11institutetext: Institut d’Estudis Espacials de Catalunya (IEEC), Gran
Capità, 2-4, 08034 Barcelona, Spain
11email<EMAIL_ADDRESS>22institutetext: Institut de Ciències del Cosmos
(ICCUB), Universitat de Barcelona (UB), Martí i Franquès, 1, 08028 Barcelona,
Spain 33institutetext: Departament de Física Quàntica i Astrofísica (FQA),
Universitat de Barcelona (UB), Martí i Franquès, 1, 08028 Barcelona, Spain
44institutetext: Observatoire de Paris (Meudon) GEPI Batiment 11, Hipparque 5
Place Jules Janssen, 92195, Meudon, France 55institutetext: INAF -
Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Gobetti
93/3, 40129, Bologna, Italy 66institutetext: Instituto de Astrofísica de
Canarias, E-38205 La Laguna, Tenerife, Spain 77institutetext: Universidad de
La Laguna, Avda. Astrofísico Fco. Sánchez, E-38205 La Laguna, Tenerife, Spain
88institutetext: INAF – Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5,
50125 Firenze, Italy 99institutetext: Institute of Theoretical Physics and
Astronomy, Vilnius University, Sauletekio av. 3, 10257 Vilnius, Lithuania
1010institutetext: Max-Planck-Institut für Astronomie, Königstuhl 17, 69117
Heidelberg, Germany 1111institutetext: Harvard-Smithsonian Center for
Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA

# OCCASO V.

Chemical-abundance trends with Galactocentric distance and age111Tables 1, A1,
A2 and A4 are only available in electronic form at the CDS via anonymous ftp
to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-
bin/qcat?J/A+A/.

J. Carbajo-Hijarrubia 112233 L. Casamiquela 11223344 R. Carrera 55 L.
Balaguer-Núñez 112233 C. Jordi 112233 F. Anders 112233
C. Gallart 6677 E. Pancino 88 A. Drazdauskas 99 E. Stonkutė 99 G. Tautvaišienė
99 J.M. Carrasco 112233 E. Masana 112233
T. Cantat-Gaudin 1010 S. Blanco-Cuaraesma 1111

###### Abstract

Aims.

Methods.

Results.

###### Abstract

Context. Open clusters provide valuable information on stellar nucleosynthesis
and the chemical evolution of the Galactic disc, as their age and distances
can be measured more precisely with photometry than for field stars.

Aims. Our aim is to study the chemical distribution of the Galactic disc using
open clusters by analysing the existence of gradients with Galactocentric
distance, azimuth or height from the plane and dependency with age.

Methods. High-resolution spectra ($R$¿60 000) of 194 stars belonging to 36
open clusters are used to determine atmospheric parameters and chemical
abundances with two independent methods: equivalent widths and spectral
synthesis. The sample has been complemented with 63 clusters with high-
resolution spectroscopy from literature.

Results. We measure local thermodynamic equilibrium abundances for 21
elements: $\alpha$ (Mg, Si, Ca, and Ti), odd-Z (Na and Al), Fe-peak (Fe, Sc,
V, Cr, Mn, Co, Ni, Cu, and Zn), and neutron-capture (Sr, Y, Zr, Ba, Ce, and
Nd). We also provide non-local thermodynamic equilibrium abundances for
elements when corrections are available. We find inner disc young clusters
enhanced in [Mg/Fe] and [Si/Fe] compared to other clusters of their age. For
[Ba/Fe] we report an age trend flattening for older clusters (age$<$2.5 Ga).
The studied elements follow the expected radial gradients as a function of
their nucleosynthesis groups, which are significantly steeper for the oldest
systems. For the first time, we investigate the existence of an azimuthal
gradient, finding some hints of its existence among the old clusters (age$>$2
Ga).

###### Key Words.:

Stars: abundances – Galaxy: open clusters and associations: general – Galaxy:
abundances – Galaxy: evolution – Galaxy: disc

##  1 Introduction

Various processes involved in the formation and evolution of the Galactic disc
have left their imprint on the chemo-dynamical properties of the stars that
populate it. Several tracers have been used to unveil these processes such as
H ii regions (e.g., Balser et al. 2011; Arellano-Córdova et al. 2020; Méndez-
Delgado et al. 2022), planetary nebulae (e.g. Maciel et al. 2007; Stanghellini
& Haywood 2018), Cepheids (e.g. Genovali et al. 2014; Luck 2018; Minniti et
al. 2020; da Silva et al. 2022), low-mass stars (e.g. Boeche et al. 2014;
Anders et al. 2017), massive stars (e.g. Daflon & Cunha 2004; Bragança et al.
2019) or star clusters (e.g., Janes 1979; Friel et al. 2002). Open clusters
(OCs) have proved particularly useful as some of their properties such as age
and distance can be more precisely determined than for other tracers.
Moreover, the OC population covers almost the full disc lifetime, allowing to
trace the overall disc history of star formation and nucleosynthesis (e.g.,
Friel 2013).

Studies based on OCs have provided valuable information about the chemical
distribution of the disc stars, such as a decreasing metallicity with
increasing Galactocentric position (e.g., Janes 1979; Bragaglia et al. 2008;
Sestito et al. 2008; D’Orazi et al. 2009; Jacobson et al. 2011). However,
these initial studies were hampered by the small number of systems studied
homogeneously (e.g., Pancino et al. 2010; Jacobson et al. 2011). Other authors
built larger samples by compiling values in the literature, but they were
limited by their heterogeneity (e.g., Carrera & Pancino 2011; Yong et al.
2012; Donati et al. 2015; Heiter et al. 2015; Netopil et al. 2016). These
issues have been overcome in the last years by the Gaia mission data (_Gaia_
Collaboration et al. 2016) and the massive ground-based spectroscopic surveys
to complement them. Gaia Collaboration et al. (2023a) studied a sample of 503
OCs older than 100 Ma within a Galactocentric radius ($R_{\rm GC}$) of
$\sim$12 kpc based on the third Gaia Data Release (DR3, Gaia Collaboration et
al. 2023b). Unfortunately, Gaia only provides abundances for a few elements
due to its medium spectral resolution, $R\sim$11 500, and the small wavelength
coverage, 845 to 872 nm (_Gaia_ Collaboration et al. 2016).

High resolution, $R$¿20 000, ground-based spectroscopic surveys are providing
radial velocities and chemical abundances with a better precision than Gaia.
Currently, only GES (Gaia-ESO survey, Gilmore et al. 2012), APOGEE (Apache
Point Observatory Galactic Evolution Experiment, Majewski et al. 2017), and
GALAH (GALactic Archaeology with HERMES, De Silva et al. 2015) have published
data. GES and GALAH are sampling the Southern Hemisphere, while APOGEE is
observing both hemispheres using twin telescopes and instruments. APOGEE and
GALAH have not made observations specifically designed to study open clusters,
which means that the stars studied are in different evolutionary states and
therefore show different abundances due to stellar evolution. In addition, the
number of sampled stars varies greatly between OCs, with many of them having
abundances for a single star. Of the 150 OCs observed in APOGEE (Myers et al.
2022) only 47 have abundances of at least four stars. In the case of GALAH,
from the 75 observed systems (Spina et al. 2021) only 14 OCs have measurements
of at least four stars.

GES used two different instruments: GIRAFFE with a spectral resolution similar
to APOGEE and GALAH, and UVES (Ultraviolet and Visual Echelle Spectrograph,
Dekker et al. 2000) with a spectral resolution of $\sim$47 000 covering a
wavelength range between 480 and 680 nm. Recently, Magrini et al. (2023,
hereafter GES23) have studied a sample of 62 OCs older than 100 Ma acquired
with GES-UVES. Their work provides abundances for 25 chemical elements,
including a number of neutron capture elements such as Y, Zr Mo or Pr.
However, GES has sampled only the Southern Hemisphere, while several key OCs,
such as NGC 6791, are only available for Northern observers.

Since 2013, we are systematically observing stars in OCs in the framework of
the Open Clusters Chemical Abundances from Spanish Observatories (OCCASO)
project. Its main driver is to study the kinematic and chemical evolution of
the Galactic disc by measuring radial velocities and detailed elemental
abundances. For this purpose, we study at least four red clump (RC) stars by
high-resolution spectroscopy, $R$¿60 000, and covering a large wavelength
range, 400 to 900 nm (Casamiquela et al. 2016, hereafter Paper I). Atmospheric
parameters and Fe abundances for 115 stars belonging to 18 OCs were determined
by Casamiquela et al. (2017, hereafter Paper II). Casamiquela et al. (2019,
hereafter Paper III) extended this by deriving abundances of five Fe-peak and
five $\alpha$ elements for the same sample. Radial velocities for 336 stars
belonging to 51 OCs have been presented by Carrera et al. (2022, hereafter
Paper IV).

The present paper is the fifth of the series directly based on OCCASO spectra.
We publish chemical abundances for 36 OCs observed until December 2020,
doubling the number of OCs published in Paper III. We provide improved
abundances for 21 chemical elements and, for the first time in the project,
the odd-z elements Na, and Al, the Fe-peak elements Mn, Cu and Zn, and neutron
capture elements Sr, Y, Zr, Ba, Ce, Nd.

The paper is organised as follows. The target selection, observations, and
data reduction are explained in Sect. 2. The spectroscopic determination of
atmospheric parameters is detailed in Sect. 2. The chemical abundance
computation methods, solar abundance scale, and cluster abundances are
explained in Sect. 4. Trends in the Galactic disc are investigated in Sect. 5.
Finally, general conclusions are discussed in Sect. 6.

