the
Fe-peak, CNO, $\alpha$, p- and n-capture elements with high accuracy, on a par
with the astrometric and photometric information provided by Gaia.

This implies that there is a need for observations that take a complementary
approach, i.e. study in details with high-resolution (R $>$ 50-70000), large
wavelength coverage, and high signal-to-noise spectroscopy a few stars per
OCs, deriving a full chemical characterization. Indeed, measuring elements of
all nucleosynthetic chains, which probe different nuclear reaction sites in
stars, means providing robust constraints to stellar evolutionary models and
to the history of the Galactic disc.

This paper is part of a series on a large project, Stellar Populations
Astrophysics (SPA), at the Italian TNG telescope. In this paper we will
present the atmospheric parameters and radial velocities for stars in 16 OCs
never or poorly studied before with high resolution spectroscopy, plus two
more as a comparison sample, while chemical abundance analysis will be
presented in following papers.

In Section 2 we discuss the sample selection, followed by a description of
observations and of the data reduction procedure in Section 3. Determination
of the spectroscopic parameters is discussed in Section 4, while in Section 5
we discuss our results in comparison with outcomes from the literature and
chemo-dynamical models of the Galactic disc. Section 6 outlines our
conclusions.

Figure 1: The CMDs of nine of the 18 open clusters; the dots are stars
selected from Gaia DR2 database, the large diamonds are targets observed in
this work. All points are coloured by membership probability (Cantat-Gaudin et
al., 2018). Figure 2: As in the previous figure, for the remaining nine
clusters. Table 1: Properties of the observed clusters.

Cluster | RA | Dec | l | b | logAge | AV | plx | pmRA | pmDE | dist | RGC | —z—
---|---|---|---|---|---|---|---|---|---|---|---|---
| (J2000) | (J2000) | (deg) | (deg) | (yr) | (mag) | (mas) | (mas yr${}^{-1})$ | (mas yr-1) | (pc) | (pc) | (pc)
ASCC 11 | 03:32:13.44 | +44:51:21.6 | 150.546 | -9.224 | 8.39 | 0.60 | 1.141 | 0.926 | -3.030 | 867 | 9095 | 139
Alessi 1 | 00:53:22.32 | +49:32:09.6 | 123.255 | -13.33 | 9.16 | 0.08 | 1.390 | 6.536 | -6.245 | 689 | 8726 | 159
Alessi-Teusch 11 | 20:16:30.48 | +52:03:03.6 | 87.435 | 9.268 | 8.16 | 0.37 | 1.520 | -0.139 | -1.295 | 634 | 8335 | 102
Basel 11b | 05:58:11.28 | +21:57:54.0 | 187.442 | -1.117 | 8.36 | 1.56 | 0.534 | 1.046 | -4.137 | 1793 | 10121 | 34
COIN-Gaia 30 | 01:24:19.20 | +70:34:26.4 | 125.684 | 7.878 | 8.41 | 1.25 | 1.346 | -6.145 | 2.067 | 767 | 8804 | 105
Collinder 463 | 01:48:07.44 | +71:44:16.8 | 127.391 | 9.358 | 8.06 | 0.79 | 1.137 | -1.715 | -0.307 | 849 | 8874 | 138
Gulliver 18 | 20:11:37.20 | +26:31:55.2 | 65.527 | -3.971 | 7.60 | 1.59 | 0.613 | -3.198 | -5.646 | 1595 | 7816 | 110
Gulliver 24 | 00:04:38.64 | +62:50:06.0 | 117.62 | 0.447 | 8.25 | 1.05 | 0.636 | -3.241 | -1.57 | 1498 | 9131 | 11
Gulliver 37 | 19:28:18.48 | +25:20:49.2 | 59.547 | 3.806 | 8.55 | 1.33 | 0.642 | -0.775 | -3.74 | 1438 | 7712 | 95
NGC 2437 | 07:41:46.80 | -14:50:38.4 | 231.889 | 4.051 | 8.48 | 0.73 | 0.603 | -3.838 | 0.365 | 1511 | 9345 | 106
NGC 2509 | 08:00:48.24 | -19:03:21.6 | 237.844 | 5.840 | 9.18 | 0.23 | 0.363 | -2.708 | 0.764 | 2495 | 9887 | 254
NGC 2548 | 08:13:38.88 | -05:43:33.6 | 227.842 | 15.390 | 8.59 | 0.15 | 1.289 | -1.313 | 1.029 | 772 | 8857 | 205
NGC 7082 | 21:28:44.64 | +47:06:10.8 | 91.115 | -2.859 | 7.79 | 0.79 | 0.729 | -0.293 | -1.106 | 1339 | 8472 | 66
NGC 7209 | 22:04:53.76 | +46:30:28.8 | 95.480 | -7.296 | 8.63 | 0.53 | 0.820 | 2.255 | 0.283 | 1154 | 8525 | 146
Tombaugh 5 | 03:47:56.16 | +59:04:12.0 | 143.944 | 3.599 | 8.27 | 2.07 | 0.561 | 0.515 | -2.388 | 1706 | 9768 | 107
UPK 219 | 23:27:24.96 | +65:18:36.0 | 114.325 | 3.861 | 8.17 | 1.20 | 1.210 | -1.734 | -2.459 | 873 | 8735 | 58
Comparison clusters |
Collinder 350 | 17:48:04.32 | +01:31:30.0 | 26.952 | 14.773 | 8.77 | 0.52 | 2.708 | 4.965 | -0.019 | 371 | 8021 | 94
NGC 2682 | 08:51:23.04 | +11:48:50.4 | 215.691 | 31.921 | 9.63 | 0.07 | 1.135 | 10.986 | -2.964 | 889 | 8964 | 470

111All data come from Cantat-Gaudin et al. (2020) and are based on Gaia DR2.

Table 2: Radial velocities of targets. Name | Gaia ID | S/N | RV |  $\sigma$RV | RV(Gaia) |  $\sigma$RV(Gaia) | Notes
---|---|---|---|---|---|---|---
|  | (600nm) | km s-1 | km s-1 | km s-1 | km s-1 |
ASCC 11 | 241730418805573760 | 93 | -13.36 | 0.15 | -13.87 | 0.23 |
Alessi 1_1 | 402506369136008832 | 93 | -5.50 | 0.05 | -4.19 | 0.64 |
Alessi 1_2 | 40250599117880752 | 88 | -3.13 | 0.07 | -5.23 | 0.81 |
Alessi 1_3 | 402867593065772288 | 127 | -4.57 | 0.20 | -4.25 | 1.83 |
Alessi 1_4 | 402880684126058880 | 120 | -4.29 | 0.03 | -4.54 | 0.42 |
Alessi-Teusch 11 | 2184332753719499904 | 126 | -27.11 | 0.12 | -27.09 | 0.17 |
Basel 11b_1 | 3424056131485038592 | 125 | 2.26 | 0.18 | 3.11 | 0.49 |
Basel 11b_2 | 3424055921028900736 | 86 | 1.68 | 0.15 | 5.81 | 1.11 |
Basel 11b_3 | 3424057540234289408 | 66 | 2.57 | 0.15 | 2.71 | 1.12 |
COIN-Gaia 30 | 532533682228608384 | 93 | -26.66 | 0.13 | -26.10 | 0.14 |
Collinder 463_1 | 534207555539397888 | 143 | -9.68 | 0.16 | -12.47 | 0.14 |
Collinder 463_2 | 534363067715447680 | 152 | -11.64 | 0.12 | -11.62 | 0.14 |
Gulliver 18 | 1836389309820904064 | 87 | -1.97 | 0.18 | -3.32 | 0.21 |
Gulliver 24 | 430035249779499264 | 78 | -30.37 | 0.16 | -31.86 | 0.49 |
Gulliver 37 | 2024469226291472000 | 78 | -4.59 | 0.17 | 2.52 | 8.22 | binary? (see text)
NGC 2437_1 | 3029609393042459392 | 64 | 49.77 | 0.11 | 50.07 | 0.61 |
NGC 2437_2 | 3029202711180744832 | 111 | 47.16 | 0.14 | 46.80 | 0.50 |
NGC 2437_3 | 3030364134752459904 | 95 | 49.02 | 0.17 | 48.80 | 0.25 |
NGC 2437_4 | 3029132686034894592 | 128 | 49.93 | 0.15 |  |  |
NGC 2437_5 | 3029156222454419072 | 50 | 49.37 | 0.17 | 49.34 | 0.17 |
NGC 2437_6 | 3029207006148017664 | 72 | 27.12 | 0.17 | 28.76 | 9.91 | P$\sim 3350^{d}$ (Mermilliod et al., 2007)
NGC 2437_7 | 3029226694277998080 | 74 | 49.53 | 0.19 | 51.60 | 0.62 |
NGC 2509 | 5714209934411718784 | 128 | 61.63 | 0.14 | 61.42 | 1.45 |
NGC 2548_1 | 3064481400744808704 | 125 | 9.06 | 0.14 | 8.68 | 0.29 |
NGC 2548_2 | 3064537647636773760 | 138 | 8.16 | 0.03 | 8.83 | 0.31 |
NGC 2548_3 | 3064579703955646976 | 77 | 8.01 | 0.04 | 8.76 | 0.22 |
NGC 2548_4 | 3064486692144030336 | 94 | 8.98 | 0.10 | 8.10 | 0.67 | SB (Mermilliod et al., 2008)
NGC 7082_1 | 1972288740859811072 | 144 | -11.89 | 0.15 | -11.07 | 0.19 |
NGC 7082_2 | 1972288637780285312 | 151 |  |  |  |  | Double lined binary
NGC 7209_1 | 1975004019170020736 | 81 | -19.00 | 0.16 | -18.14 | 0.27 |
NGC 7209_2 | 1975002919658397568 | 161 | -17.98 | 0.15 | -18.89 | 0.35 |
Tombaugh 5_1 | 473266779976916480 | 72 | -23.03 | 0.15 | -22.05 | 1.03 |
Tombaugh 5_2 | 473275782228263296 | 54 | -22.63 | 0.12 | -22.84 | 0.26 |
Tombaugh 5_3 | 473268424940932864 | 53 | -20.53 | 0.13 | -23.45 | 0.29 |
UPK 219 | 2209440823287736064 | 81 | -0.07 | 0.15 | -2.03 | 0.55 |
Comparison clusters |
Collinder 350_1 | 4372743213795720704 | 240 | -13.98 | 0.17 | -14.57 | 0.20 |
Collinder 350_2 | 4372572888274176768 | 70 | -16.30 | 0.13 | -14.73 | 0.16 |
NGC 2682_1 | 604921512005266048 | 141 | 33.63 | 0.23 | 34.29 | 0.30 |
NGC 2682_2 | 604920202039656064 | 78 | 34.93 | 0.14 | 34.49 | 0.35 |
NGC 2682_3 | 604904950611554432 | 82 | 33.93 | 0.20 | 36.83 | 0.96 | binary (see Tab. 6)
NGC 2682_4 | 604917728138508160 | 53 | 27.41 | 0.13 | 26.98 | 2.70 | binary (see Tab. 6)

##  2 Target selection and dataset

In this work, we present the analysis of 40222We observed 41 stars, but one is
a double-spectrum binary and was excluded from analysis. giant stars in 18
open clusters. Sixteen of the OCs were poorly or not at all studied before,
while two of them were previously studied and work as comparison samples.
These clusters were selected because they are in the Sun vicinity so we could
obtain good quality, high-resolution spectra and are old enough to have stars
evolved off the main sequence. The target stars were not selected directly
from the Gaia DR2 catalog, but from the membership analysis done by Cantat-
Gaudin et al. (2018). We targeted only stars with high membership probability
and we have from one (in 8 cases) to a maximum of 7 stars (in 1 cluster) per
OC.