##  2 Observations and methodology

###  2.1 Observational material

Table 1: Properties of the 36 OCCASO OCs in this work. Positions and ages were computed by Cantat-Gaudin et al. (2020) from Gaia DR2 (_Gaia_ Collaboration et al. 2018). Examples of abundances derived in the present work and the number of stars studied in each cluster are shown in the last columns of the printed table. The complete table with the LTE abundances of the 21 elements plus the NLTE of those available is published at the CDS. Cluster | $\alpha_{\rm ICRS}$ | $\delta_{\rm ICRS}$ | Age | Distance | _X_ | _Y_ | _Z_ | $R_{\rm GC}$ | [Fe/H] | [Mg/Fe] | $N$
---|---|---|---|---|---|---|---|---|---|---|---
| [deg] | [deg] | [Ga] | [pc] | [pc] | [pc] | [pc] | [pc] | [dex] | [dex] |
Berkeley 17 | 80.13 | 30.574 | 7.2 | 3341 | -3325 | 252 | -214 | 11668 | -0.24$\pm$0.04 | 0.11$\pm$0.28 | 4
FSR 0278 | 307.761 | 51.021 | 2.2 | 1708 | 63 | 1695 | 202 | 8448 | 0.15$\pm$0.05 | -0.06$\pm$0.03 | 5
FSR 0850 | 86.257 | 24.74 | 0.5 | 2232 | -2226 | -136 | -89 | 10567 | -0.01$\pm$0.02 | -0.03$\pm$0.12 | 3
IC 4756 | 279.649 | 5.435 | 1.3 | 506 | 406 | 299 | 47 | 7938 | -0.03$\pm$0.01 | -0.08$\pm$0.01 | 7
NGC 188 | 11.798 | 85.244 | 7.1 | 1698 | -851 | 1319 | 646 | 9285 | -0.03$\pm$0.07 | 0.04$\pm$0.06 | 4
NGC 752 | 29.223 | 37.794 | 1.2 | 483 | -324 | 303 | -191 | 8669 | -0.02$\pm$0.02 | -0.08$\pm$0.03 | 7
NGC 1817 | 78.139 | 16.696 | 1.1 | 1799 | -1742 | -189 | -405 | 10084 | -0.16$\pm$0.03 | -0.01$\pm$0.02 | 5
NGC 1907 | 82.033 | 35.33 | 0.6 | 1618 | -1605 | 207 | 8 | 9947 | -0.05$\pm$0.02 | 0.00$\pm$0.03 | 4
NGC 2099 | 88.074 | 32.545 | 0.4 | 1432 | -1429 | 58 | 77 | 9769 | 0.06$\pm$0.03 | -0.07$\pm$0.01 | 8
NGC 2354 | 108.503 | -25.724 | 1.4 | 1370 | -713 | -1158 | -163 | 9127 | -0.03$\pm$0.04 | -0.03$\pm$0.03 | 6
NGC 2355 | 109.247 | 13.772 | 1.0 | 1941 | -1744 | -753 | 397 | 10112 | -0.10$\pm$0.04 | -0.02$\pm$0.02 | 6
NGC 2420 | 114.602 | 21.575 | 1.7 | 2587 | -2316 | -757 | 869 | 10683 | -0.22$\pm$0.03 | 0.02$\pm$0.02 | 7
NGC 2539 | 122.658 | -12.834 | 0.7 | 1228 | -713 | -971 | 236 | 9105 | 0.05$\pm$0.06 | -0.10$\pm$0.03 | 5
NGC 2632 | 130.054 | 19.621 | 0.7 | 183 | -139 | -67 | 98 | 8479 | 0.23$\pm$0.04 | -0.09$\pm$0.02 | 4
NGC 2682 | 132.846 | 11.814 | 4.3 | 889 | -613 | -440 | 470 | 8964 | 0.04$\pm$0.04 | -0.03$\pm$0.02 | 8
NGC 6633 | 276.845 | 6.615 | 0.7 | 424 | 339 | 247 | 61 | 8004 | -0.02$\pm$0.03 | -0.06$\pm$0.01 | 4
NGC 6645 | 278.158 | -16.918 | 0.5 | 1810 | 1739 | 490 | -113 | 6618 | 0.12$\pm$0.03 | -0.04$\pm$0.05 | 6
NGC 6705 | 282.766 | -6.272 | 0.3 | 2203 | 1955 | 1009 | -106 | 6464 | 0.11$\pm$0.07 | -0.03$\pm$0.03 | 12
NGC 6728 | 284.715 | -8.953 | 0.6 | 1829 | 1638 | 791 | -181 | 6747 | 0.02$\pm$0.01 | -0.06$\pm$0.03 | 5
NGC 6791 | 290.221 | 37.778 | 6.3 | 4231 | 1423 | 3903 | 800 | 7942 | 0.15$\pm$0.14 | -0.06$\pm$0.25 | 6
NGC 6811 | 294.34 | 46.378 | 1.1 | 1161 | 212 | 1116 | 241 | 8203 | -0.03$\pm$0.02 | -0.06$\pm$0.02 | 6
NGC 6819 | 295.327 | 40.19 | 2.2 | 2765 | 754 | 2628 | 407 | 8027 | 0.04$\pm$0.06 | -0.05$\pm$0.05 | 4
NGC 6939 | 307.917 | 60.653 | 1.7 | 1815 | -182 | 1764 | 386 | 8703 | 0.03$\pm$0.06 | 0.00$\pm$0.06 | 5
NGC 6940 | 308.626 | 28.278 | 1.3 | 1101 | 376 | 1026 | -137 | 8029 | 0.14$\pm$0.05 | -0.06$\pm$0.03 | 6
NGC 6991 | 313.621 | 47.4 | 1.5 | 577 | 26 | 576 | 15 | 8333 | -0.03$\pm$0.01 | -0.06$\pm$0.01 | 3
NGC 6997 | 314.128 | 44.64 | 0.6 | 901 | 71 | 898 | -7 | 8317 | 0.22$\pm$0.07 | -0.09$\pm$0.01 | 6
NGC 7142 | 326.29 | 65.782 | 3.1 | 2406 | -628 | 2288 | 396 | 9255 | 0.00$\pm$0.04 | -0.06$\pm$0.07 | 4
NGC 7245 | 333.812 | 54.336 | 0.6 | 3210 | -632 | 3145 | -104 | 9507 | -0.01$\pm$0.03 | -0.09$\pm$0.03 | 5
NGC 7762 | 357.472 | 68.035 | 2.0 | 897 | -408 | 794 | 91 | 8784 | 0.05$\pm$0.07 | -0.07$\pm$0.02 | 5
NGC 7789 | -0.666 | 56.726 | 1.5 | 2100 | -901 | 1887 | -196 | 9432 | 0.00$\pm$0.07 | -0.07$\pm$0.02 | 4
Ruprecht 171 | 278.012 | -16.062 | 2.8 | 1522 | 1458 | 430 | -82 | 6895 | 0.14$\pm$0.04 | -0.05$\pm$0.03 | 6
Skiff J1942+38.6 | 295.611 | 38.645 | 1.5 | 2378 | 700 | 2251 | 312 | 7964 | 0.10$\pm$0.06 | -0.05$\pm$0.06 | 6
UBC 3 | 283.799 | 12.326 | 0.1 | 1704 | 1214 | 1187 | 141 | 7223 | -0.01$\pm$0.03 | 0.04$\pm$0.02 | 4
UBC 6 | 344.01 | 51.187 | 0.7 | 1493 | -387 | 1428 | -199 | 8843 | 0.02$\pm$0.02 | -0.08$\pm$0.03 | 6
UBC 59 | 82.239 | 48.043 | 0.5 | 2585 | -2439 | 789 | 334 | 10808 | 0.03$\pm$0.02 | -0.02$\pm$0.06 | 3
UBC 215 | 100.461 | -5.243 | 0.4 | 1419 | -1137 | -842 | -111 | 9514 | 0.08$\pm$0.05 | -0.10$\pm$0.02 | 5

OCCASO project is dedicated to accurately measuring radial velocities and
detailed elemental abundances in OCs, as tracers of the Milky Way disc (see
Paper I for details). To reach this goal, it is essential to obtain a sample
as large and homogeneous as possible. To this end, OCCASO targets stars at the
same evolutionary stage, the RC, to avoid star-to-star abundances variation
caused by stellar evolution. RC stars can be easily identified even in
sparsely populated colour-magnitude diagrams. They are also brighter than
main-sequence stars, allowing us to observe them at further heliocentric
distances. Since they are warmer than brighter giants, their spectra are less
line crowded, which enables a more accurate determination of atmospheric
parameters (effective temperature, surface gravity, and metallicity) and
chemical abundances. This requirement constrains our sample to OCs older than
100 Ma, since stars in younger systems have not had enough time to evolve to
the RC stage.

In order to obtain representative values from the cluster’s average abundances
and radial velocities, we target at least four stars per cluster, although in
some cases this number can reach up to 12 stars. We adopt this strategy
because there is a non-negligible probability of contamination from non-
members, even when using membership determined from Gaia’s proper motions and
parallaxes. Moreover, some targets can be spectroscopic binaries, which
complicates their analysis even being real cluster members.

Figure 1: OCs observed in OCCASO (blue circles) together with OCs complying
with similar requirements (at least four observed RC stars and spectral
resolution above 20 000) from GES DR5 (orange diamonds), APOGEE DR17 (green
squares) and GALAH DR3 (red pentagons). All combined conform the OCCASO+
sample (see Sect. 4).

OCCASO employs three high resolution spectroscopic facilities housed at
Spanish observatories: the FIber-fed Echelle Spectrograph (FIES) at the Nordic
Optical Telescope (NOT) (NOT, Telting et al. 2014, $R$$\sim$67 000,
370¡$\lambda$¡900 nm); the High Efficiency and Resolution Mercator Echelle
Spectrograph (HERMES) at the Mercator telescope (Raskin et al. 2011,
$R$$\sim$82 000, 377¡$\lambda$¡900 nm). Both FIES and HERMES are located at
the Roque de los Muchachos Observatory in La Palma, Spain. The third
instrument is the Calar Alto Fiber-fed Echelle spectrograph (CAFE) installed
on the CAHA2.2m Telescope (Aceituno et al. 2013, $R$$\sim$62 000,
400¡$\lambda$¡900 nm), situated at the Centro Astronómico Hispano-Alemán
(CAHA) in Almería, Spain.

The data reduction of the observed spectra is fully described in Paper IV.
Briefly, bias subtraction, flat-field correction, order tracing, extraction,
and wavelength calibration were performed by the dedicated pipelines of each
instrument: HERMES DRS for HERMES (Raskin et al. 2011); FIEStool for FIES
(Telting et al. 2014); and CAFExtractor for CAFE (Lillo-Box et al. 2020). Our
own OCCASO pipeline performs the sky and telluric line subtraction,
combination of different exposures, normalisation, and merging of the echelle
orders. Additionally, we calculate the radial velocity of the stars by cross-
correlation with a template, obtaining measurements with a precision of 10 m
s-1. The improvements in the spectra combination procedure, described in Paper
IV, have considerably reduced the noise of the final spectra, allowing the
detection of weaker lines than in our previous papers.

We use the same sample as described in Paper IV, work in which we discard
stars considered as non-members. Additionally, we rejected objects whose
spectrum had a signal-to-noise ratio lower than 50 pix-1 to ensure a good
quality abundance measurement. After this, our sample is formed by 194 stars
belonging to 36 OCs, which main features are summarised in Table 1. Figure 1
shows the projection of the studied OCs onto the Galactic plane.

###  2.2 Methodology

We followed a similar analysis strategy as in Papers II and III. Two different
methods are used to compute the atmospheric parameters and chemical
abundances: equivalent widths (EW) and spectral synthesis (SS). For the sake
of homogeneity, in both cases, we use the MARCS atmosphere models (Gustafsson
et al. 2008) and the sixth version of the GES line list (Heiter et al. 2021).
We refer the reader to Paper II for details.

For spectral synthesis, we used iSpec (Blanco-Cuaresma et al. 2014a; Blanco-
Cuaresma 2019), and performed the analysis by comparison between the observed
spectrum and synthetic one. The spectra are computed on the fly with the
SPECTRUM radiative transfer code (Gray & Corbally 1994). We vary the
parameters to be measured until the chi-square value of the fit is minimized.
It provides effective temperatures, $T_{\mathrm{eff}}$, surface gravities,
$\log g$, microturbulence velocities, $v_{\mathrm{mic}}$, metallicities,
$\mathrm{[M/H]}$, and rotational velocities, $v\mathrm{sin}i$. We consider
that the line broadening is mainly due to rotation, by assuming a negligible
macroturbulence velocity, as both broadening mechanisms are difficult to
discern in red giants. (e.g., Thygesen et al. 2012). The atmospheric
parameters derived with SS are presented in Table 5.

The equivalent widths are measured by DAOSPEC, a code that detects and fits
absorption lines (Stetson & Pancino 2008). We take advantage of the DOOp
wrapper (Cantat-Gaudin et al. 2014), which allows to batch-process the
analysis and to determine the best DAOSPEC input parameters. The resulting EWs
are fed to GALA (Mucciarelli et al. 2013), which derives the atmospheric
parameters using the WIDTH9 radiative transfer code (Kurucz 2005), using the
classical spectroscopic method based on Fe lines. We reject too weak and too
strong lines outside the range -5.9 $\geq\log\frac{EW}{\lambda}\geq-4.8$. The
atmospheric parameters derived with EW are $T_{\mathrm{eff}}$, $\log g$,
$v_{\mathrm{mic}}$, and $\mathrm{[M/H]}$ (Table 5). Unlike the procedure
carried out in Papers II and III, we do not perform a normalisation with
DAOSPEC, since it is already done during the reduction process. Once the
atmospheric parameters have been determined by both methods we compare them
and determine the chemical abundances as detailed in Sect. 3.