We selected (almost) only red clump stars, not giants in general. They are a
rather homogeneous type of stars, bright enough (their absolute magnitude is
$M_{V}\sim 0-1$ mag, Girardi 2016) to be observed at very high-resolution in
the Solar neighborhood and to relatively large distance, warm enough
($T_{\textrm{eff}}\sim 4500-5700$ K, Girardi 2016) that they can be analysed
to derive meaningful parameters and abundances. Their temperature is high
enough to allow a precise abundance analysis, as opposed to cooler, upper red
giant branch stars, where line crowding (in particular at near solar
metallicities) may hamper accurate analyses (see for instance the discussion
in Casali et al. 2020).

Table 1 shows information on the clusters’ position (both equatorial and
Galactic coordinates) and some basic parameter, such as age, distance,
reddening, etc. All values come from a single homogeneous source, i.e. Cantat-
Gaudin et al. (2020), where Gaia DR2 data are used to define candidate cluster
members and derive cluster properties (see that paper for details).

All these OCs are located in the Galactic thin disc, cover the Galactocentric
distance range 7.7-10 kpc, reside close to the Galactic midplane, and are in
the age range from about 50 Myr to 4 Gyr. Figures 1, 2 show the colour-
magnitude diagrams (CMD) in the Gaia system of the 18 OCs, based on the
selection of Cantat-Gaudin et al. (2018); the stars are coloured with
membership probability and the observed targes are indicated by larger
symbols.

Figure 3: Difference in radial velocities for targets between Gaia and this
work. The error of our sample is shown in the x-axis (it is so small that it
falls within the symbol), and only the Gaia error is plotted on the y-axis, as
it is much larger. The grey lines indicate the mean value of offset $-0.11\pm
1.9$ km s-1 (standard deviation).

##  3 Observation, data reduction and radial velocities

Observations were carried out in four observing runs in December 2018,
January, August, and December 2019. In most cases, multiple exposures were
obtained (see Table LABEL:tab:A1 for details on the individual targets). The
exposure times varied according to the star magnitude and the sky conditions;
the goal was to reach at least a signal-to-noise ratio (S/N) of 50 at 500nm.
We combined multi-exposure spectra before further data analysis.

All of those data were obtained with the 3.5 m Telescopio Nazionale Galileo
(TNG) at El Roque de los Muchachos Observatory (La Palma, Canary Islands,
Spain). We used the two high-resolutions spectrographs HARPS-N and GIANO in
GIARPS mode. This means that observation in the optical range and near-IR are
executed at the same time, with the light from a single target separated using
a dichroic (a few more details can be found in previous SPA papers, see e.g.
Frasca et al., 2019; D’Orazi et al., 2020). The analysis of GIANO data will be
presented in forthcoming works; we deal here with the HARPS-N data. HARPS-N
covers the wavelength range between 383 and 693 nm, with resolution R =
115000; this spectrograph is very well suited for determining radial velocity
(RV) and abundances with very high precision.

The spectra were reduced by the HARPS-N Data Reduction Pipelines (they were
corrected for bias, flat field, extracted to 1d spectra, wavelength calibrated
and corrected for barycentric motion) and retrieved from the Italian Center
for Astronomical Archives in Trieste (http://ia2-harps.oats.inaf.it/).
Spectrum continuum normalization with cubic splines and combination were done
with IRAF333IRAF is distributed by the National Optical Astronomy Observatory,
which is operated by the Association of the Universities for Research in
Astronomy, inc. (AURA) under cooperative agreement with the National Science
Foundation. using the tasks continuum, scombine.

The calculation of RV was done on the final normalized spectra with the IRAF
task rvidlines, which measures the wavelength shift of a list of features. For
all stars, the RV information and the error is given in Table 2, together with
the S/N measured at 600 nm. As we selected high probability members, we find
very similar RVs for stars belonging to the same cluster, which further
reinforces their membership.

All our stars except one (plus the binary) have an RV determined with the RVS
instrument on board Gaia, which observes the near-IR Ca ii triplet region at
resolution R=11500. These RVs and the corresponding errors, from Gaia DR2, are
also presented in Table 2.

Figure 3 shows the distribution of RV offsets between the value determined in
this work and that from Gaia. These offsets are generally small with an
average offset of $-0.11\pm 1.89$ km s-1. Owing to our small sample we have
not performed further comparison.

The few discrepant values seem probably due to the much larger errors in the
Gaia RVS determinations or to binarity. Two stars in NGC 2682, namely 3 and 4
in our list, one in NGC 2437, namely 6 in our list, and one in NGC 2548,
namely 4 in our list, are known binaries (Mermilliod et al., 2007, 2008;
Geller et al., 2021). The only Gulliver 37 star observed shows a large Gaia RV
dispersion, 8.2 km s-1 based on 11 transits, with an average RV of 2.5 km s-1,
compared to our value of -4.59 km s-1. Moreover, we have in hand an optical
high resolution spectrum for another project from which we derived a RV of
$21.92\pm 0.02$ km s-1 (Carrera et al. in prep.). Due to the large velocity
dispersion reported by Gaia and the discrepancy among the different radial
velocity determinations, we consider it as a probable spectroscopic binary.
All these stars do not present doubling of their lines, so the measure of the
atmospheric parameters is negligibly affected by the presence of any unseen
companion with respect to the precision of our analysis.

##  4 Stellar Parameters

Atmospheric parameters were determined spectroscopically, minimizing abundance
trends with excitation potential, strength of the line and imposing ionisation
equilibrium. In this section we describe this procedure.

###  4.1 Line list and Equivalent Width measurement

We adopted the line list for Fe i from Ruffoni et al. (2014) and Fe ii from
Meléndez & Barbuy (2009), selecting the lines in the 430 to 640 nm interval.
HARPS-N spectra start from 383 nm; however, the S/N is quite low in that
region for our spectra. This fact, combined with the extreme crowding of
features in the blue part of the spectrum, led us to discard all Fe lines
bluer than 430 nm, as their intensity could not be measured with the same
accuracy of those in redder regions.

Equivalent widths (EWs hereafter) were measured with the code ARES, following
the procedure described in Sousa et al. (2015), using a S/N dependent approach
to set the local continuum. Visual inspection was performed for very strong
lines (EW $>150$mÅ) and lines with large fitting errors (EW ¡
3$\times\sigma$(EW)) and we performed a manual measurement of the EW using
IRAF splot when the inspection showed an issue with the ARES fitting (e.g.
incorrect continuum placing due to a badly subtracted cosmic ray). Lines which
turned out to be blended were discarded.

###  4.2 Photometric Parameters

The determination of the atmospheric parameters requires a set of initial
parameters to be used in the first iteration of the abundance analysis
determination. For this purpose, we used photometric parameters, that are
determined on the basis of observed photometric colour(s) and the latest
generation of stellar isochrones.

To determine the initial stellar effective temperature ($T_{\textrm{}eff}$) we
used IRFM (Infrared flux method). This method is model independent and relies
on the flux on the stellar surface in infrared wavelength which relates to the
surface temperature. We derived the J-K colours from 2MASS, applied the
appropriate reddening for the target coordinate as derived from IRSA (Schlafly
& Finkbeiner, 2011). The computation of photometric surface temperature
follows the colour-temperature polynomial fitting based on a sample of F0-K5
type from Alonso et al. (1999), appropriate for stars in the range 3700-5300
K, which is consistent with our sample.

To determine $\log g$ we used Padova CMD isochrones (PARSEC release v1.2s,
Marigo et al. 2017 and COLIBRI, Pastorelli et al. 2020, and references
therein), with web interface CMD 3.4.

For age and metallicity input for the isochrones we used information taken
from the literature (see Table 1). In case no estimate of metallicity was
available, we adopted the average metallicity of the thin disc ([Fe/H] =
-0.25dex Soubiran et al. 2003). This is admittedly low for the typical OC,
however it has a negligible impact on the final parameters in Table 3.

In fact, while it is important to provide reasonably accurate input parameters
in the isochrones to derive similarly reasonable photometric parameters, these
values are just the starting point to build the initial model atmosphere. The
actual atmospheric parameters have been determined on the basis of the
observed spectra (see next section).

Table 3: Initial parameters from photometric data.  Name | $T_{\textrm{eff}}$ |  $\log$ g | [Fe/H] | vmicro
---|---|---|---|---
| (K) | (dex) | (dex) | (km s-1)
ASCC 11 | 4867 | 2.36 | -0.25 | 1.0
Alessi 1_1 | 4677 | 2.39 | -0.25 | 1.0
Alessi 1_2 | 4600 | 2.28 | -0.25 | 1.0
Alessi 1_3 | 4804 | 3.50 | -0.25 | 1.0
Alessi 1_4 | 4578 | 2.25 | -0.25 | 1.0
Alessi-Teusch 11 | 4790 | 3.40 | 0.10 | 1.0
Basel 11b_1 | 6259 | 1.70 | 0.01 | 1.0
Basel 11b_2 | 5917 | 1.59 | 0.01 | 1.0
Basel 11b_3 | 5693 | 1.83 | 0.01 | 1.0
COIN-Gaia 30 | 5231 | 1.70 | -0.25 | 1.0
Collinder 463_1 | 4730 | 2.12 | -0.25 | 1.0
Collinder 463_2 | 4730 | 2.30 | -0.25 | 1.0
Gulliver 18 | 4598 | 1.50 | -0.25 | 1.0
Gulliver 24 | 4567 | 1.62 | -0.25 | 1.0
Gulliver 37 | 5095 | 1.80 | -0.25 | 1.0
NGC 2437_1 | 4792 | 2.24 | 0.00 | 1.0
NGC 2437_2 | 5218 | 3.38 | 0.00 | 1.0
NGC 2437_3 | 5206 | 3.38 | 0.00 | 1.0
NGC 2437_4 | 5087 | 2.39 | 0.00 | 1.0
NGC 2437_5 | 4990 | 2.37 | 0.00 | 1.0
NGC 2437_6 | 4848 | 2.29 | 0.00 | 1.0
NGC 2437_7 | 4549 | 1.95 | 0.00 | 1.0
NGC 2509 | 4705 | 2.27 | 0.00 | 1.0
NGC 2548_1 | 5114 | 2.50 | -0.24 | 1.0
NGC 2548_2 | 5047 | 2.27 | -0.24 | 1.0
NGC 2548_3 | 4853 | 2.35 | -0.24 | 1.0
NGC 2548_4 | 5327 | 2.70 | -0.24 | 1.0
NGC 7082 | 4994 | 1.56 | -0.01 | 1.0
NGC 7209_1 | 4799 | 2.50 | 0.01 | 1.0
NGC 7209_2 | 4142 | 1.50 | 0.01 | 1.0
Tombaugh 5_1 | 5024 | 2.42 | 0.06 | 1.0
Tombaugh 5_2 | 5021 | 2.40 | 0.06 | 1.0
Tombaugh 5_3 | 5270 | 2.44 | 0.06 | 1.0
UPK 219 | 5203 | 3.01 | -0.25 | 1.0
Comparison clusters
Collinder 350_1 | 4200 | 1.30 | 0.10 | 1.0
Collinder 350_2 | 5300 | 3.20 | 0.10 | 1.0
NGC 2682_1 | 4537 | 3.61 | 0.00 | 1.0
NGC 2682_2 | 4601 | 2.50 | 0.00 | 1.0
NGC 2682_3 | 4823 | 2.90 | 0.00 | 1.0
NGC 2682_4 | 4967 | 3.36 | 0.00 | 1.0

###  4.3 Spectroscopic Parameters

The atmospheric parameters analysis was performed using the code MOOG (used
through the python wrapper pymoogi444https://github.com/madamow/pymoogi, based
on the MOOG 2019 version), a 1D, Local Thermodynamic Equilibrium (LTE) stellar
line analysis code. The model atmospheres were calculated interpolating the
ATLAS9 stellar atmosphere library (Castelli & Kurucz, 2003) in 1D plane-
parallel geometry. While in principle spherical models should be better for
giants, in practice this choice does not introduce significant differences
(Casali et al., 2020, see e.g.).

Atmospheric parameters were determined iteratively, by varying their values in
the input model atmosphere until excitation and ionisation equilibria were
reached.

In other words, $T_{\textrm{eff}}$’s and microturbulent velocities
($v_{micro}$) were determined by minimising the trends of Fe I abundance as
determined from different lines as a function of their excitation potential
and their intensity, respectively; surface gravity was determined by matching
(within the errors) output Fe I and Fe II abundances. While we adopt the solar
abundances from Grevesse & Sauval (1998), we have also repeated the procedure
for a solar spectrum collected with HARPS-N during one of our runs, finding an
Fe abundance essentially identical to that reported in Grevesse & Sauval
(1998). Errors on the atmospheric parameters were derived following the same
procedure outlined in Epstein et al. (2010).

The resulting atmospheric parameters along with their associated uncertainties
are reported in Table LABEL:tab:A2 for all individual targets. The mean value
of metallicity for each cluster is quoted in Table 5 (where, for Collinder
350, we use only the warmer star).

Table 4: Mean RV and metallicity for the observed clusters.

Cluster | RV |  $\sigma$RV | [Fe/H] |  $\sigma$[Fe/H] | N
---|---|---|---|---|---
| (km s-1) | (km s-1) | (dex) | (dex) |
ASCC 11 | -10.94 | 0.19 | -0.14 | 0.05 | 1
Alessi 1 | -4.37 | 0.85 | 0.00 | 0.08 | 4
Alessi-Teusch 11 | -27.11 | 0.12 | -0.19 | 0.05 | 1
Basel 11b | 2.17 | 0.37 | -0.01 | 0.05 | 3
COIN-Gaia 30 | -26.66 | 0.13 | 0.03 | 0.05 | 1
Collinder 463 | -10.66 | 0.98 | -0.15 | 0.05 | 2
Gulliver 18 | -1.97 | 0.18 | -0.10 | 0.05 | 1
Gulliver 24 | -30.37 | 0.16 | -0.10 | 0.05 | 1
Gulliver 37 | -4.59 a | 0.17 | 0.10 | 0.05 | 1
NGC 2437 | 49.13a | 0.93 | 0.00 | 0.04 | 7
NGC 2509 | 61.63 | 0.14 | -0.10 | 0.06 | 1
NGC 2548 | 8.41a | 0.46 | -0.02 | 0.03 | 4
NGC 7082 | -11.89 | 0.15 | -0.15 | 0.05 | 1
NGC 7209 | -18.49 | 0.51 | -0.04 | 0.04 | 2
Tombaugh 5 | -21.21 | 1.30 | 0.05 | 0.05 | 3
UPK 219 | -0.07 | 0.15 | 0.02 | 0.05 | 1
Comparison clusters
Collinder 350 | -15.14 | 1.16 | 0.00 | 0.05 | 1
NGC 2682 | 34.28a | 0;65 | 0.03 | 0.03 | 4
555a Binary systems (see Tab. 2).
When only 1 star is observed, $\sigma$ is the error, nor the dispersion.

###  4.4 Comparison with literature

Two clusters, namely Collinder 350 and NGC 2682 (M67) have been included in
the present sample for comparison purpose. The former has been investigated in
a previous SPA paper (Casali et al., 2020), which reports results obtained
with a slightly different approach for the same stars, therefore allowing the
mapping of possible offsets. The latter, NGC 2682, is one of the most studied
OCs and represents an ideal benchmark cluster. A few more clusters have some
recently published analyses. One of them was purposely observed (Tombaugh 5)
as the existing spectra were of moderate-resolution; for two more (Basel 11b,
NGC 2548) the results were published after our observations had already been
taken. Finally, we note that some other clusters (ASCC 11, Alessi-Teusch 11,
Collinder 350, NGC 2548, and NGC 2682) were observed within the LAMOST survey,
at R=1800 (Zhang et al., 2019). However, we will not discuss this last case,
limiting comparisons only to spectra with at least moderate resolution
(R$>10000$).

We give details below and briefly summarise how our results compare to
literature. In particular, Fig. 4 gives a visual comparison, while Tab. 6
presents detailed values for the benchmark cluster NGC 2682 and the other
stars. We remark that, for NGC 2682, there are no systematic differences among
the different studies.

#### Collinder 350 -

In our sample, we collected and analysed spectra for two members of Collinder
350, one of the least studied open clusters in the solar neighborhood. For the
coolest star (4200 K) we obtained [Fe/H]=$-0.40\pm 0.08$ dex, while for the
hottest (5300 K) we found [Fe/H]=$0.00\pm 0.05$ dex. In the work of Blanco-
Cuaresma & Fraix-Burnet (2018), the metallicity was estimated at 0.03 dex from
the spectrum of one single star with a resolution about 80,000. In the SPA
paper by Casali et al. (2020), the value of [Fe/H] was measured using two
methods on the same stars as in our analysis. Even in this case the two stars
show a difference in the measured metallicity, even if smaller than in the
present analysis. With ROTFIT (i.e. fitting of spectral libraries) they get
$0.03\pm 0.07$ dex (cooler star) and $-0.02\pm 0.09$ dex (hotter star) while
with FAMA (i.e. automatic method based on EWs analysis) they derive $-0.24\pm
0.02$ dex (cooler star) and $-0.03\pm 0.06$ dex (hotter star).

Comparing to the present paper, the results are overall in fair agreement. The
hotter star shows [Fe/H]=0, a value consistent with that from both methods in
Casali’s work. For the cooler star, our result is more metal poor than that
measured by Casali’s paper using either method. As mentioned by Casali et al.
(2020), we believe that at such temperature the placements of the continuum is
challenging in these stars, as they are very rich in absorption lines, leading
to the less accurate metallicities for star with low temperature
($T_{\textrm{eff}}$ ¡ 4300 K, $log$ g ¡ 1.8 dex).

#### NGC 2682 (M67) -

This is one of the most studied open clusters and has solar metallicity and
age (see e.g. Bertelli Motta et al., 2018; Bossini et al., 2019). We observed
four stars, two on the red clump and single, and two red giants known to be
binary systems (see Tables 2 and 6). In particular, the two single-lined
binaries (stars 3, 4 in our list) were studied by Mermilliod et al. (2007,
2008) and Geller et al. (2021), who derived their orbital parameters. Star NGC
2682 3 (aka S1237) is a yellow straggler (Leiner et al., 2016), i.e. a star
falling between the blue stragglers (BSS) and the red giants, above the
subgiant branch level in optical CMDs. Leiner et al. (2016) used Kepler K2
data to derive a mass of 2.9$\pm$0.2 M⊙, about twice the mass of turn-off
stars, corroborating the notion that yellow stragglers are a later
evolutionary phase of BSS. We collected results of spectral analysis from
multi-work and list them in Table 6. The parameters are in overall good
agreement with the literature.