####  2.2.1 Line list selection

Figure 2: Difference between the abundance calculated using a given line and
the Ti i mean abundance represented versus the wavelength of the line. The
blue circles are the reference lines, while orange triangles are the others.
The distribution of the reference lines is used as selection criteria, keeping
those lines that are in the region delimited by $\pm 1\sigma$ (between dotted
lines). Ti is used as an illustration; the procedure is the same for all
chemical elements.

The sixth version of the GES line list is a compilation of atomic parameters
of 80 612 spectral lines (Heiter et al. 2021) covering the whole wavelength
range of our spectra. The master line list provides two flags for each line:
_gf_flag_ , related to the reliability of the transition probabilities of the
lines; and _synflag_ indicating whether the line is blended or not at the UVES
resolution, $R$$\sim$47 000. Both flags can take the values: Y, for “yes, we
recommend to use this line”; N, for “not recommended”; or U, for “undecided”
(see Heiter et al. 2021, for details). However, we may be able to resolve some
lines blended in UVES spectra as OCCASO uses a higher resolution ($R\geq$67
000). Therefore, all lines are potentially usable regardless of their
_synflag_ values, as already suggested by Heiter et al. (2021).

In the case of SS, which is less sensitive to line blends, we determine the
atmospheric parameters using lines that lead to consistent abundances in a
solar spectrum as described in Paper II. For the determination of atmospheric
parameters with EW and abundances in both methods, we select the most suitable
lines with the following procedure: First, we discard all lines with _gf_flag_
=N since they are considered of low accuracy. Second, we run both methods on
the whole sample, selecting lines which are measurable in at least 50 stars.
Third, we discard lines yielding different chemical abundances because of
blends or inaccurate atomic parameters. For each element, we calculate the
difference between the abundance of each line and the mean abundance. By doing
this for all stars, we obtain a distribution for each line, which we evaluate
by calculating their median and standard deviation. The lines that have a
dispersion higher than 0.25 dex are discarded. In Papers II and III we employ
a fixed value to discard lines with discrepant medians. On the contrary, in
this work we use reference lines in this process, considering as such those
marked as _gf_flag_ =Y and _synflag_ = Y or U. We evaluate the distribution of
their medians and select all the lines in the $\pm 1\sigma$ region (dashed
lines in Fig. 2). We repeat the process twice to improve the discarding of
lines with discrepant values. The EW and SS line lists are presented in Tables
6 and 7, respectively.

For the elements with less than 10 detected lines (Na, Mg, Al, Zn, Sr, Y, Ba,
Ce, Zr, and Nd), statistical criteria are not enough to assess the goodness of
a line. In this case, we select the best lines by visual inspection of our
highest signal-to-noise ratio spectra. Additionally, the Na lines at 589.00
and 589.59 nm, labelled as YY, are discarded because they are contaminated by
interstellar medium absorption lines.

####  2.2.2 Impact of the initial guess values in GALA

GALA requires an initial guess of the atmospheric parameters to start the
analysis. In Papers II and III, the same initial guess values were used to
analyse the entire sample, $T_{\mathrm{eff}}$=4700 K and $\log g$=2.50 dex. In
the present work, we investigate the impact of the initial guess values on the
final result. To do that, we initialise the analysis with 21 different
combinations of $T_{\mathrm{eff}}$ and $\log g$ in a grid covering
4100$\leq$$T_{\mathrm{eff}}$$\leq$5300 K with a step of 200 K, and
1.4$\leq$$\log g$$\leq$3.5 dex with a step of 0.3 dex. For each combination of
initial guess values, we compute the atmospheric parameters of all stars.

We use three independent criteria to evaluate the goodness of the results.
First, the merit function provided by GALA, which estimates the quality of the
global solution. It is calculated considering the values of the optimisation
parameters and the corresponding uncertainties (see Mucciarelli et al. 2013,
for details). Second, we evaluate the difference between the derived
atmospheric parameters and the initial guess values, selecting the results
that satisfy $|T_{\rm eff,guess}-T_{\rm eff}|<$100 K and $|\log g_{\rm
guess}-\log g|<$0.5 dex. This rules out solutions that converged to values
very different from the initial guesses. Finally, we take into account the
number of times that a result is obtained for the same star starting from the
different guess values. The most repeated result may be the most reliable.

We used the combination of the three criteria to choose the atmospheric
parameters for each star. For 151 stars, 78% of cases, the three criteria
yield the same atmospheric parameters. These are the most robust EW results,
flagged as 1 in column ’GALAF’ of Table 5. For 15% of the cases, two of the
criteria pointed to the same atmospheric parameters, while the other provides
a different solution. In these cases, we select the solution obtained from two
of the criteria. They are flagged as 2 if the results are derived from the
first and second criteria and 3, if it is obtained from the second and third
criteria. There are no cases with agreement from the first and third criteria.
Finally, in the remaining 13 stars, 7% of the sample, every criterion provides
a different solution. These cases are flagged as 4, and the adopted result is
the one with the best merit function.

####  2.2.3 Solar abundance scale

To determine the solar abundances, we proceed as in Papers II and III,
retrieving the HARPS, UVES and NARVAL solar spectra from the Gaia benchmark
stars spectral library (Blanco-Cuaresma et al. 2014b). We analyse these
spectra with the same methodology as the OCCASO ones and with the two methods,
SS and EW. The solar abundances are calculated using the weighted mean of the
values derived for each spectrum, with the standard deviation as uncertainty.
The abundance values are calculated with the method chosen in Sect. 3.2. The
derived values are listed in Table 2. They are consistent, within twice the
standard deviation, with the photospheric values obtained by Grevesse et al.
(2007) and Asplund et al. (2009). The only exceptions are Na, and Ba with
differences of 0.2 and 0.4 dex respectively, being both below 3$\sigma$.

Table 2: Solar abundances calculated in this work, compared with Grevesse et al. (2007, GAS07) and Asplund et al. (2009, AGS09). Element | This work | Method | GAS07 | AGS09
---|---|---|---|---
| [dex] |  | [dex] | [dex]
Fe i | 7.48$\pm$0.01 | EW | 7.45 $\pm$0.05 | 7.50 $\pm$0.04
Mg i | 7.50$\pm$0.01 | SS | 7.53$\pm$0.09 | 7.60 $\pm$0.04
Si i | 7.43$\pm$0.01 | EW | 7.51$\pm$0.04 | 7.51 $\pm$0.03
Ca i | 6.36$\pm$0.01 | SS | 6.31$\pm$0.04 | 6.34 $\pm$0.04
Ti i | 4.91$\pm$0.02 | EW | 4.90$\pm$0.06 | 4.95 $\pm$0.05
Na i | 6.49$\pm$0.03 | SS | 6.17$\pm$0.04 | 6.24 $\pm$0.04
Al i | 6.55$\pm$0.02 | SS | 6.37$\pm$0.06 | 6.45 $\pm$0.03
Sc ii | 3.18$\pm$0.02 | SS | 3.17$\pm$0.10 | 3.15 $\pm$0.04
V i | 3.90$\pm$0.01 | SS | 4.00$\pm$0.02 | 3.93 $\pm$0.08
Cr i | 5.59$\pm$0.02 | EW | 5.64$\pm$0.10 | 5.64 $\pm$0.04
Mn i | 5.48$\pm$0.02 | SS | 5.39$\pm$0.03 | 5.43 $\pm$0.05
Co i | 4.90$\pm$0.02 | SS | 4.92$\pm$0.08 | 4.99 $\pm$0.07
Ni i | 6.27$\pm$0.02 | EW | 6.23$\pm$0.04 | 6.22 $\pm$0.04
Cu i | 4.03$\pm$0.03 | SS | 4.21$\pm$0.04 | 4.19 $\pm$0.04
Zn i | 4.35$\pm$0.02 | EW | 4.60$\pm$0.03 | 4.56 $\pm$0.05
Sr ii | 3.03$\pm$0.07 | SS | 2.92$\pm$0.05 | 2.87 $\pm$0.07
Y ii | 2.08$\pm$0.04 | SS | 2.21$\pm$0.02 | 2.21 $\pm$0.05
Zr ii | 2.58$\pm$0.04 | SS | 2.58$\pm$0.02 | 2.58 $\pm$0.04
Ba ii | 2.19$\pm$0.02 | SS | 2.17$\pm$0.07 | 2.18 $\pm$0.09
Ce ii | 1.45$\pm$0.05 | SS | 1.70$\pm$0.10 | 1.58 $\pm$0.04
Nd ii | 1.45$\pm$0.02 | SS | 1.45$\pm$0.05 | 1.42 $\pm$0.04
Figure 3: Differences, in the sense SS-EW, of the $T_{\mathrm{eff}}$ (top) and
$\log g$ (bottom) as a function of the SS values. Mean difference (solid
line), standard deviation (dashed lines), and 3$\sigma$ levels (dotted lines),
are shown in the plot. Typical error bars, calculated as the square of the
quadratic sum of each method uncertainties, are shown in the bottom-right
corner.

Figure 4: Distribution of the differences in $T_{\mathrm{eff}}$ (top), $\log
g$ (bottom) between OCCASO and APOGEE DR17 (green), GALAH DR3 (purple), GES
DR5 (orange), Gaia DR3 (grey) and Jacobson et al. (2011, JFP, blue). The
histogram is smoothed. The mean of the differences and the standard deviation
are shown in the panels.

##  3 Results of the spectroscopic analysis

###  3.1 Atmospheric Parameters

Table 3: Differences of the atmospheric parameters and abundances for the
stars in common with the literature, in the sense ours-others. We only
consider those works with more than 10 stars in common. $N$ is the number of
stars in common with OCCASO per study and parameter.