Table 5: Comparison of atmospheric parameters with literature.

star | $T_{\textrm{eff}}$ | $\sigma T_{eff}$ | log g |  $\sigma$log g | [Fe/H] |  $\sigma$[Fe/H] | Reference |  Notes
---|---|---|---|---|---|---|---|---
| (K) |  | (dex) |  | (dex) |  |  |
NGC 2682_1 | 4687 | 50 | 2.37 | 0.07 | -0.05 | 0.05 | Present study | RC, single
| 4700 |  | 2.40 |  | -0.02 |  | Jacobson et al. (2011) |
| 4803 | 84 | 2.48 | 0.04 |  | 0.01 | APOGEE DR16 |
| 4745 | 57 | 2.59 | 0.09 | 0.04 | 0.04 |  Casamiquela et al. (2017) EW |
| 4771 | 13 | 2.55 | 0.03 | -0.04 | 0.05 |  Casamiquela et al. (2017) SS |
| 4793 |  | 2.55 |  | -0.10 |  | Gao et al. (2018) |
NGC 2682_2 | 4900 | 110 | 2.76 | 0.10 | 0.02 | 0.05 | Present study | RC, single
| 4700 |  | 2.50 |  | 0.04 |  | Jacobson et al. (2011) |
| 4802 | 84 | 2.34 | 0.04 | 0.00 | 0.01 | APOGEE DR16 |
| 4762 | 37 | 2.53 | 0.07 | 0.04 | 0.04 |  Casamiquela et al. (2017) EW |
| 4776 | 13 | 2.54 | 0.03 | -0.08 | 0.05 |  Casamiquela et al. (2017) SS |
| 4824 |  | 2.62 |  | -0.08 |  | Gao et al. (2018) |
NGC 2682_3 | 5000 | 50 | 2.77 | 0.10 | -0.03 | 0.03 | Present study | SB1, $P\sim 700^{d}$ (Geller et al. 2021),
| 5000 |  | 2.70 |  | -0.09 |  | Jacobson et al. (2011) | yellow straggler (Leiner et al. 2016)
| 5056 |  | 2.85 |  | 0.06 | 0.09 | APOGEE DR16 |
| 5067 |  | 2.85 |  | 0.06 | 0.09 | Luck (2015) |
| 5018 | 71 | 2.88 | 0.19 | -0.09 | 0.04 | Spina et al. (2021) |
NGC 2682_4 | 5195 | 50 | 3.25 | 0.10 | 0.00 | 0.05 | Present study | SB1, $P\sim 43^{d}$ (Mermilliod et al. 2007;
| 5100 |  | 3.00 |  | -0.11 |  | Jacobson et al. (2011) |  Geller et al. 2021)
| 5040 | 92 | 3.34 | 0.06 | -0.03 | 0.01 | APOGEE DR16 |
| 5098 | 71 | 3.12 | 0.26 | -0.11 | 0.04 | Spina et al. (2021) |
Collinder 350_1 | 4200 | 50 | 1.30 | 0.20 | -0.40 | 0.08 | Present study |
| 4100 | 100 | 1.35 | 0.23 | -0.24 | 0.01 |  Casali et al. (2020) FAMA |
| 4330 | 50 | 1.28 | 0.24 | -0.03 | 0.07 |  Casali et al. (2020) ROTFIT |
Collinder 350_2 | 5300 | 50 | 3.15 | 0.10 | 0.02 | 0.05 | Present study |
| 5170 | 110 | 2.85 | 0.27 | -0.03 | 0.06 |  Casali et al. (2020) FAMA |
| 5070 | 60 | 2.99 | 0.19 | -0.02 | 0.09 |  Casali et al. (2020) ROTFIT |
Basel 11b_3 | 4950 | 50 | 2.83 | 0.10 | -0.04 | 0.05 | Present study |
| 4817 | 86 | 2.37 | 0.04 | 0.00 | 0.01 | APOGEE DR 16 | 2M05581816+2158437
Tombaugh 5_1 | 5010 | 50 | 3.17 | 0.15 | 0.04 | 0.05 | Present study |
| 4710 |  | 2.10 |  | 0.21 | 0.05 | Baratella et al. (2018) |
NGC 2548_1 | 5370 | 70 | 3.67 | 0.15 | 0.00 | 0.05 | Present study |
| 4912 |  | 4.64 |  |  |  | Sun et al. (2020) |
| 5074 | 73 | 2.69 | 0.19 | -0.12 | 0.05 | Spina et al. (2021) |
NGC 2548_2 | 5050 | 50 | 2.65 | 0.10 | -0.02 | 0.04 | Present study |
| 5049 | 72 | 2.59 | 0.10 | -0.02 | 0.04 | Spina et al. (2021) |
NGC 2548_3 | 4930 | 50 | 2.70 | 0.10 | -0.01 | 0.05 | Present study |
| 4549 |  | 4.69 |  |  |  | Sun et al. (2020) |
| 4829 | 72 | 2.53 | 0.19 | 0.03 | 0.04 | Spina et al. (2021) |

666$\sigma[Fe/H]$ is the error from metallicity measurement for present study;
EW is the result obtained by the equivalent widths-based GALA software; SS is
the results obtained from spectral synthesis using iSpec

#### Tombaugh 5 -

A metallicity estimate based on moderate-resolution spectroscopy also exists
for Tombaugh 5. Baratella et al. (2018) reported atmospheric parameters (from
mid-res spectra, R=13 000) for five members out of seven observed. In this
case two stars are in common with our sample, as they were targeted on
purpose. However, only one (Tombaugh 5_1 for us, 7701 in Baratella et al.
2018) is analyzed in both papers, as they considered the second (Tombaugh 5_2
for us, 8099 in Baratella et al. 2018) a possible non member on the basis of
its RV and previous literature. The latter star has a high probability of
being a member according to Cantat-Gaudin et al. (2018) and the RVs measured
by us and Gaia RVS upport its membership. In Baratella et al. (2018)
temperature and gravity were derived spectroscopically and abundances were
measured using EWs, with estimated errors of 0.15-0.20 dex. They found an
average metallicity of 0.06 $\pm$ 0.11 dex. The mean value from our analysis
is 0.05 $\pm$ 0.05 dex. The agreement between the analyses for all atmospheric
parameters is reasonable, given the large difference in resolution between the
data sets (see Fig. 4).

#### NGC 2548 -

A recent paper Sun et al. (2020) is based on moderate-resolution spectra. They
report the radial and rotational velocities for nearly 300 stars in NGC 2548
(M48), measured with Hydra@WIYN spectra (R$\sim$13500) in a region about 40 nm
wide, around the Li i 670.8 nm line. The candidate cluster stars were selected
from CMDs based on UBVRI photometry and about two thirds resulted member, both
single or in binary/multiple systems, combining the spectroscopic data and
Gaia DR2 results. Temperature and gravity were derived from photometric data.
For a subsample of 99 well behaved, low rotational velocity, single cluster
members, the value of the metallicity was derived using 16 Fe i lines, finding
an average metallicity [Fe/H]=$-0.06\pm 0.007$ dex. This is in agreement with
$-0.02\pm 0.04$ dex from 4 stars in our work.

Two stars are in common, for which Sun et al. (2020) report
$T_{\textrm{eff}}$’s of 4912 K and 4549 K, with $\log g$ 4.64 and 4.69
respectively. We obtained $T_{\textrm{eff}}$’s 5370$\pm$110 K and 4930$\pm$60
K and our values for $\log g$ are much lower, 3.67$\pm$0.1 dex and
2.68$\pm$0.05 dex respectively, consistent with the stars being evolved rather
than in the main sequence. The gravities in Sun et al. (2020) are derived
using the Yale-Yonsei isochrones, an approach that leads to two possible
solutions, corresponding to an evolved or a main sequence star. Sun et al.
(2020) adopted the dwarf solution, and the reason behind this choice is not
discussed in the paper. However, given the position on the CMD of the observed
stars (see Fig. 2), we expect that the solution corresponding to an evolved
(and thus brighter) star is the most appropriate one.

#### Basel 11b -

Finally, among the 128 OCs published in Donor et al. (2020) and based on
SDSS/APOGEE DR16, there is Basel_11b. The paper presents the metallicity based
on one single star, [Fe/H]=0. For this star we determined [Fe/H]=-0.04 and the
mean iron abundance, based on 3 stars, is of $-0.013\pm 0.034$ dex. Further
comparison of atmospheric parameters is found in Tab. 6.

##  5 Discussion

The average metallicities for our sample of OCs are given in Tab. 5. These are
combined with the clusters positions (in the following, Galactocentric
distance and height on the plane) to discuss the metallicity distribution in
the disc, adding literature data to increase the sample and the coverage of
disc properties.

###  5.1 Metallicity distribution in the disc

The study of the radial and vertical fossil gradients such as age and
composition is one of the main approaches in probing the Galactic disc(s).

In this section, we discuss the observed behaviour of OCs’ metallicity, age
and Galactocentric distance and what information can be gained by the
comparison with models for the disc. First we derive the classical radial
gradient in metallicity (e.g. Friel et al. 2002), then we compare observations
with models. Our sample is distributed essentially on the Galactic plane, the
SPA clusters are all within 0.5 kpc (Cantat-Gaudin et al., 2020), and
generally in the solar neighbourhood, with Galactocentric distance ranging
between 7.7 to $\sim$10 kpc. With one single exception – NGC 2682, which was
observed for comparison purposes – the clusters are quite young, with 14 out
of 18 clusters being younger than 0.5 Gyr.

By combining our data with results from some selected surveys and studies, we
can widen the sample to clusters with larger ages and/or distances from the
Galactic centre, building a sample that allows us to probe the properties of
the Galactic disc, and can be used to constrain models of the chemo-dynamical
evolution of the disc. The SPA sample currently includes the present analysis
as well as four more OCs in Casali et al. (2020), one from D’Orazi et al.
(2020), and another one in Frasca et al. (2019). We include the results from
the large spectoscopic surveys, namely: a) APOGEE 777We downloaded the Value
Added Catalog for the Open Clusters Chemical Abundance and Mapping project
(OCCAM), at https://data.sdss.org/sas/dr16/apogee/vac/apogee-occam/ (Donor et
al., 2020), with more than 120 OCs, mostly in the 6-15 kpc range in Rgc and
with [Fe/H] from -0.5 to 0.4 dex; b) GES, covering the wide range of
Galactocentric distance from 5.8 to 20 kpc, and the metallicity range -0.5 to
0.4 dex; c) and GALAH (Spina et al., 2021), keeping only clusters whose
parameters are based on HERMES spectra and excluding those based on the
recalibration of APOGEE (see Spina et al. 2021 for details). Moreover, we
include the OCCASO sample (Casamiquela et al., 2017), whose OCs are located at
Galactocentric distances similar to those of the SPA project (6.5 to 10.5 kpc)
and with metallicities around the solar value, ranging from $-$0.1 dex to 0.17
dex.