Parameter | APOGEE DR17 | $N$ | GALAH DR3 | $N$ | GES DR5 | $N$ |  Gaia DR3 | $N$ | Jacobson et al. (2011) | $N$
---|---|---|---|---|---|---|---|---|---|---
$T$eff [K] | 26.0$\pm$52 | 49 | 93.0$\pm$51 | 14 | 133.0$\pm$89 | 35 | 127$\pm$107 | 142 | 79$\pm$56 | 24
$\log g$[dex] | 0.0$\pm$0.1 | 49 | 0.2$\pm$0.1 | 14 | 0.0$\pm$0.1 | 35 | 0.3$\pm$0.2 | 142 | 0.0$\pm$0.1 | 24
$[$Fe/H$]$ [dex] | 0.0$\pm$0.1 | 49 | 0.0$\pm$0.1 | 14 | 0.0$\pm$0.1 | 35 | -0.1$\pm$0.1 | 142 | 0.0$\pm$0.1 | 24
$[$Mg/Fe$]$ [dex] | 0.0$\pm$0.1 | 47 | 0.0$\pm$0.1 | 14 | -0.1$\pm$0.1 | 33 |  |  | -0.2$\pm$0.1 | 24
$[$Si/Fe$]$ [dex] | 0.1$\pm$0.1 | 49 | -0.1$\pm$0.1 | 14 | 0.0$\pm$0.1 | 35 |  |  | -0.1$\pm$0.1 | 24
$[$Ca/Fe$]$ [dex] | 0.0$\pm$0.1 | 49 | -0.1$\pm$0.1 | 14 | 0.0$\pm$0.1 | 35 |  |  | 0.1$\pm$0.1 | 24
$[$Ti/Fe$]$ [dex] | 0.1$\pm$0.1 | 49 | 0.0$\pm$0.1 | 14 | 0.1$\pm$0.2 | 35 |  |  | 0.2$\pm$0.1 | 24
$[$Na/Fe$]$ [dex] | 0.1$\pm$0.1 | 47 | 0.0$\pm$0.1 | 14 | 0.1$\pm$0.1 | 33 |  |  | 0.0$\pm$0.1 | 24
$[$Al/Fe$]$ [dex] | 0.2$\pm$0.1 | 47 | 0.0$\pm$0.1 | 14 | 0.0$\pm$0.1 | 33 |  |  | -0.2$\pm$0.1 | 7
$[$V/Fe$]$ [dex] | 0.1$\pm$0.2 | 47 | -0.1$\pm$0.1 | 12 | 0.0$\pm$0.1 | 33 |  |  | – | 0
$[$Cr/Fe$]$ [dex] | 0.0$\pm$0.1 | 49 | 0.0$\pm$0.1 | 14 | 0.0$\pm$0.1 | 35 |  |  | 0.0$\pm$0.1 | 7
$[$Mn/Fe$]$ [dex] | -0.1$\pm$0.1 | 47 | -0.2$\pm$0.1 | 14 | 0.0$\pm$0.1 | 33 |  |  | – | 0
$[$Co/Fe$]$ [dex] | 0.0$\pm$0.1 | 47 | 0.1$\pm$0.1 | 14 | 0.1$\pm$0.2 | 33 |  |  | – | 0
$[$Ni/Fe$]$ [dex] | 0.0$\pm$0.1 | 49 | 0.0$\pm$0.2 | 13 | 0.0$\pm$0.1 | 35 |  |  | 0.1$\pm$0.1 | 24
$[$Cu/Fe$]$ [dex] | – | 0 | 0.0$\pm$0.1 | 14 | 0.0$\pm$0.2 | 32 |  |  | – | 0
$[$Zn/Fe$]$ [dex] | – | 0 | -0.1$\pm$0.2 | 13 | 0.0$\pm$0.1 | 33 |  |  | – | 0
$[$Y/Fe$]$ [dex] | – | 0 | -0.2$\pm$0.2 | 14 | 0.1$\pm$0.1 | 33 |  |  | – | 0
$[$Zr/Fe$]$ [dex] | – | 0 | -0.1$\pm$0.1 | 11 | -0.1$\pm$0.3 | 21 |  |  | -0.1$\pm$0.1 | 7
$[$Ba/Fe$]$ [dex] | – | 0 | -0.3$\pm$0.2 | 14 | 0.1$\pm$0.1 | 31 |  |  | – | 0
$[$Ce/Fe$]$ [dex] | 0.1$\pm$0.2 | 47 | – | 0 | 0.1$\pm$0.2 | 32 |  |  | – | 0
$[$Nd/Fe$]$ [dex] | – | 0 | -0.2$\pm$0.0 | 12 | -0.1$\pm$0.1 | 33 |  |  | – | 0

222Flags of surveys used to select stars for comparison: APOGEE DR17:
ASPCAPFLAG = 0 or 4; GES DR5: SFLAGS $\neq$ SNR,SRP,NIA; GALAH DR3: flag_sp =
0, flag_fe_h=0; Gaia DR3 & flags 1-7, 10 and 12 = 0, flag 8 and 9 $\leq$ 1.

The effective temperatures and surface gravities, derived by each method with
their uncertainties, are listed in Table 5 and compared in Fig. 3. The
$T_{\mathrm{eff}}$ obtained with SS are slightly higher by 15$\pm 80$ K than
those derived with EW. The typical uncertainties for EW and SS are 55 K and 17
K, respectively. For surface gravity, we find that EW yields values 0.08 dex
higher than SS. The standard deviation of the differences is 0.2 dex, which is
larger than the average uncertainties for EW, 0.09 dex, and SS, 0.04 dex,
respectively. This difference and the dispersion may be explained by the well
known difficulties in deriving $\log g$ from spectroscopy, even from high
quality and large wavelength coverage spectra as in our case. These results
are compatible with the values obtained in Paper II for a smaller sample.
Other studies using different methods find similar differences among methods
(e.g., Smiljanic et al. 2014).

Owing to the fact that $T_{\mathrm{eff}}$ and $\log g$ values derived from
both methods are compatible within the uncertainties, we obtain the weighted
average of them, also listed in Table 5, following the same strategy as in
Paper II. Because general metallicity and microturbulence velocity are
modelled differently in both methods, we do not attempt to combine them. The
average values of $T_{\mathrm{eff}}$ and $\log g$ are used to calculate again
the microturbulence and overall metallicity in both methods independently, and
additionally, the rotational velocity in SS. This procedure decreases the
differences between the two methods in the chemical abundance determination
(see Paper II). The $T_{\mathrm{eff}}$ and $\log g$ uncertainties of each
method are added in quadrature to calculate the uncertainties of the average
values.

Our sample includes several stars that have also been observed by other
spectroscopic surveys. We compared our sample with studies that share a
minimum of 10 stars, as shown in Fig. 4 and listed in Table 2: APOGEE DR17
(Abdurro’uf et al. 2022), GALAH DR3 (Buder et al. 2021), GES DR5 (Randich et
al. 2022), Gaia DR3 GSP-Spec (Gaia Collaboration et al. 2023a) and Jacobson et
al. (2011). In all cases, star selection was based on different flags provided
by each survey, which are described in Table 2.

There is an excellent agreement with APOGEE DR17 within the uncertainties. For
GALAH DR3, we obtain slightly larger values, although this comparison is based
on only 14 stars. Moreover, the distribution in $T_{\mathrm{eff}}$ shows a
tail towards negative differences. The difference with $T_{\mathrm{eff}}$ from
GES DR5 shows a double peaked distribution, which yields a large standard
deviation of 89 K. The highest peak shows that our $T_{\mathrm{eff}}$ are
higher than those provided by GES DR5. In contrast, their $\log g$ values show
a good agreement with ours. The comparison with Gaia DR3 shows wider
distributions, as expected from the larger uncertainties of the Gaia
measurements, and our values of $T_{\mathrm{eff}}$ and $\log g$ are larger.
The irregular distribution obtained for the differences in $\log g$ reflects
the greater difficulty in deriving gravities from spectroscopy in the small
wavelength range covered by Gaia. We performed the comparison after applying
the $\log g$ correction proposed by Recio-Blanco et al. (2023). In the case of
Jacobson et al. (2011), our $T_{\mathrm{eff}}$ are slightly higher than
theirs, but $\log g$ values show a good agreement. We have also compared our
results with other high-resolution studies ($R\sim$ 20 000) with less than 10
stars in common. There are no clear systematics with any of those studies.

###  3.2 Stellar chemical abundances

From SS, we determined abundances for 21 chemical elements: Fe, Mg, Si, Ca,
Ti, Na, Al, Sc, V, Cr, Mn, Co, Ni, Cu, Zn, Sr, Y, Zr, Ba, Ce, and Nd.
Additionally, for those elements with a significant number of unblended lines
and not significantly affected by hyperfine or isotopic structure, we also
derived abundances from EW for Fe, Cr, Ni, Ti, Si, Ca, and Zn.

The abundances are calculated as the weighted average of the values derived
for each individual line. The abundance uncertainties are computed as
$\frac{\sigma}{\sqrt{n}}$, being $\sigma$ the standard deviation and $n$ the
number of lines. We take into account the low statistic correction factor in
$\sigma$ by applying the equation 5 of Roesslein et al. (2007). When only one
line was measured (Zn, Sr, Zr, and Ce), we adopt the line abundance error as
the elemental uncertainty. The abundances computed by both methods are listed
in Table 5.

Several stars in our sample were observed with more than one instrumental
configuration. This is used to check the consistency of our results. For Fe
abundances, we find mean differences of 0.03$\pm$0.04 dex in EW, and
0.02$\pm$0.03 dex in SS, in good agreement with the quoted uncertainties. The
situation is similar for other elements. Therefore, in the case of stars
observed with more than one instrumental configuration, the final abundances
are calculated as the weighted mean of the several values obtained. We found
correlations with temperature for the elements Si, Na, Al, V, Ni, Sr, Y, Zr,
Ba, Ce and Nd having slopes, in absolute value, greater than 1.5x10-4 dex/K.
This is because in our stars temperature and age are correlated, therefore, as
the abundance of these elements depends on age (see sect. 4.2) they will also
have a temperature dependence.

Figure 5 shows the distributions of the [X/H] uncertainties of the 21 elements
measured in the stars of the sample. The elements from Fe to Ni have mean
uncertainties of about 0.05 dex, while from Cu on they have slightly higher
uncertainties $\sim$0.1 dex. This is explained by the small number of lines
measured for these elements. Sr has the most imprecise measurements, with mean
uncertainties of 0.15 dex.

Figure 5: Distribution of the stellar [X/H] uncertainties of the 21 chemical
elements derived with SS or EW methods. Figure 6: Differences of the
abundances computed by both EW and SS methods versus SS [Fe/H] values. The
mean differences and standard deviations are shown in each panel. In the
bottom-right corner of each panel, we plot the mean uncertainty.

In Fig. 6, we compare the derived abundances of the 7 elements studied with
the two methods. We find good agreement between methods except in the case of
Zn for which, there is a systematic difference of $0.22\pm 0.04$ dex. The
origin of the difference is unknown. It is not due to the radiative transfer
code used in SS since we obtain similar results when we repeat the analysis
using Turbospectrum (Plez 2012). The mean difference for Fe abundance is 0.01
dex, with a standard deviation of 0.04 dex. So, we no longer find the
difference in Fe abundance found in Paper II of 0.07$\pm$0.05 dex. This may be
explained by the improvements in the continuum placement implemented in Paper
IV, and the improved EW analysis discussed above. The stars that have the
largest differences between their abundances measured by the two methods are
those with the lowest S/N. When the abundance of an element is measured by
both methods, we evaluate the number of used lines, the abundance internal
error, and systematic differences with the literature to select which method
we rely on the most. We adopt the SS abundances except for Fe, Si, Ti, Cr, Ni
and Zn, for which we use the EW determinations.

Table 2 shows the differences with the literature for the studied elements.
Only the works with at least 10 stars in common with our sample have been
included. For [Fe/H], there is an excellent agreement with APOGEE DR17, GES
DR5 and GALAH DR3. Gaia DR3 shows a systematic difference of $-0.1\pm 0.1$
dex. We perform this comparison after applying the correction in [Fe/H]
proposed by Recio-Blanco et al. (2023). We do not find general systematic
differences with the literature for the rest of the elements. Furthermore, we
have compared our results and other high-resolution studies ($R\gtrsim$20 000)
that have ten or more stars in common with our sample. Our analysis reveals no
evident patterns or consistent discrepancies between these studies and ours.

###  3.3 Cluster chemical abundances

We adopt the membership analysis performed in Paper IV based on the proper
motions and radial velocities. There are 32 stars reported in the literature
as spectroscopic binary members (see Paper IV for details). We have checked
for the presence of double lines in their spectra using iSpec, but we do not
find signs of secondary stars in any of them. We consider their spectra valid
for abundance analysis.

The abundance of each cluster is the weighted average of those of its member
stars. The associated error is then the standard deviation of the abundances
multiplied by the low statistics correction factor (see Sect. 3.2). The full
cluster abundances are listed in the full version of Table 1, available at the
CDS. In Fig. 7 we show the distribution of standard deviations for each
element. For the elements from Fe to Cu, the mean standard deviation is around
0.05 dex, and it is higher for the elements from Zn to Nd. This is a direct
consequence of the higher uncertainties measuring those elements. NGC 6791
shows the largest standard deviations because it is the most distant cluster
in the sample and has been measured with the lowest S/N.