The relationship between metallicity and Galactocentric distance, classically
called metallicity gradient, is shown in Fig. 5 for the combined sample,
dividing it in three age intervals (selected because they are the same in
Minchev et al. 2014b, see below). The metallicities come from the cited
sources (averaging values when more than one was available for a given
cluster) and the Rgc values come from Cantat-Gaudin et al. (2020) where
Rgc,Sun=8.34 form Reid et al. (2014). Table 6 contains the corresponding
gradients, for the whole sample and separated in age bins. As the slope shows
a change at about Rgc =14 kpc, we use that limit for inner/outer regions. The
same was done in past works, as can be seen from Tab. 8, where we list some
selected literature papers. We show the values in Friel et al. (2002), since
this is possibly the first analysis of an homogeneous and large spectroscopic
sample, albeit based on low resolution spectra, and in a few recent papers. As
every work uses different age bins and Rgc ranges, we can make only a
qualitative assessment of the results. However, the values in the two tables
compare generally well; a few discrepant values are commented in the notes of
Tab. 8.

Figure 6 shows metallicity as a function of distance from the Galactic plane,
divided in age bin like the previous figure. The calculated values are listed
in Table 6 as well. Note that, overall, the trends with $|z|$, even in well
populated bins (e.g. young clusters in the inner disk), is far less
statistically relevant than the corresponding slope wrt galactic radius.

We also explored possible residual azimuthal gradient (only for well populated
bins), finding extremely weak and moderately significant trends. For the
overall sample within 14 kpc, we found 0.0028$\pm$0.0007, while for the sample
below 2 Gyr within the same radius we found 0.0022$\pm$0.0009.

Table 6: Observed slope of the metallicity gradient. Age | Rgc | d[Fe/H]/dRGC | d[Fe/H]/d$|z|_{GC}$ | Nclusters
---|---|---|---|---
(Gyr) | (kpc) | (dex kpc-1) |  |
all ages | Rgc¡14 | -0.066$\pm 0.005$ | $-0.249\pm 0.062$ | 157
all ages | Rgc¿14 | -0.032$\pm 0.007$ | $-0.040\pm 0.028$ | 4
age¡2 | Rcg¡14 | -0.059$\pm 0.006$ | $-0.236\pm 0.119$ | 133
2¡age¡4 | Rgc¡14 | -0.089$\pm 0.007$ | $-0.195\pm 0.144$ | 13
age¿4 | Rgc¡14 | 0.008$\pm 0.041$ | $0.081\pm 0.117$ | 11

###  5.2 Comparison with chemo-dynamical models

It must be kept in mind that the chemo-dynamical evolution of the Galactic
disc(s) is a rather complex process and the expectation is that much of the
initial information will be diluted through the dynamical evolution and radial
mixing of the disc. This is expected to affect differently populations of
different ages: broadly speaking, the older a cluster is, the more it has
undergone dynamical evolution and radial mixing, even if other factors come
into play, like the details of the formation environment and more in general
the shape and mass of the disc at the time of formation and early evolution.

Recent models for the Galactic thin disc, e.g. Minchev et al. (2013, 2014a,
2014b), taking into account sophisticated simulations in the cosmological
context (Martig et al., 2009, 2012) along with a detailed knowledge of the
chemical evolution of the disc have been able to generate theoretical
gradients to compare with the observations at different ages. However,
uncertainties exist, also due to model assumptions (e.g. initial gradient of
chemical abundance), especially with regard to the oldest clusters and those
furthest from the Galactic plane, which have undergone a large amound of
dynamical evolution during their lives.

Minchev et al. (2014a) provide gradients calculated for different age
intervals and ranges of distance from the Galactic plane. Figure 7 shows the
comparison between the combined sample predictions from chemo-dynamical thin-
disc model by (Minchev et al., 2014, the so-called MCM model). These models
are calculated taking into account cosmological and dynamical properties, and
chemical evolution, combining the effects of migration, the distance above the
disc midplane, and then extending the model beyond the solar neighborhood.
Furthermore, the uncertainties of observation, and the evolution of Galactic
disc was considered. From the MCM models we consider the range 0-0.8 kpc in
terms of distance from the Galactic plane, which is appropriate for clusters
in SPA, but also for the vast majority of the literature sample. We plot the
predictions for both $|z|<0.3$ kpc and for 0.3 kpc$<|z|<$0.8 kpc.

Figures 7 and 8 show the comparison between MCM predictions and results from
observation data, grouped according to age in bins of 0.3 and 0.5 Gyr,
respectively, up to 4.5 Gyr. Predictions are generally a good match for the
observation in the age range from 0.3 Gyr to 4.5 Gyr. It is worth noting that
in the oldest age bin, two of the three discrepant clusters have —z—$>1$ kpc,
larger than the range the models have been calculated for, while the third has
a metallicity based on one single star. On the other hand, in the youngest bin
the fitting is very poor, even if the clusters lie closer than 0.5 kpc from
the Galactic plane; we will come back to this problem later. For a more
quantitative comparison with the Minchev et al. (2014b) results we derived the
gradients in our sample following the bins in age and Z in their Table 1,
considered the error of metallicities in the fitting process. We employed
their same separations in Rgc and [z] and computed the corresponding gradients
for the observed sample. Interestingly, the gradient computed for age younger
than 2 Gyr and —z— ¡ 0.25 kpc, i.e. the bin comprising the vast majority of
our sample (97 OCs), is comparable to that from the prediction: $-0.066\pm
0.008$ versus -0.057 for observed and predicted slope, respectively (see
below, however, for a caveat on the very young clusters). Values are instead
different in the other bins, which are, however, very scarcely populated, so
it is difficult to assess if the discrepancy is important.

Coming back to the very different distribution of OC metallicity for young
ages (already noticed in past work, e.g. Spina et al. 2017), several possible
factors might be the source(s) of the poor fit. On the observational side,
inaccurate measurements of the parameters could cause the issue. For example,
a systematic underestimate in the Galactic distances would lead to outer disc
clusters being compared to solar neighborhood predictions. A systematic
underestimate in Z, on the other hand, would mean that we would be using
predictions unsuitable for the sample. Rgc and Z are based on Gaia DR2 data,
with typical uncertainties of around 5% to 10% for distance (Cantat-Gaudin et
al., 2020), which is not large enough to generate the observed effect.
Moreover, if such a systematic errors existed, they would very likely affect
similarly clusters in the other age bins, a phenomenon of which there is no
evidence. We note that a systematic error in the adopted age could not in any
way explain the poor fit: the discrepant clusters with $<0.3$ Gyr would not be
reproduced by predictions of any of the plotted ages.

Another possibility is that (part of) the metallicities we are using in the
plot are not enough accurate. In particular we have checked in more detail
APOGEE cluster from Donor et al. (2020). Firstly, we have detected that for
several cluster there are significant difference between the two latest data
releases, DR16 (Donor et al., 2020) and DR14 (Donor et al., 2018; Carrera et
al., 2019), even if the same stars are used in both cases. This is for
instance the case of King 7, with [Fe/H]=-0.13$\pm$0.05 and -0.04$\pm$0.01 in
DR16 and DR14, respectively. These differences may be explained by the
different methodologies used in the two releases to obtain the final
abundances (see Jönsson et al., 2018, 2020, for a detailed explanation). For
several clusters the values are based only on a single star with either low
astrometric membership probability from Cantat-Gaudin et al. (2018), e.g.
Berkeley 79, Czernik 23, FSR 852, King 12, NGC 1857 and/or with discrepant
radial velocities in comparison with the literature, e.g. Czernik 23, NGC
2311, NGC 6383. Additionally, the star observed in NGC 2311 lies well out of
the cluster sequence and the star in NGC 6383 has a high rotation velocity
($>$20 km s-1) which complicates its analysis. In principle the APOGEE sample
includes two stars in NGC 2232 with giants atmospheric parameters but this
cluster does not have giants. In fact, they have a negligible astrometric
membership probability from Cantat-Gaudin et al. (2018) and their radial
velocities ($\sim$82 km s-1) are in disagreement with the average value
derived from 19 stars ($\sim$25 km s-1) with very high astrometric
probabilities from Gaia DR2 by Soubiran et al. (2018). In the case of the star
forming region NGC 1977, the DR16 metallicity [Fe/H]=-0.21 dex, derived from 3
stars (there is no determination in DR14), is in disagreement with the value
reported in the high quality high resolution spectroscopy by Netopil et al.
(2016) of [Fe/H]=-0.06 dex from 2 stars obtained from Cunha, Smith, & Lambert
(1995). In the case of NGC 2264, again without astrometric membership
probabilities available there is good agreement between APOGEE DR16
([Fe/H]=-0.18 dex from 23 stars) and the value determined by King et al.
(2000) ([Fe/H]=-0.18 dex from 3 stars) but they disagree with the recent
determination by Baratella et al. (2020) of [Fe/H]=+0.11 dex from a single
star observed with UVES as part of Gaia-ESO. Baratella et al. (2020) amply
discusses the problems of analysing spectra of MS stars in young stars and
tries to devise a more robust method. In contrast, there are other cases, such
as Berkeley 33, NGC 136, and SAI 116, whose metallicities seem reliable
although they are based only on 4, 1 and 2 stars, respectively. These stars
have a very high astrometric membership probability from Cantat-Gaudin et al.
(2018) and the derived radial velocities are in good agreement with other
values reported in the literature.

All these cases seem to indicate that we have to take the metallicity of young
clusters with care. We also tried to understand which ages are more
problematic. We show in Fig. 9 only clusters younger than 300 Myr, with the
symbol size proportional to the number of stars available in each cluster.
Apparently, the sample size is not the (main) source of the problem.

The clusters showing the larger discrepancy with respect to the model are
younger than 200 Myr, and especially younger than 100 Myr. This is affecting
analyses done both in the optical and the IR. As already discussed in
literature (Yana Galarza et al., 2019; Baratella et al., 2020; Spina et al.,
2020), in young dwarves chromospheric effects and considerable magnetic fields
are at play, making a traditional 1-D LTE analysis based on minimising trends
for Fe lines less than optimal. In fact, an intensification of strong
absorption lines, those forming near the top of the stellar photosphere where
the magnetic fields are more vigorous, has been observed as a function of the
activity level during the stellar cycle (Yana Galarza et al., 2019; Spina et
al., 2020). The cause of this effect can be imputed to Zeeman broadening of
atomic lines or the effect of cool stellar spots. However, it is possible that
many other phenomena related to the chromospheric activity, which are
neglected in stellar models, are simultaneously at work contributing to this
spectral variability. Interestingly, these problems for young stars have
essentially only been studied and discussed in the case of MS or PMS stars.
Our sample allow to shows clearly for the first time that the effect(s) extend
also to giants. Indeed, this is not surprising, as the challenges posed by the
modeling of atmospheres and spectra for giants are even more severe than those
for dwarfs.