Figure 7: Distribution of the [X/H] standard deviations derived with the SS
(blue) and EW (orange) methods for the 36 OCs in our sample. Figure 8:
Comparison of the abundances of Fe and $\alpha$ elements of the OCs in our
sample with the literature, in the sense this work minus literature.

In Fig. 8 we compare our abundances of Fe and $\alpha$ elements with other
high-resolution studies ($R>$20 000). In Fig. 22 we show the comparisons for
the rest of the elements. There is a general good agreement with the
literature. The differences are at the same level than the ones found in the
star-by-star comparison in Table 2. The OC that presents the most considerable
difference with the literature is NGC 6791, specially in [Fe/H], although the
values of some studies are compatible with ours considering the errors (e.g.,
GES DR5). This was already noticed and reported in Paper III.

###  3.4 Non local thermodynamic equilibrium corrections

Some of the elements could be affected by non local thermodynamic equilibrium
(NLTE), so we have reviewed the effect of considering it in our sample. We
calculated the corrections for Fe, Mg, Si, Ca, Ti, Cr, Mn and Co using
Spectrum Tools333https://nlte.mpia.de/index.php that make use of the works of
Bergemann et al. (2012), Bergemann et al. (2015), Bergemann et al. (2013),
Mashonkina et al. (2007), Bergemann (2011), Bergemann & Cescutti (2010),
Bergemann & Gehren (2008) and Bergemann et al. (2010), respectively. We
corrected Ba from the results of (Korotin et al. 2015) and Na based on (Lind
et al. 2011).

When comparing the results considering local thermodynamic equilibrium (LTE)
with NLTE, we find that Fe, Mg (Fig. 9), Si, Ca, Ti, Mn and Ba do not show
significant differences when applying NLTE corrections. Cr and Co have
slightly higher values when applying NLTE, finding LTE-NLTE differences of
-0.06$\pm$0.02 dex and -0.03$\pm$0.02 dex, respectively. While Na has
systematically lower values when correcting for NLTE (Fig. 10) with
differences LTE-NLTE of 0.13$\pm$0.04 dex. The abundance values per OC
applying NLTE are published in the CDS version of Table 1.

Figure 9: LTE-NLTE comparison per OC for Mg. Figure 10: LTE-NLTE comparison
per OC for Na.

##  4 Galactic trends

We use the mean abundances of our OCs sample in combination with the positions
and ages from Cantat-Gaudin et al. (2020), and the orbital parameters derived
in Paper IV to investigate the abundance trends in the Galactic disc.

The OCCASO sample provides high-precision abundances derived homogeneously,
but it is limited in Galactocentric radius to $R_{\rm GC}<11.7$ kpc and
Galactic azimuth $\phi<$ -10° (Fig. 1). In order to enlarge the spatial
coverage, we created OCCASO+ adding OCs from high-resolution ($R$¿20 000)
surveys: GES DR5 (Magrini et al. 2023), APOGEE DR17 (Myers et al. 2022) and
GALAH DR3 (Spina et al. 2021). The OCCASO and GES samples complement each
other due to their similar spectral resolutions and wavelength coverage.
Additionally, each one observes a distinct hemisphere, resulting in different
Galactic azimuths being studied. APOGEE DR17 covers 0°$\leq$ $\phi$ $\leq$
30°and $R_{\rm GC}>11$ kpc not covered by OCCASO due to its limiting
magnitude.

In all cases, we select OCs that have a minimum of four stars studied in the
RC region. For those OCs observed by more than one survey, we prioritised the
results with the highest resolution in the following sequence: OCCASO, GES,
and APOGEE. Owing to the small number of systems sampled by GALAH, we only
select them if they have not been observed by any of the others. As the other
works we compare with mostly use LTE values, we use the OCCASO LTE values in
this section. We discard the V and Co abundances from Myers et al. (2022) due
to their large uncertainties in comparison with OCCASO. In total, OCCASO+
contains 99 OCs: 36 from OCCASO, 40 from GES DR5, 19 from APOGEE DR17, and
four from GALAH DR3 (Fig. 1). This means that this sample matches 64% with the
GES23 sample, 20% with the high-quality sample from Myers et al. (2022), and
17% with the sample of (Spina et al. 2021). We have analysed the stars of OCs
in common between the different surveys and OCCASO by calculating the
differences of [X/Fe] values. We do not find any dependence of the abundance
differences on atmospheric parameters or [Fe/H]. What we find are slight
[X/Fe] abundances zero points between studies (Fig. 22). We apply the
abundance offsets to the literature samples so that all abundances are on the
same scale. Special attention should be paid to the GALAH abundances, as
several of the elements are published with NLTE calculations (Buder et al.
2021). By checking the stars in common (Table 2), we find neither dependencies
with atmospheric parameters nor zero points higher than with other surveys.
The only exception is [Ba/Fe], for which we find differences around 0.4 dex.
NLTE cannot explain these differences (see section 3.4) so the difference
between GALAH and OCCASO results must be due to other sources. We have,
therefore, used the GALAH abundances in OCCASO+. The way in which OCCASO+ has
been selected makes it the most complete sample of OCs with precise
abundances. In the following subsections, we analyse different Galactic disc
chemical abundance trends with both OCCASO and OCCASO+ samples.

###  4.1 Abundance dependence on [Fe/H]

Figure 11: Abundance [X/Fe] ratios as function of [Fe/H] for OCCASO (circles)
and the OCCASO+ (triangles) samples, respectively, colour-coded with the age
of the OCs. The colour in the name of the element indicates the
nucleosynthetic group: $\alpha$ (orange), odd-Z (green), Fe-peak (blue) and
neutron-capture (pink).

We plot in Fig. 11 the abundance ratios [X/Fe] versus [Fe/H] for the OCCASO
(circles) and OCCASO+ (triangles) samples, colour-coded by age. To make the
interpretation easier, the different elements are sorted by their
nucleosynthetic group.

All $\alpha$ elements (Mg, Si, Ca, and Ti) show a slightly decreasing trend
with metallicity, which is more clear for the OCCASO+ sample since it covers a
larger [Fe/H] range. This trend, widely reported in the literature, is
explained by the production of $\alpha$ elements mainly in Core Collapse
Supernovae (CCS) from massive stars in short timescales in comparison with Fe,
which is produced on longer timescales mostly by type Ia Supernovae (SNe Ia).
The slopes of each $\alpha$ element can be different because of their
different production chains (e.g., Magrini et al. 2017).

The odd-z elements (Na and Al) seem to show a mild decreasing trend at
subsolar metallicities up to [Fe/H] $\sim-0.2$. Even though there are large
uncertainties involved, the Na abundance seems to increase at super-solar
metallicities, while Al trend remains mostly flat. These elements are produced
by massive and asymptotic giant branch (AGB) stars, but Na is also synthesised
in red giants, and mixing effects bring the Na to the surface. The process is
more important in massive giants, and thus Na can appear enhanced in young OCs
(see Sect. 4.2).

The Fe-peak elements (Sc, V, Cr, Mn, Co, Ni, Cu, Zn) are likely to be produced
by different nucleosynthesis processes. On the one hand, Sc, V, Cr, Mn, Co and
Ni are thought to be produced by the same processes as Fe (Kobayashi et al.
2020), with a trend generally flat. Nevertheless, Sc and Co show mild
decreasing trends at low metallicities, similarly to Al. On the other hand,
the nucleosynthesis of the elements Cu and Zn is under debate (Bisterzo et al.
2005; Romano & Matteucci 2007; Prantzos et al. 2018; Kobayashi et al. 2020).
The different processes suggested relate their formation to massive stars. Cu
exhibits a larger scatter, which may be attributed to higher uncertainties
(computed as the abundance standard deviation), making it difficult to extract
further conclusions. However, Zn appears to show a decreasing trend with Fe
abundance, that could be compatible with its formation in massive stars.

Neutron capture elements (Sr, Y, Zr, Ba, Ce, and Nd) show larger scatters than
the others, mostly due to their age dependence (see Sect. 4.2), and also
because of their larger uncertainty. These elements can be produced by slow
(s) or fast (r) processes of neutron capture. They are defined by whether the
capture timescale is longer or shorter than $\beta$ decay, and occur at
different astrophysical sites. The s-process occurs mainly in AGB stars (e.g.,
Gallino et al. 1998), while the origin of the r-process is still under debate
(e.g., Kajino et al. 2019). All neutron-capture elements studied in this work
are produced by both processes, with different relative contributions. They
all show similar general behaviour with a dependence on age, being older OCs
more depleted. Y, Zr, Ba, and Ce show a slight increasing trend with [Fe/H],
reaching their maximum at [Fe/H]$\sim$0 dex to decrease again more abruptly at
higher [Fe/H] abundances. This last decrease is explained since as [Fe/H]
increases, the ratio of neutrons to Fe in AGB star decreases. As a result,
there is a smaller proportion of s-process elements being produced (Gallino et
al. 2006; Cristallo et al. 2009; Karakas & Lattanzio 2014). Nd is the element
which has the highest percentage production by r-process, 38% according to
Prantzos et al. (2020). It shows a steeper and more continuous decreasing
trend with [Fe/H] compared to the other elements, which can be a consequence
of its higher production by r-process.

The obtained abundances patterns are generally compatible with those
previously reported in the literature for thin disc stars (e.g., Adibekyan et
al. 2012; Delgado Mena et al. 2017, 2019; Mikolaitis et al. 2019; Tautvaišienė
et al. 2021). However, the increase of [Mn/Fe] with [Fe/H] reported in the
literature (e.g., Adibekyan et al. 2012; Mikolaitis et al. 2019) is not
clearly seen in our case. We also find differences in the neutron capture
maximum abundance compared to what is stated in the literature. While Delgado
Mena et al. (2017) finds it at [Fe/H] $\sim 0$ dex for the five elements
similarly to us, Tautvaišienė et al. (2021) finds the maximum around -0.2 dex
for Ba and Ce. In general, for one-zone Galactic chemical evolution models,
the maximum is predicted at lower Fe abundances than seen in the observations
(e.g., Bisterzo et al. 2017; Prantzos et al. 2018; Kobayashi et al. 2020).

###  4.2 Abundance dependence on age

Figure 12: [X/Fe] ratios vs age, colour-coded as a function of [Fe/H]. The
symbols and panels are equivalent to Fig. 11. The best fits for OCCASO (green)
and OCCASO+ (black), respectively, are plotted.

We plot the dependence of the different elements with age in Fig. 12 where OCs
are colour-coded by their [Fe/H] abundance. For each element, we perform a
linear fit to quantify its age dependence. These fits were performed by the
same method as in Anders et al. (2017), using a maximum likelihood algorithm
as first guess, and computing a Markov-Chain Monte-Carlo with the python
package _emcee_ (Foreman-Mackey et al. 2013). The results of all fits are
listed in the Table 8. The best fits for both OCCASO and OCCASO+ samples are
overplotted in Fig. 12, with green and black lines, respectively.

From Mg to Co, we see very small dispersions, highlighting the small
variability of these abundance ratios in the age and metallicity range covered
by OCs of the thin disc. Nevertheless, due to the high-precision of our
abundances (which is maximised thanks to the number of stars per cluster) we
are able to see some mild trends.

In particular, we obtain positive slopes for the $\alpha$ elements Si and Mg,
which has been reported in the literature using high-precision samples of
field stars (e.g., Delgado Mena et al. 2019). Furthermore, we have noticed
enhanced levels of these elements in several young inner disc OCs aged between
0.1 to 0.7 Ga. Specifically, increased levels of Mg are observed in NGC 6067,
NGC 6259, and UBC 3 OCs, while higher Si levels are found in NGC 6067 and NGC
6705 OCs. In the literature, the cluster NGC 6705 was found to be $\alpha$
enhanced (Magrini et al. 2014; Casamiquela et al. 2018), and was considered as
a peculiar OC. To our knowledge, this is the first time that NGC 6705 is
reported to belong to a group of $\alpha$ enhanced clusters in the inner disc.

The two odd-Z elements Na and Al have different behaviours. Al shows an
overall increasing trend consistent with the results of Delgado Mena et al.
(2019). Na is mostly flat, though we remark a mild enhancement at young ages
until $\sim$1.8 Ga, and a plateau for older ages. The Na-enhancement can be
explained by the fact that the atmospheres of massive red giant stars (1.5-2
M⊙) are polluted after the first dredge-up because of deep mixing (Smiljanic
et al. 2016; Lagarde et al. 2012). As a consequence, OCs younger than
$\sim$1.2–2.5 Ga can appear enhanced in Na (see also Casamiquela et al. 2020).

Fe-peak elements show some variability in their trends as a function of age,
with part of them being positive in both fits, in particular V, Co, Ni, Cu and
Zn. Mikolaitis et al. (2019) have also found clear positive trends for Co and
Ni in agreement with us, but they find a negative trend for Cu and Mn, in
contrast with our results. On the other hand, Delgado Mena et al. (2019)
reports similar trends as ours in Cu and Zn.

Neutron capture elements are known to have high dependencies with age. We find
steep decreasing trends for all the elements comparable to what has been found
in the literature, in particular, Casamiquela et al. (2021) which used a
sample of 47 OCs that included most of the OCCASO sample. The trends are also
in agreement with those found by Viscasillas Vázquez et al. (2022) for GES
OCs, and those reported by Spina et al. (2016) and Delgado Mena et al. (2019)
for field stars. The enhancement of s-process elements in young stars is
tentatively explained in chemical evolution models by assuming enhanced AGB
yields of low-mass stars (e.g., D’Orazi et al. 2009; Cristallo et al. 2015).
These low-mass stars may take several Ga to deliver their chemical products
into the interstellar medium, leading to a delay in the enrichment of
s-process elements, and therefore, to a strong [X/Fe]-age dependence. What is
more, the age dependence does not have to be monotonic. In the trend of Ba and
possibly Zr there is a hint of a flattening at ages older than $\sim$2.5 Ga
(see Fig. 12). A similar flattening is reported for Ce by Sales-Silva et al.
(2022) from the APOGEE DR17 OC sample, but at 4 Ga. This kind of non-monotonic
trend could be caused by a change in the enrichment rate of the interstellar
medium by AGB stars. However, we also recall that the computation of Ba
abundances can significantly be affected by activity in young stars (Reddy &
Lambert 2017; Spina et al. 2020), which can alter the Ba trend with age.

Each element has a different trend according to the ratio at which it is
formed by s- and r- processes, as shown in Fig. 13. The slopes get steeper
with increasing contribution of s-process. This dependence has also been found
for solar twins (Spina et al. 2016) and field stars (Delgado Mena et al.
2019). It can be explained by the different production timescales of the two
processes. Unlike the s-process, r-process is expected to occur on short
timescales, shorter than those of Fe production. Therefore, the more an
element is produced per s-process, the steeper its [X/Fe]-age dependence would
be. Several authors calculate the s-process contribution (e.g., Bisterzo et
al. 2014; Prantzos et al. 2020), giving diferent values. The element for which
their results differ the most is Sr, being 68.9% at Bisterzo et al. (2014) and
91.2% at Prantzos et al. (2020). Our results agree better with the value of
Prantzos et al. (2020).

Figure 13: Slopes of the [X/Fe]–age relations of the neutron-capture elements
as a function of their s-process contribution percentages computed by Prantzos
et al. (2020). Figure 14: [Fe/H] versus Galactocentric radius for OCCASO+
sample. The different surveys are colour-coded as in Fig. 1. The grey vertical
lines represent the uncertainties of abundances, and the black line represents
our best fit.

###  4.3 Radial trends

In this section, we study the dependencies on the $R_{\rm GC}$ of [Fe/H] and
abundance ratio to Fe of the rest of the elements. The radial distribution of
[Fe/H] is one of the most widely used tracers in Galactic archaeological
studies. The general consensus is that there is a steeper decreasing gradient
in the inner disc and a plateau in the outer regions, the so-called knee
shape. In this section, we investigate the radial trends on [Fe/H] vs $R_{\rm
GC}$ obtained with the OCCASO and OCCASO+ sample. We use the fitting procedure
described in Sect. 4.2, but in the case of OCCASO+ we model the fit with two
lines, adjusting at the same time the knee position. The OCCASO sample
contains only OCs inside the knee. Table 4 contains the slopes obtained with
both samples compared to recent literature studies. The knee position derived
with OCCASO+ is 11.3 $\pm$ 0.8 kpc which confirms the position found in the
latest studies in te literature. The results obtained for both OCCASO and
OCCASO+ samples inside the knee are compatible with previous determinations in
the literature. Outside the knee, we recover a flatter trend for OCCASO+ in
comparison with GES23, and more similar to Spina et al. (2022), Myers et al.
(2022) and Carrera et al. (2019). We remark that this result is independent of
the inclusion of Berkeley 29, the furthest cluster in the sample.

Table 4: Comparison of [Fe/H] radial gradient with the literature in the region inside and outside the knee radius and globally, indicating in each case the number of OCs studied and the knee position.  Reference | Inside the knee radius | $N$ | Outside the knee radius | $N$ | Global | $N$ | Knee
---|---|---|---|---|---|---|---
| [dex kpc-1] |  | [dex kpc-1] |  | [dex kpc-1] |  | [kpc]
This work OCCASO | -0.059$\pm$0.017 | 36 | – | – | – | – | –
This work OCCASO+ | -0.069$\pm$0.008 | 71 | -0.025$\pm$0.011 | 28 | -0.062$\pm$0.007 | 99 | 11.3 $\pm$ 0.8
Paper III | -0.056$\pm$0.011 | 18 | – | – | – | – | –
Carrera et al. (2019) | -0.077$\pm$0.007 | – | -0.018$\pm$0.009 | – | -0.077$\pm$0.007 | 90 | 11
Donor et al. (2020) | -0.068$\pm$0.004 | 68 | -0.009$\pm$0.011 | 3 | – | 71 | 13.9
Zhang et al. (2021) | -0.066$\pm$0.005 | 157 | -0.032$\pm$0.007 | 4 | – | 161 | 14
Myers et al. (2022) | -0.073$\pm$0.002 | 51 | -0.032$\pm$0.002 | 34 | -0.055 $\pm$0.001 | 85 | 11.5
GES23 | -0.081$\pm$0.008 | 42 | -0.044$\pm$0.014 | 20 | -0.054$\pm$0.004 | 62 | 11.2
Spina et al. (2022) | -0.064$\pm$0.007 | – | -0.019$\pm$0.008 | – | – | – | 12.1 $\pm$ 1.1
Netopil et al. (2022) | -0.063$\pm$0.004 | 116 | – | – | -0.058$\pm$0.005 | 136 | 12
Gaia Collaboration et al. (2023a) | -0.054$\pm$0.008 | 503 | – | – | – | – | –
Figure 15: Abundance [X/Fe] ratios as function of $R_{\rm GC}$ for the OCCASO
(circles) and OCCASO+ (triangles) samples colour-coded with age of the OCs.
Fitted lines for both samples are shown in green and black, respectively.

In Fig. 15 we show the dependence of the different studied elements [X/Fe]
with $R_{\rm GC}$ for the OCCASO (circles) and OCCASO+ (triangles) samples and
their corresponding linear fits (green and black). The OCCASO+ sample has been
fitted with two lines as in the case of the [Fe/H] trends. We have limited the
sample to 16 kpc, since at larger distances we only have one cluster in the
sample, and therefore this region is not sampled correctly. The code used
allows us to determine if a knee shape is found or not. For more than half of
the elements, there is no knee shape, and we fit the whole sample with a
single line. We find a knee shape for the elements Ti, Na, Cr, Y, Zr, Ba, Ce,
and Nd. The results of the analysis are listed in Table 11.

The $\alpha$ elements show mild positive gradients, more evident for Ca Mg,
and Ti. This is in agreement with the models of the inside-out formation of
the Galactic disc. The differences among the elements, as already mentioned,
are due to their different detailed production processes.

The odd-z elements show different behaviours. Al has a flat relation whereas
Na shows a decrease up to 10 kpc showing a positive trend from there off. As
already discussed, the enrichment of the Na atmospheric abundances along the
red giant phase makes difficult to reach further conclusions.

Analogous to the discussion in Sect. 4.1, we expect Fe-peak elements produced
by the same processes as Fe to have flat radial gradients. We observe this for
V, Mn, Co, and Ni inside the knee. However, Cr shows a positive gradient
inside the knee, which seems to decrease again outside it. On the contrary, we
note that Sc shows a flat trend in the OCCASO sample and a positive trend in
the OCCASO+ sample. Additionally, elements mainly produced in massive stars
should exhibit positive radial trends, as some $\alpha$ elements do. It could
also be the case for Zn, showing a positive trend in the whole studied range.

In the case of the neutron-capture elements, we see again larger dispersions
at a given $R_{\rm GC}$ due to their age dependence and larger uncertainties.
They tend to show positive gradients, possibly due to the dependence of
s-process production with metallicity. However, there are significant
differences among them in agreement with the fact that each element is
produced in different proportions by r- and s-processes as discussed on Sect.
4.2. We find flatter trends for Sr, Y, Zr, and Ce, and steeper trends for Ba
and Nd. In general, the trends are flatter outside the knee for all the
elements. That is due to the dependence of s-process on [Fe/H] previously
discussed. In the case of Sr we do not know if it happens, as we have no
observations outside the knee.

In order to compare our results with the latest studies, we have analysed the
samples published by GES23, Myers et al. (2022), Spina et al. (2021) and
OCCASO+ restricting the analysis to the same $R_{\rm GC}$ range that in OCCASO
sample. The comparison is shown in Fig. 16. Overall, we find good agreement
between the different samples taking into account the uncertainties, although
there are a few exceptions. We highlight that we find large differences
between Spina et al. (2021) and our sample in Ca and Na, and between Myers et
al. (2022) and our sample in Na, Co, and Ce. The different slope of Co may be
due to the dispersion and high uncertainties of the APOGEE abundances in this
element. We also find remarkable differences of Ca, Al, Cu, V, and Zn
gradients for GES23 compared with the other samples. For some of these
elements (Ca, Ti, Al, and V) the difference in the gradient is due to the fact
that in the inner disc ($R_{\rm GC}$ $\leq$ 8 kpc) some OCs in GES23 tend to
have lower abundances compared to other surveys (see Fig. 21). Some of these
OCs in GES23 are in common with Myers et al. (2022) (Trumpler 20 and Ruprecht
134), which does not confirm their low abundances. Indeed, GES23 abundances of
those OCs are $\sim$0.07 dex lower than Myers et al. (2022) values. Given the
way in which the OCCASO+ sample is constructed, we believe that its results
are the most robust and reliable.

Figure 16: Comparison of [X/Fe] radial gradients with the literature.