##  6 Summary

This paper provides the atmospherical parameters for 40 red giants/red clumps
in 18 open clusters covering the Galactocentric distance range 7.7 ¡ Rgc ¡10
kpc. Almost all of them are young clusters, with 15 OCs between 40 and 600
Myr, two around 1.5 Gyr, and one (NGC 2682) at 4.2 Gyr. Their parameters were
measured using very high-resolution, high S/N HARPS-N spectra, the EW method
and 1D-LTE atmosphere models. The main results comprise:

(i) Very precise radial velocities with uncertainties of 0.05-0.25 km s-1 were
measured. The offset between our results and that from Gaia DR2 is -0.11$\pm$
1.9 km s-1.

(ii) Accurate stellar parameters were derived. For the stars located in the
effective temperature range of 4200-5800 K uncertainties in $T_{\textrm{eff}}$
are around 60 K, and 0.12 dex in surface gravity. All of the clusters have
metallicity close to solar, with a deviation within 0.05 dex.

(iii) We compared five of our clusters (Collinder 350, NGC 2682, Tombaugh 5,
NGC 2548, Basel 11b) with results from previous work, finding no systematic
bias in our determination. A few discrepant cases were examined.

(iv) We explored the trend between metallicities and Galactocentric distance
combining our data with clusters from APOGEE, GES, OCCASO, GALAH, and other
SPA OCs (ASCC 123, Gulliver 51, Ruprecht 171, NGC 2632, NGC 7044). We
confirmed the variation of slope in the metallicity gradient near Rgc=14 kpc
and found a gradient slope for the inner disc similar to other literature
works.

(v) We used the combined sample to compare to the chemo-dynamical predictions
by Minchev et al. (2014a), finding good agreement for clusters older than 0.3
Gyr. Conversely, younger clusters show a large dispersion, not predicted by
models, with observed metallicities too low for the Galactocentric position
(not explainable by radial mixing, given the young cluster age).

(vi) We examined the cases of young clusters with low metallicity, finding
that some results may be doubtful. However, there seems to be a general
difficulty in deriving accurate metallicities for clusters younger than about
200 Myr, both for dwarfs (confirming literature works) and giants. We
tentatively ascribe this to the impropriety of the traditional analysis for
young stars, as discussed in the previous section.

Table 7: Slope of the metallicity gradient from selected literature papers.

Reference | sample | d[Fe/H]/d Rgc | N OCs | Comment
---|---|---|---|---
Friel et al. (2002) | low-res optical spectra | -0.059$\pm 0.010$ | 39 | 7¡Rgc¡16 kpc, all agesa
Reddy et al. (2016) | high-res optical spectra | -0.052$\pm 0.011$ | 67 | 6¡Rgc¡12 kpc and $|z|<$500 pc
|  | -0.015$\pm 0.007$ | 12 | 12¡Rgc¿24 kpcb
Carrera et al. (2019) | APOGEE DR14, GALAH DR2 | -0.052$\pm 0.003$ | 46 | 6¡Rgc¡13 kpcc
|  | -0.077$\pm 0.007$ |  | 6¡Rgc¡11kpcc
|  | 0.018$\pm 0.009$ |  | 11¡Rgc¡13 kpcc
Casamiquela et al. (2019) | high-res optical spectra | -0.056$\pm 0.011$ | 18 | Rgc¡12 kpc, all agesd
Donor et al. (2020) | APOGEE DR16 | -0.068$\pm 0.004$ | 68 | Rgc¡ 13.9 kpc, all agese
|  | -0.009$\pm 0.011$ | 3 | Rgc¿13.9 kpc, all ages

888a the slope is also given in different age ranges, with a gradient
steepening for increased age: -0.023, -0.053, -0.075 for age $<$2, 2-4, and
$>$4 Gyr, respectively (a similar result was found by Andreuzzi et al. (2011)
using [Fe/H] from high resolution spectroscopy, who found values of -0.07 and
-0.15 for age lower and larger than 4 Gyr, respectively).
b They do not consider it representative of the disc midplane (their Sec. 5)
c The first value is obtained for OCs where at least 4 stars were measured,
the second and third to the whole sample. The gradient in the outer bin
becomes -0.04$\pm$0.01 if the two low metallicity OCs close to Rgc11 kpc are
excluded.
d No significant difference in the age ranges covered.
e Also divided for age: -0.50 (13 OCs, age$<$0.4 Gyr), -0.073 (16 OCs, 0.4-9,7
Gyr), -0.066 (27 OCs, 0.8-2 Gyr), and -0.094 (12 OCs, $>$2 Gyr).

Figure 4: Comparison of atmospheric parameters for targets with high
resolution spectroscopic determinations. We plot out $T_{\textrm{eff}}$ in the
x axis and the difference (our minus literature) and the error from literature
on the y-axis. In the right column we show the different sources in NGC 2682
and in the left column all other clusters. Ref: (1) Jacobson et al. (2011);
(2) APOGEE DR16; (3) (Casamiquela et al., 2017) EW; (4) Casamiquela et al.
(2017) SS; (5) Gao et al. (2018); (6) Luck (2015);(7) Spina et al. (2021)
Figure 5: The distribution of metallicity with Galactocentric distance in
three age bins (the same used in Minchev et al. 2014, see Sec. 5.2). Beside
our clusters, we show data from APOGEE (Donor et al., 2020), GES (Casali et
al., 2019), OCCASO (Casamiquela et al., 2017),and GALAH (Spina et al., 2021)
plus SPA results already published (Frasca et al., 2019; Casali et al., 2020;
D’Orazi et al., 2020). Figure 6: The distribution of metallicity with distance
from mid-plate in three age bins (the same used in Minchev et al. 2014, see
Sec. 5.2). Beside our clusters, we show data from APOGEE (Donor et al., 2020),
GES (Casali et al., 2019), OCCASO (Casamiquela et al., 2017),and GALAH (Spina
et al., 2021) plus SPA results already published (Frasca et al., 2019; Casali
et al., 2020; D’Orazi et al., 2020).

Figure 7: Comparison between model predictions (Minchev et al., 2014a, b) and
observation for young clusters. The red and the blue lines are predictions
from the MCM models for $|z|<0.3$ kpc and 0.3 $<|z|<$0.8 kpc respectively. The
colours in the symbol indicates the distance from the Galactic plane. All
considered clusters are within 0.6 kpc from the Galactic plane, with SPA
clusters being all within 0.5 kpc. The open ’diamond’ in the first panel is
ASCC 123 (Frasca et al., 2019) and the ’triangle’ symbol in 0.3-0.6 Gyr range
is NGC 2632 (D’Orazi et al., 2020) The fit is generally good for clusters
older than 0.3 Gyr, but the predictions fail to reproduce the data among the
very young clusters (see text). Figure 8: As in Fig. 7 for clusters bewteen
1.5 and 4.5 Gyr. Note the paucity of OCs older than 2.5 Gyr (in particular,
only one SPA cluster is present, Ruprecth 171, from Casali et al. 2020). The
data are quite well reproduced by the models; the exceptions are the old and
very metal poor clusters which have $|z|$ ¿ 1 kpc, further away from the
Galactic plane than the plotted models. Figure 9: As in Fig 5, but colour
coding the symbols by their age.The size of the symbols is proportional to the
number of stars studied in the cluster to obtain the mean value reported here.
(The open diamond represents ASCC 123 from Frasca et al. (2019)). The left
panel shows all the clusters younger than 300Myr, while the middle and right
panels show the fit without clusters younger than 100Myr and 200Myr,
respectively. The fit to the models is quite reasonable when considering
clusters older than 200Myr.

###### Acknowledgements.

We thank the TNG personnel for help during the observations and I. Minchev for
sharing his evolutionary models. This research used the facilities of the
Italian Center for Astronomical Archive (IA2) operated by INAF at the
Astronomical Observatory of Trieste. This work exploits the Simbad, Vizier,
and NASA-ADS databases and the software TOPCAT (Taylor, 2005). This work has
made use of data from the European Space Agency (ESA) mission Gaia
(https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and
Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has
been provided by national institutions, in particular the institutions
participating in the Gaia Multilateral Agreement. We acknowledge funding from
MIUR Premiale 2016 MITiC.

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##  Appendix A Data and results on individual stars