###  4.4 The temporal evolution of Galactocentric radial gradients

Table 5: Change with age of the [Fe/H] radial gradients studied through MCMC at Sect. 4.3. The columns b1 and m1 are the y-intercept and slope of the first line fitted, respectively. The column m2 is the slope of the second line. The position of the knee, the number of OCs and Spearman correlation coefficient are listed in the last columns. We analyse the OCCASO and OCCASO+ samples and reanalyse the others. The OCCASO and Gaia DR3 samples do not have clusters beyond the knee, so only the first line is fitted. In the first bin of all the samples, we did not find the knee shape. Sample | Age bin | b1 | m1 | m2 | Knee | N | Spearman
---|---|---|---|---|---|---|---
| [Ga] | [dex] | [dex kpc-1] | [dex kpc-1] | [kpc] |  |
OCCASO | 0.1-1 | 0.365 $\pm$ 0.211 | -0.032 $\pm$ 0.022 |  |  | 15 | -0.30
OCCASO | 1-2 | 0.457 $\pm$ 0.084 | -0.079 $\pm$ 0.015 |  |  | 11 | -0.44
OCCASO | 2-3 | 0.347 $\pm$ 0.285 | -0.054 $\pm$ 0.037 |  |  | 4 | -0.47
OCCASO | 3-7.3 | 0.828 $\pm$ 0.553 | -0.099 $\pm$ 0.023 |  |  | 5 | -0.95
OCCASO+ | 0.1-1 | 0.452 $\pm$ 0.062 | -0.049 $\pm$ 0.006 |  |  | 39 | -0.75
OCCASO+ | 1-2 | 0.795 $\pm$ 0.114 | -0.09 $\pm$ 0.014 | -0.019 $\pm$ 0.018 | 10.1 $\pm$ 0.8 | 29 | -0.87
OCCASO+ | 2-3 | 0.651 $\pm$ 0.275 | -0.075 $\pm$ 0.033 | -0.042 $\pm$ 0.017 | 9.6 $\pm$ 2.1 | 14 | -0.85
OCCASO+ | 3-8 | 0.996 $\pm$ 0.305 | -0.113 $\pm$ 0.034 | -0.012 $\pm$ 0.018 | 10.9 $\pm$ 1.1 | 15 | -0.80
Myers et al. (2022) | 0.1-1 | 0.395 $\pm$ 0.087 | -0.051 $\pm$ 0.008 |  |  | 39 | -0.72
Myers et al. (2022) | 1-2 | 0.673 $\pm$ 0.135 | -0.08 $\pm$ 0.016 | -0.041 $\pm$ 0.01 | 10.1 $\pm$ 1.0 | 20 | -0.90
Myers et al. (2022) | 2-3 | 0.687 $\pm$ 0.177 | -0.085 $\pm$ 0.017 | -0.014 $\pm$ 0.026 | 11.9 $\pm$ 0.9 | 11 | -0.77
Myers et al. (2022) | 3-8 | 1.455 $\pm$ 0.329 | -0.161 $\pm$ 0.035 | -0.025 $\pm$ 0.017 | 10.8 $\pm$ 0.6 | 12 | -0.70
Spina et al. (2021) | 0.1-1 | 0.558 $\pm$ 0.062 | -0.065 $\pm$ 0.006 |  |  | 57 | -0.72
Spina et al. (2021) | 1-2 | 0.566 $\pm$ 0.187 | -0.068 $\pm$ 0.019 | -0.037 $\pm$ 0.047 | 11.5 $\pm$ 1.3 | 22 | -0.86
Spina et al. (2021) | 2-3 | 0.83 $\pm$ 0.234 | -0.097 $\pm$ 0.024 | -0.019 $\pm$ 0.028 | 11.9 $\pm$ 1.1 | 10 | -0.93
Spina et al. (2021) | 3-7.3 | 1.307 $\pm$ 0.295 | -0.147 $\pm$ 0.032 | -0.02 $\pm$ 0.018 | 11.1 $\pm$ 0.8 | 16 | -0.82
GES23 | 0.1-1 | 0.356 $\pm$ 0.057 | -0.043 $\pm$ 0.006 |  |  | 23 | -0.82
GES23 | 1-2 | 0.811 $\pm$ 0.152 | -0.09 $\pm$ 0.019 | -0.02 $\pm$ 0.026 | 10.8 $\pm$ 1.0 | 17 | -0.85
GES23 | 3-7.3 | 1.025 $\pm$ 0.34 | -0.118 $\pm$ 0.038 | -0.014 $\pm$ 0.017 | 10.7 $\pm$ 1.1 | 12 | -0.75
Gaia DR3 | 0.1-1 | 0.384 $\pm$ 0.069 | -0.043 $\pm$ 0.005 |  |  | 303 | -0.43
Gaia DR3 | 1-2 | 0.588 $\pm$ 0.196 | -0.065 $\pm$ 0.01 |  |  | 48 | -0.55
Gaia DR3 | 2-3 | 0.933 $\pm$ 0.392 | -0.079 $\pm$ 0.025 |  |  | 25 | -0.41
Gaia DR3 | 3-7.3 | 0.354 $\pm$ 0.26 | -0.105 $\pm$ 0.021 |  |  | 13 | -0.54

In order to investigate the change of the [Fe/H] radial gradient with time, we
consider OCs with $R_{\rm GC}$ ¡ 16 kpc for the reasons given above, and we
divide the samples used in Sect. 4.3 into four age bins: 0.1-1, 1-2, 2-3, ¿3
Ga, respectively. We maintain the same analysis methodology, providing the
slopes of the two fitted lines and the knee position for each age bin (listed
in Table 5). The OCCASO+ sample shows the absence of knee in the youngest bin,
and its presence in older clusters (see Fig. 20). By studying the slope inside
the knee, the OCCASO+ sample shows a steepening of the trend with age that
seems to be non-linear (Fig. 17), since the second age bin has a steeper
gradient than expected considering the other age bins. We have tested that
this non-linear dependence still appears when we change the way we partition
the clusters into age bins. The slope outside the knee remains constant,
taking into account errors. By re-analysing the other samples, we obtain
values compatible with OCCASO+, which is a confirmation of the dependence we
find. This is an effect that does not appear in field stars, as has been found
in the studies such as Casagrande et al. (2011); Anders et al. (2017, 2023).
They show a steepening of the gradient between 1 and 2 Ga to become
progressively flatter at older ages, contrary to OCs. The flattening of the
gradient is an expected effect of radial migration.

There are two hypotheses that attempt to explain the change in the gradient
with age in OCs: Anders et al. (2017) proposed that the change of trend is
produced by the radial migration process coupled with a selective bias that
the Galaxy exerts on OCs. Only outward migrating clusters survive because the
Galactic potential becomes less destructive as the distance from the centre
increases. Inward migrating clusters are quickly disrupted. Otherwise, GES23
show in their Fig. 5 that young clusters (¡1Ga) in the inner disc have lower
Fe abundances than older OCs, and propose that a considerable infall of gas
with low metallicity has produced the last episode of star formation. In the
OCCASO sample, we do not clearly see this age separation. In fact, there are
two young OCs in the inner disc that are metal-rich: NGC 2632 and NGC 6997 in
contradiction with this hypothesis (Fig. 20). This should be studied further,
with larger samples in the inner disc.

Another issue for which there is still no satisfactory explanation is the
presence of the plateau in the radial gradient. This feature is seen in OCs,
and recently in cepheids (da Silva et al. 2023). Similar hypotheses to the
ones above have been suggested to explain it: Magrini et al. (2009) suggested
that merger events or infall from the halo, affecting large radii and
providing pre-enriched material, could explain the plateau. Another possible
explanation is that the plateau is produced by radial migration of OCs towards
the outer disc. We do not know the event or mechanism that could have produced
it, though. The OCCASO+ sample shows no plateau in the youngest age bin
(¡1Ga), with OCs extended up to 14 kpc. We have ruled out that we have a
radial bias in the sample, as in the Cantat-Gaudin et al. (2020) catalogue
this is the maximum distance at which clusters appear in this age range. As
the knee formation event does not affect young clusters, it must be a process
that occurred more than 1 Ga ago. The presence of OCs older than 1 Ga at more
than 14 kpc, together with the appearance of the knee from this epoch,
supports this hypothesis. The recent cepheid study by da Silva et al. (2023)
shows signs of flattening in the outer disc. Given the youth of these stars,
the presence of cepheids forming a plateau cannot be explained by radial
migration processes. Further research is needed on both tracers in the outer
disc.

Figure 17: Evolution of the [Fe/H] radial gradient with age for the different
samples analysed. The gradients are those measured inside the knee. The age
position of each sample is slightly changed for clarity of the plot.

Figure 18 shows the change of $\delta$[X/Fe]/$\delta R_{\rm GC}$ as a function
of age of the OCCASO+ sample. Filled symbols represent the slopes coloured by
the age range, and blue lines represent the gradient for all the OCs
independently of age. In cases where we did not find a knee in the global
analysis, we have analysed each age bin using clusters up to 16 kpc.
Otherwise, in the cases where we found a knee, we evaluated the slope of the
first line of the fit (m1 in Table 5). We recall that when separating the
samples by age, the amount of OCs per bin is small, and therefore the
differences might be dominated by small number statistics. In most of the
cases, the values in each bin are around the global one, without a clear
correlation with age. Therefore, for almost all elements, if there is gradient
age dependence, it should be lower than our uncertainties. If we take as a
reference the bin with the highest error, we can establish that the upper
limit on the trend change is $\sim$ 0.01 dex kpc-1. This value is of the order
of 6 times lower than the gradient change we find for [Fe/H] indicating the
absence of change with age of $\delta$[X/Fe]/$\delta R_{\rm GC}$. Or, in other
words, $\delta$[X/H]/$\delta R_{\rm GC}$ changes with age essentially as
$\delta$[Fe/H]/$\delta R_{\rm GC}$. However, there are hints of trend change
for some elements. [Mg/Fe] seems to show an increase in the gradient with age,
while [Ti/Fe] and [Ni/Fe] appear to decrease. More OCs and a better
distribution among the different age bins are needed to address this topic in
more detail.

Figure 18: Change of OCCASO+ [X/Fe] radial gradient in four age bins, depicted
by the size and the colour of the markers. In each panel, age is growing
towards the right. Blue horizontal lines represent the radial gradient for the
whole age range, and the shadow area shows its dispersion. The table with the
trends is available under request.

###  4.5 Azimuthal gradient

Figure 19: Evolution with age of the [Fe/H] radial gradient (left) analysed at
the same time as the azimuthal gradient (right). The age position of each
sample is slightly changed for clarity of the plot.

One interesting feature of the metallicity distribution of the OCs population
as a function of Galactocentric radius is the large dispersion ($>$0.3 dex)
observed at any position (Figs. 14 and 15). This dispersion cannot be
explained by individual uncertainties and is usually attributed to the radial
migration. During their lifetimes, stars, and OCs, can move from their birth
Galactocentric radius due to the dynamical influence of non-axisymmetric
structures in the Galaxy such as spiral arms (e.g., Sellwood & Binney 2002),
the bar (e.g., Minchev & Famaey 2010) or minor satellites (e.g., Quillen et
al. 2009). An alternative explanation is that the observed dispersion could be
due to variations of the metal content with Galactic azimuth, as proposed by
Friel (2013). Some evidence of abundance variations with the azimuth has been
reported for Cepheids (Luck et al. 2006) and young OCs (Davies et al. 2009)
but not for H ii regions (Arellano-Córdova et al. 2020). However, the lack of
OC samples covering a wide azimuthal range has hampered the investigation of
this hypothesis.