Table 8: Information on the observed targets based on Gaia DR2.  Name | Gaia ID | Ra | Dec | Gmag | BP-RP | Obs date | MJD-Obs | exp time |
---|---|---|---|---|---|---|---|---|---
|  | (J2000) | (J2000) |  |  |  |  | (s) |
ASCC 11 | 241730418805573760 | 03:32:04.87 | +44:57:45.4 | 8.3675 | 1.507 | 2018-12-16 | 58468.84 | 1200 |
|  |  |  |  |  | 2018-12-16 | 58468.82 | 1200 |
Alessi 1_1 | 402506369136008832 | 00:53:15.44 | +49:31:53.8 | 9.824 | 1.187 | 2018-12-19 | 58471.82 | 1800 |
|  |  |  |  |  | 2018-12-19 | 58471.84 | 1800 |
Alessi 1_2 | 402505991178890752 | 00:53:20.38 | +49:28:49.7 | 9.808 | 1.176 | 2018-12-19 | 58471.87 | 1800 |
|  |  |  |  |  | 2018-12-19 | 58471.89 | 1800 |
Alessi 1_3 | 402867593065772288 | 00:54:51.06 | +49:53:17.2 | 9.565 | 1.186 | 2018-12-18 | 58470.86 | 1500 |
|  |  |  |  |  | 2018-12-18 | 58470.88 | 1500 |
Alessi 1_4 | 402880684126058880 | 00:54:10.20 | +49:40:08.9 | 7.099 | 1.349 | 2018-12-19 | 58471.93 | 1800 |
|  |  |  |  |  | 2018-12-19 | 58471.95 | 1800 |
Alessi-Teusch 11 | 2184332753719499904 | 20:16:22.4 | +52:06:18.4 | 7.099 | 1.349 | 2019-08-15 | 58710.86 | 690 |
Basel 11b_1 | 3424056131485038592 | 05:58:08.10 | +21:57:44.7 | 10.989 | 1.782 | 2019-01-30 | 58513.90 | 3600 |
Basel 11b_2 | 3424055921028900736 | 05:58:10.12 | +21:57:23.2 | 11.256 | 1.876 | 2019-01-30 | 58513.95 | 1800 |
|  |  |  |  |  | 2019-01-30 | 58513.97 | 1800 |
|  |  |  |  |  | 2019-01-30 | 58513.99 | 1800 |
Basel 11b_3 | 3424057540234289408 | 05:58:18.16 | +21:58:43.7 | 11.320 | 1.884 | 2018-12-20 | 58472.19 | 1800 |
|  |  |  |  |  | 2018-12-20 | 58472.21 | 1800 |
|  |  |  |  |  | 2018-12-20 | 58472.24 | 1800 |
|  |  |  |  |  | 2018-12-20 | 58472.26 | 1800 |
COIN-Gaia 30 | 532533682228608384 | 01:24:05.26 | +70:25:25.1 | 8.352 | 1.762 | 2019-12-07 | 58824.85 | 1400 |
Collinder 463_1 | 534207555539397888 | 01:33:49.45 | +71:51:09.6 | 7.946 | 1.632 | 2018-12-19 | 58471.80 | 1200 |
Collinder 463_2 | 534363067715447680 | 01:45:09.15 | +71:53:25.3 | 7.725 | 1.572 | 2018-12-17 | 58469.81 | 1200 |
Gulliver 18 | 1836389309820904064 | 20:11:43.9 | 26:35:07.0 | 8.964 | 2.178 | 2019-08-10 | 58705.88 | 1380 |
Gulliver 24 | 430035249779499264 | 00:04:28.47 | +62:42:04.4 | 10.494 | 1.927 | 2019-08-12 | 58707.15 | 1500 |
|  |  |  |  |  | 2019-08-12 | 58707.16 | 1500 |
Gulliver 37 | 2024469226291472000 | 19:28:18.44 | +25:22:53.4 | 10.592 | 1.559 | 2019-08-13 | 58708.94 | 608 |
|  |  |  |  |  | 2019-08-13 | 58708.93 | 1500 |
NGC 2437_1 | 3029609393042459392 | 07:41:36.9 | -14:26:11.2 | 10.277 | 1.338 | 2018-12-17 | 58469.08 | 1800 |
|  |  |  |  |  | 2018-12-17 | 58469.10 | 1800 |
NGC 2437_2 | 3029202711180744832 | 07:41:28.52 | -14:54:17.5 | 10.293 | 1.285 | 2018-12-17 | 58469.12 | 1800 |
|  |  |  |  |  | 2018-12-17 | 58469.15 | 1800 |
NGC 2437_3 | 3030364134752459904 | 07:41:00.64 | -14:12:08.4 | 10.417 | 1.274 | 2018-12-17 | 58469.17 | 1800 |
|  |  |  |  |  | 2018-12-17 | 58469.19 | 1800 |
NGC 2437_4 | 3029132686034894592 | 07:42:47.85 | -15:17:44.16 | 10.838 | 1.302 | 2018-12-18 | 58470.18 | 2400 |
|  |  |  |  |  | 2018-12-18 | 58470.20 | 2400 |
NGC 2437_5 | 3029156222454419072 | 07:42:41.24 | -14:59:51.4 | 10.948 | 1.262 | 2018-12-19 | 58471.17 | 2400 |
|  |  |  |  |  | 2018-12-19 | 58471.20 | 2400 |
NGC 2437_6 | 3029207006148017664 | 07:41:19.42 | -14:48:47.5 | 9.877 | 1.332 | 2018-12-18 | 58470.10 | 1500 |
|  |  |  |  |  | 2018-12-18 | 58470.12 | 1500 |
NGC 2437_7 | 3029226694277998080 | 07:41:19.36 | -14:40:59.7 | 9.975 | 1.322 | 2018-12-08 | 58470.14 | 1500 |
|  |  |  |  |  | 2018-12-08 | 58470.16 | 1500 |
NGC 2509 | 5714209934411718784 | 08:00:44.36 | -19:06:59.4 | 12.883 | 1.275 | 2019-01-15 | 58498.07 | 2400 |
|  |  |  |  |  | 2019-01-15 | 58498.10 | 2400 |
|  |  |  |  |  | 2019-01-15 | 58498.13 | 2400 |
NGC 2548_1 | 3064481400744808704 | 08:13:35.42 | -05:53:02.04 | 9.377 | 1.098 | 2018-12-17 | 58469.24 | 1200 |
|  |  |  |  |  | 2018-12-17 | 58469.25 | 1200 |
NGC 2548_2 | 3064537647636773760 | 08:12:37.24 | -05:40:51.0 | 9.151 | 1.111 | 2018-12-18 | 58470.24 | 1200 |
|  |  |  |  |  | 2018-12-18 | 58470.25 | 1200 |
NGC 2548_3 | 3064579703955646976 | 08:14:28.10 | -05:42:16.09 | 9.187 | 1.190 | 2018-12-19 | 58471.23 | 1200 |
|  |  |  |  |  | 2018-12-19 | 58471.25 | 1200 |
NGC 2548_4 | 3064486692144030336 | 08:13:40.44 | -05:46:24.96 | 8.873 | 0.786 | 2018-12-17 | 58469.27 | 1800 |
NGC 7082_1 | 1972288740859811072 | 21:28:48.97 | +47:06:54.2 | 8.171 | 1.508 | 2019-12-08 | 58825.80 | 1400 |
NGC 7082_2 | 1972288637780285312 | 21:28:34.58 | +47:05:22.92 | 7.622 | 1.470 | 2019-12-07 | 58824.81 | 1400 |
NGC 7209_1 | 1975004019170020736 | 22:05:09.94 | +46:31:25.3 | 9.766 | 1.323 | 2019-08-14 | 58709.14 | 1725 |
|  |  |  |  |  | 2019-08-14 | 58709.16 | 1725 |
NGC 7209_2 | 1975002919658397568 | 22:05:17.63 | +46:29:00.6 | 8.891 | 1.734 | 2019-08-14 | 58709.12 | 2070 |
Tombaugh 5_1 | 473266779976916480 | 03:47:30.99 | +59:02:50.8 | 11.163 | 2.123 | 2018-12-20 | 58472.11 | 1800 |
|  |  |  |  |  | 2018-12-20 | 58472.09 | 1800 |
|  |  |  |  |  | 2018-12-20 | 58472.15 | 1800 |
|  |  |  |  |  | 2018-12-20 | 58472.13 | 1800 |
Tombaugh 5_2 | 473275782228263296 | 03:48:32.98 | +59:15:16.56 | 11.366 | 2.147 | 2019-01-15 | 58497.06 | 1800 |
|  |  |  |  |  | 2019-01-14 | 58497.04 | 1800 |
|  |  |  |  |  | 2019-01-15 | 58498.00 | 1800 |
Tombaugh 5_3 | 473268424940932864 | 03:47:46.78 | +59:05:36.6 | 11.138 | 2.028 | 2019-01-14 | 58498.03 | 1800 |
|  |  |  |  |  | 2019-01-14 | 58497.98 | 1800 |
|  |  |  |  |  | 2019-01-14 | 58497.02 | 1800 |
UPK 219 | 2209440823287736064 | 23:30:29.72 | +65:08:35.3 | 8.728 | 1.734 | 2019-12-08 | 58825.84 | 1400 |
Comparison clusters |
Collinder 350_1 | 4372743213795720704 | 17:46:24.88 | +01:02:39.7 | 5.957 | 1.869 | 2018-08-20 | 58350.87 | 300 |
|  |  |  |  |  | 2018-08-20 | 58350.88 | 300 |
Collinder 350_2 | 4372572888274176768 | 17:48:43.82 | +01:09:51.1 | 8.421 | 1.485 | 2018-08-20 | 58350.88 | 1380 |
NGC 2682_1 | 604921512005266048 | 08:51:26.17 | +11:53:51.9 | 10.202 | 1.238 | 2020-02-02 | 58881.07 | 1500 |
|  |  |  |  |  | 2020-02-02 | 58881.08 | 1500 |
NGC 2682_2 | 604920202039656064 | 08:51:59.51 | +11:55:04.8 | 10.205 | 1.243 | 2020-02-02 | 58881.10 | 1500 |
|  |  |  |  |  | 2020-02-02 | 58881.12 | 1500 |
NGC 2682_3 | 604904950611554432 | 08:51:50.19 | +11:46:06.9 | 10.511 | 1.110 | 2020-02-02 | 58881.14 | 1500 |
|  |  |  |  |  | 2020-02-02 | 58881.16 | 1500 |
|  |  |  |  |  | 2019-12-08 | 58825.19 | 1500 |
|  |  |  |  |  | 2019-12-08 | 58825.21 | 1500 |
|  |  |  |  |  | 2019-12-08 | 58825.22 | 1500 |
|  |  |  |  |  | 2020-03-11 | 58919.04 | 1500 |
|  |  |  |  |  | 2020-03-11 | 58919.06 | 1500 |
NGC 2682_4 | 604917728138508160 | 08:51:23.76 | +11:49:49.3 | 11.231 | 1.073 | 2020-03-10 | 58918.99 | 1420 |
|  |  |  |  |  | 2020-03-11 | 58919.00 | 1420 |
|  |  |  |  |  | 2020-03-11 | 58919.02 | 1420 |

Table 9: Final atmospheric parameters of observed targets Name | Gaia ID | $T_{\textrm{eff}}$ |  $\sigma$ $T_{\textrm{eff}}$ |  $\log$ g | $\sigma$ | [Fe/H] | $\sigma_{1}$ | $\sigma_{2}$ | $\log\epsilon$ | std | Nlines | $\log\epsilon$ | std | Nlines | vmicro | $\sigma$
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
|  | (K) |  | (dex) |  $\log$ g | (dex) | [Fe/H] | [Fe/H] | Fe I |  | Fe I | Fe II |  | Fe II | (km s-1) | vmicro
ASCC 11 | 241730418805573760 | 5250 | 70 | 2.15 | 0.10 | -0.14 | 0.05 | 0.06 | 7.360 | 0.159 | 46 | 7.360 | 0.210 | 10 | 2.4 | 0.3
Alessi 1_1 | 402506369136008832 | 5000 | 70 | 2.65 | 0.15 | -0.10 | 0.05 | 0.05 | 7.392 | 0.181 | 74 | 7.391 | 0.136 | 16 | 1.5 | 0.1
Alessi 1_2 | 402505991178890752 | 5200 | 70 | 3.20 | 0.10 | 0.08 | 0.10 | 0.10 | 7.583 | 0.135 | 75 | 7.587 | 0.128 | 15 | 1.5 | 0.2
Alessi 1_3 | 402867593065772288 | 5250 | 65 | 3.27 | 0.10 | 0.07 | 0.06 | 0.06 | 7.576 | 0.105 | 75 | 7.575 | 0.098 | 15 | 1.6 | 0.1
Alessi 1_4 | 402880684126058880 | 5120 | 60 | 3.09 | 0.06 | -0.05 | 0.10 | 0.10 | 7.470 | 0.122 | 72 | 7.476 | 0.089 | 16 | 1.6 | 0.1
Alessi-Teusch 11 | 2184332753719499904 | 4560 | 50 | 2.10 | 0.15 | -0.19 | 0.05 | 0.05 | 7.310 | 0.172 | 75 | 7.311 | 0.219 | 16 | 2.1 | 0.2
Basel 11b_1 | 3424056131485038592 | 5180 | 50 | 3.15 | 0.13 | 0.00 | 0.05 | 0.05 | 7.500 | 0.093 | 68 | 7.507 | 0.127 | 13 | 2.2 | 0.1
Basel 11b_2 | 3424055921028900736 | 5220 | 30 | 3.07 | 0.10 | 0.02 | 0.03 | 0.03 | 7.525 | 0.141 | 72 | 7.529 | 0.135 | 15 | 2.2 | 0.2
Basel 11b_3 | 3424057540234289408 | 4950 | 50 | 2.83 | 0.10 | -0.04 | 0.05 | 0.05 | 7.464 | 0.136 | 77 | 7.467 | 0.116 | 15 | 2.1 | 0.1
COIN-Gaia 30 | 532533682228608384 | 5200 | 50 | 3.40 | 0.05 | 0.03 | 0.05 | 0.05 | 7.538 | 0.158 | 71 | 7.552 | 0.163 | 15 | 2.3 | 0.2
Collinder 463_1 | 534207555539397888 | 4730 | 30 | 2.12 | 0.10 | -0.20 | 0.05 | 0.05 | 7.310 | 0.130 | 72 | 7.310 | 0.180 | 16 | 2.5 | 0.1
Collinder 463_2 | 534363067715447680 | 4730 | 30 | 2.30 | 0.15 | -0.10 | 0.05 | 0.05 | 7.410 | 0.131 | 70 | 7.410 | 0.207 | 16 | 2.4 | 0.1
Gulliver 18 | 1836389309820904064 | 4590 | 100 | 2.60 | 0.17 | -0.10 | 0.04 | 0.05 | 7.409 | 0.212 | 76 | 7.395 | 0.281 | 16 | 2.8 | 0.3
Gulliver 24 | 430035249779499264 | 4450 | 50 | 2.50 | 0.15 | -0.18 | 0.03 | 0.03 | 7.317 | 0.145 | 74 | 7.316 | 0.240 | 16 | 2.0 | 0.2
Gulliver 37 | 2024469226291472000 | 4850 | 50 | 3.65 | 0.12 | 0.10 | 0.03 | 0.04 | 7.602 | 0.213 | 62 | 7.604 | 0.403 | 10 | 0.8 | 0.6
NGC 2437_1 | 3029609393042459392 | 5050 | 50 | 2.77 | 0.12 | 0.04 | 0.05 | 0.05 | 7.546 | 0.142 | 64 | 7.548 | 0.109 | 12 | 2.1 | 0.1
NGC 2437_2 | 3029202711180744832 | 5250 | 75 | 3.32 | 0.10 | 0.07 | 0.05 | 0.05 | 7.572 | 0.140 | 62 | 7.578 | 0.171 | 13 | 2.2 | 0.2
NGC 2437_3 | 3030364134752459904 | 5300 | 110 | 3.13 | 0.10 | -0.05 | 0.05 | 0.05 | 7.450 | 0.176 | 62 | 7.447 | 0.149 | 13 | 2.3 | 0.2
NGC 2437_4 | 3029132686034894592 | 5085 | 65 | 2.90 | 0.10 | 0.00 | 0.05 | 0.05 | 7.508 | 0.111 | 62 | 7.498 | 0.089 | 15 | 1.7 | 0.1
NGC 2437_5 | 3029156222454419072 | 5030 | 75 | 2.85 | 0.15 | 0.00 | 0.10 | 0.10 | 7.503 | 0.163 | 74 | 7.505 | 0.161 | 16 | 1.7 | 0.1
NGC 2437_6 | 3029207006148017664 | 4990 | 65 | 2.72 | 0.10 | 0.00 | 0.05 | 0.06 | 7.443 | 0.238 | 72 | 7.444 | 0.266 | 15 | 2.1 | 0.1
NGC 2437_7 | 3029226694277998080 | 4650 | 90 | 2.27 | 0.20 | -0.07 | 0.08 | 0.08 | 7.440 | 0.216 | 73 | 7.440 | 0.272 | 16 | 1.2 | 0.1
NGC 2509 | 5714209934411718784 | 4705 | 40 | 2.53 | 0.20 | -0.10 | 0.05 | 0.06 | 7.394 | 0.213 | 72 | 7.392 | 0.330 | 16 | 1.5 | 0.3
NGC 2548_1 | 3064481400744808704 | 5370 | 70 | 3.67 | 0.15 | 0.00 | 0.05 | 0.05 | 7.507 | 0.131 | 60 | 7.504 | 0.155 | 15 | 2.0 | 0.2
NGC 2548_2 | 3064537647636773760 | 5050 | 50 | 2.65 | 0.10 | -0.02 | 0.04 | 0.04 | 7.479 | 0.068 | 62 | 7.480 | 0.101 | 15 | 1.6 | 0.1
NGC 2548_3 | 3064579703955646976 | 4930 | 50 | 2.70 | 0.10 | -0.01 | 0.05 | 0.05 | 7.490 | 0.079 | 61 | 7.491 | 0.083 | 13 | 1.6 | 0.1
NGC 2548_4 | 3064486692144030336 | 5200 | 50 | 3.18 | 0.10 | -0.07 | 0.07 | 0.07 | 7.434 | 0.110 | 62 | 7.439 | 0.142 | 14 | 0.4 | 0.1
NGC 7082 | 1972288740859811072 | 4994 | 50 | 2.25 | 0.1 | -0.15 | 0.05 | 0.07 | 7.360 | 0.144 | 63 | 7.361 | 0.199 | 12 | 3.0 | 0.2
NGC 7209_1 | 1975004019170020736 | 4880 | 50 | 2.35 | 0.15 | 0.00 | 0.03 | 0.07 | 7.439 | 0.110 | 74 | 7.433 | 0.155 | 16 | 1.7 | 0.2
NGC 7209_2 | 1975002919658397568 | 4600 | 30 | 2.79 | 0.15 | -0.07 | 0.05 | 0.05 | 7.433 | 0.136 | 67 | 7.435 | 0.316 | 16 | 2.5 | 0.2
Tombaugh 5_1 | 473266779976916480 | 5010 | 50 | 3.17 | 0.15 | 0.04 | 0.05 | 0.05 | 7.543 | 0.166 | 72 | 7.546 | 0.185 | 15 | 2.2 | 0.1
Tombaugh 5_2 | 473275782228263296 | 4900 | 50 | 2.31 | 0.1 | -0.07 | 0.05 | 0.01 | 7.438 | 0.080 | 74 | 7.436 | 0.073 | 14 | 2.0 | 0.2
Tombaugh 5_3 | 473266779976916480 | 5150 | 50 | 3.08 | 0.15 | 0.07 | 0.05 | 0.05 | 7.570 | 0.095 | 69 | 7.576 | 0.147 | 15 | 2.2 | 0.2
UPK 219 | 2209440823287736064 | 5203 | 150 | 3.01 | 0.10 | 0.02 | 0.05 | 0.05 | 7.528 | 0.166 | 74 | 7.530 | 0.283 | 16 | 2.7 | 0.2
Collinder 350_1 | 4372743213795720704 | 4200 | 50 | 1.30 | 0.20 | -0.40 | 0.08 | 0.08 | 7.120 | 0.149 | 71 | 7.130 | 0.256 | 16 | 2.2 | 0.1
Collinder 350_2 | 4372572888274176768 | 5300 | 50 | 3.15 | 0.1 | 0.02 | 0.05 | 0.05 | 7.521 | 0.079 | 71 | 7.526 | 0.095 | 15 | 1.6 | 0.1
NGC 2682_1 | 604921512005266048 | 4687 | 50 | 2.37 | 0.07 | -0.05 | 0.05 | 0.05 | 7.454 | 0.174 | 75 | 7.453 | 0.181 | 16 | 1.5 | 0.2
NGC 2682_2 | 604920202039656064 | 4900 | 110 | 2.76 | 0.1 | 0.02 | 0.05 | 0.05 | 7.520 | 0.132 | 69 | 7.528 | 0.168 | 15 | 1.7 | 0.2
NGC 2682_3 | 604904950611554432 | 5000 | 50 | 2.77 | 0.1 | -0.03 | 0.03 | 0.03 | 7.467 | 0.096 | 67 | 7.467 | 0.082 | 14 | 1.2 | 0.2
NGC 2682_4 | 604917728138508160 | 5195 | 50 | 3.25 | 0.10 | 0.00 | 0.05 | 0.03 | 7.501 | 0.108 | 67 | 7.502 | 0.164 | 14 | 1.3 | 0.2
SUN |  | 5770 | 40 | 4.44 | 0.08 | -0.03 | 0.03 | 0.03 | 7.477 | 0.077 | 76 | 7.477 | 0.083 | 16 | 1.0 | 0.1
999$\sigma_{1}[Fe/H]$ is the error from metallicity measurement,
$\sigma_{2}[Fe/H]$ is the deviation of iron abundance combined to the
uncertainties of EW and error in metallicity determination