We studied the same five samples analysed in the previous section in order to
check the existence of an azimuthal gradient. For this purpose, we fit
simultaneously the radial and azimuthal gradients with OCs separated in the
same age bins used in the previous section. To do that, we fit a plane using
the scikit-spatial444https://scikit-
spatial.readthedocs.io/en/stable/index.html python package. The uncertainties
are constrained by generating 1000 possible values for each cluster assuming a
Gaussian distribution centred on the mean abundance, with a $\sigma$ equal to
its uncertainty. The resulting fits are listed in Table 12 and shown in Fig.
19 in the two projections ($R_{\rm GC}$, and azimuth $\Phi$). We do not
represent bins containing less than 10 OCs as we consider this to be
insufficient statistics to conduct the study. Compared to Fig. 17 the
evolution of the $R_{\rm GC}$ gradient (left panel) shows larger differences
among samples, particularly in the oldest age bin, probably due to the
additional dimension of the fit. In the case of OCCASO+ the obtained slopes
are compatible with the results of Sect. 4.4. In the right panel of Fig. 19 we
show the evolution of the azimuthal gradient. The two youngest bins, in the
OCCASO+ sample, do not show a dependence with azimuth, but there are hints of
a positive trend in the oldest ones. This tendency also appears at GES23,
Myers et al. (2022) and Spina et al. (2021) samples. (Gaia Collaboration et
al. 2023a), shows a flat gradient in the third age bin and a positive gradient
for the oldest OCs group. To extract clearer conclusions about the existence
of an azimuthal variation, larger samples are needed.

###  4.6 Dependence with $|Z_{max}|$

Several studies have investigated the vertical distribution of abundance with
respect to the distance from the Galactic plane ($Z$). A vertical gradient has
been reported for field stars (e.g., Boeche et al. 2013; Gaia Collaboration et
al. 2023a; Hawkins 2023) but not in OCs (e.g., Jacobson et al. 2011; Carrera &
Pancino 2011; Carrera et al. 2019). Clusters move along their orbits; hence,
here we consider the maximum height of a cluster (—$Z_{\rm max}$—) as a better
tracer to perform this study. This orbital parameter has been computed in
Paper IV. $|Z_{max}|$ is known to be correlated with OC age (Tarricq et al.
2021). This is usually attributed to the vertical heating of the disc, since
OCs born in the thin disc, are scattered away from the mid-plane by non-
axisymmetric components. This is coupled with the fact that the Galactic
potential tends to disrupt OCs, having more chances to survive those that pass
more time far away from the disc mid-plane.

We do not observe a vertical gradient with [Fe/H], in agreement with previous
works. This could be because OCs cover a smaller range of vertical distances
(—$Z_{\rm max}$— $\leq$ 1.4 kpc in our sample) compared to field stars.
However, we did find positive [X/Fe]-—$Z_{\rm max}$— gradients for Mg, Ti, Al,
and Ni, and negative gradients for Na, Sr, Y, Zr, Ba, and Nd. These trends are
clearly associated with the dependence of [X/Fe] on age, as discussed in Sect.
4.2. When we remove the age dependence, the variations with vertical distance
disappear.

##  5 Summary and Conclusions

In this work, we obtain high-resolution spectra for 194 stars, members of 36
open clusters. We used both equivalent widths and spectral synthesis methods
to determine atmospheric parameters and LTE chemical abundances for 21
elements belonging to the main nucleosynthesis groups ($\alpha$, odd-Z, Fe-
peak, and neutron-capture elements). We also provide NLTE abundances for
elements when corrections are available. Additionally, we construct the
OCCASO+ sample by adding the abundances of other 63 clusters studied in
similar conditions to ours: high-resolution ($R>$20 000) and at least four
stars sampled in the red-clump region. Both samples are used to investigate
abundance trends with $R_{\rm GC}$, and their temporal evolution, azimuth and
distance to the plane, from the orbital parameter —Zmax—, and the abundance
dependence with age. The main results of our work are:

  * •

Some of the studied elements show dependencies with age. [Mg/Fe] and [Si/Fe]
show a positive trend towards older ages. Some clusters younger than 0.7 Ga
show an unexpected enhancement in those elements. [Na/Fe] shows a decreasing
trend until $\sim$ 1.8 Ga and a plateau for older ages, while [Al/Fe] shows a
mild positive trend. [Sc/Fe], [Ni/Fe], and [Cu/Fe] show significant positive
trends, while [V/Fe], [Co/Fe] and [Zn/Fe] show negative trends. The neutron
capture elements show negative gradients, those of [Zr/Fe] and [Ba/Fe] having
a flattening at ages ¿ 2.5 Ga.

  * •

We find that the [X/Fe]-age trend of neutron-capture elements depends on their
s-process contribution.

  * •

There is a decreasing [Fe/H] radial gradient, and a flattening of the trend
beyond 11.3 $\pm$0.8 kpc, the so-called knee shape. Inside the knee radius,
the OCCASO and OCCASO+ samples show gradients of -0.06$\pm$0.02 dex kpc-1 and
-0.07$\pm$0.01 dex kpc-1, respectively. Outside the knee, the OCCASO+ sample
shows a gradient of -0.03$\pm$0.01 dex kpc-1.

  * •

The radial gradients of the other elements show different tendencies depending
on the group to which they belong. $\alpha$ elements have a positive gradient,
except [Si/Fe]. [Na/Fe] has a negative gradient, while [Al/Fe] show a flat
one. Fe-peak elements have flat gradients, except for [Cr/Fe] and [Zn/Fe] that
show positive trends. Neutron capture elements show different behaviours,
having [Ba/Fe] and [Nd/Fe] the steepest positive trends.

  * •

The [Fe/H] radial gradient shows a steepening with age that seems to be non-
linear, since the second age bin (1¿age¿2 Ga) has a steeper gradient than
expected considering the other age bins. We also find that the younger
clusters (¡1Ga) do not have the knee shape seen in the older ones. This
suggests that the event that produced the knee occurred more than 1Ga ago.
This, together with the absence of young clusters at more than 14kpc, supports
the hypothesis that the knee shape was formed by outward radial migration.

  * •

When examining the temporal evolution of [X/Fe] radial gradient, we generally
observe that the trend within each age bin follows the overall trend
calculated using all the open clusters. Hence, we do not observe a clear
correlation between these trends and the age bin. If there was any evolution
of the gradient over time, it would be smaller than our uncertainties. As a
result, we can determine an upper limit of $\sim$ 0.01 dex kpc-1 for the
extent of their change with age. This is, in general, a negligible value of
change with age for [X/Fe] gradient. Nevertheless, there are hints that
certain elements exhibit an evolution of their radial gradients. Mg appears to
show an increase in the gradient with age, while Ti and Ni seem to display a
decrease.

  * •

One feature of the [Fe/H] radial dependence is the large dispersion ($>$0.3
dex) observed at any position. That is widely attributed to the radial
migration, but the observed dispersion could be due to variations of the metal
content with Galactic azimuth. We study the existence of the azimuthal
gradient by splitting the sample into age bins. We do not find any dependence
on azimuth for clusters with ages between 0.1 and 2 Ga. However, we observe
hints of a positive trend for open clusters with ages between 2 and 7.3 Ga.

  * •

We find $\delta$[X/Fe]/$\delta$—Zmax— gradients that are mostly due to the
trends with age. They disappear once the age dependence is subtracted.

###### Acknowledgements.

This work was supported by the MINECO (Spanish Ministry of Economy, Industry
and Competitiveness) through grant ESP2016-80079-C2-1-R (MINECO/FEDER, UE) and
by the Spanish MICIN/AEI/10.13039/501100011033 and by ”ERDF A way of making
Europe” by the “European Union” through grants RTI2018-095076-B-C21 and
PID2021-122842OB-C21, and the Institute of Cosmos Sciences University of
Barcelona (ICCUB, Unidad de Excelencia ’María de Maeztu’) through grant
CEX2019-000918-M. LC acknowledges the grant RYC2021-033762-I funded by
MCIN/AEI/10.13039/501100011033 and by the European Union
NextGenerationEU/PRTR. GT, ES, and AD acknowledge funding from the Lithuanian
Science Council (LMTLT, grant No. P-MIP-23-24). Based on observations made
with the Nordic Optical Telescope, owned in collaboration by the University of
Turku and Aarhus University, and operated jointly by Aarhus University, the
University of Turku and the University of Oslo, representing Denmark, Finland
and Norway, the University of Iceland and Stockholm University at the
Observatorio del Roque de los Muchachos, La Palma, Spain, of the Instituto de
Astrofisica de Canarias. Based on observations made with the Mercator
Telescope, operated on the island of La Palma by the Flemish Community, at the
Spanish Observatorio del Roque de los Muchachos of the Instituto de
Astrofísica de Canarias. Based on observations obtained with the HERMES
spectrograph, which is supported by the Research Foundation - Flanders (FWO),
Belgium, the Research Council of KU Leuven, Belgium, the Fonds National de la
Recherche Scientifique (F.R.S.-FNRS), Belgium, the Royal Observatory of
Belgium, the Observatoire de Genève, Switzerland and the Thüringer
Landessternwarte Tautenburg, Germany. Based on observations collected at
Centro Astronómico Hispano en Andalucía (CAHA) at Calar Alto, operated jointly
by Instituto de Astrofísica de Andalucía (CSIC) and Junta de Andalucía. This
research has made use of NASA’s Astrophysics Data System Bibliographic
Services.

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##  Appendix A Complementary tables and figures

Table 6: First rows of the table with the line list for EW. The complete table is available at the CDS. Wavelength (nm) | Element
---|---
480.288 | Fe1
480.8148 | Fe1
480.9938 | Fe1
481.0528 | Zn1
481.1983 | Ni1
481.4590 | Ni1
482.3463 | Mn1
482.3483 | Mn1
482.3495 | Mn1
482.3508 | Mn1
Table 7: First rows of the table with the line list for SS. The complete table is available at the CDS. Wavelength (nm) | Element
---|---
480.1025 | Cr1
480.288 | Fe1
480.8148 | Fe1
481.0528 | Zn1
481.1983 | Ni1
481.4591 | Ni1
482.352 | Mn1
483.2426 | V1
486.9463 | Fe1
487.5493 | Ti1
Table 8: [X/Fe] abundance dependence with age of the samples OCCASO and OCCASO+ and its uncertainties computed in Sect. 4.2. $N$ and $\rho$ are the number of OCs and the Spearman correlation coefficient, respectively. | OCCASO | OCCASO+
---|---|---
Element | $\delta[X/Fe]/\delta Age$ | $N$ | $\rho$ | $\delta[X/Fe]/\delta Age$ | $N$ | $\rho$
| [dex Ga-1] |  |  | [dex Ga-1] |  |
Mg | 0.009$\pm$0.007 | 36 | 0.48 | 0.012$\pm$0.009 | 99 | 0.37
Si | 0.012$\pm$0.008 | 36 | 0.64 | 0.011$\pm$0.003 | 99 | 0.41
Ca | 0.008$\pm$0.005 | 36 | 0.15 | -0.001$\pm$0.001 | 99 | 0.05
Ti | 0.009$\pm$0.005 | 36 | 0.66 | 0.005$\pm$0.003 | 99 | 0.37
Na | -0.03$\pm$0.013 | 36 | -0.11 | -0.005$\pm$0.003 | 99 | -0.12
Al | 0.02$\pm$0.011 | 36 | 0.61 | 0.003$\pm$0.002 | 99 | 0.49
Sc | -0.005$\pm$0.004 | 36 | -0.43 | 0.017$\pm$0.005 | 76 | -0.21
V | 0.01$\pm$0.005 | 36 | 0.62 | 0.009$\pm$0.004 | 99 | 0.18
Cr | 0.018$\pm$0.004 | 36 | 0.63 | 0.002$\pm$0.002 | 99 | 0.21
Mn | 0.003$\pm$0.007 | 36 | 0.25 | 0.005$\pm$0.003 | 99 | 0.21
Co | 0.007$\pm$0.005 | 36 | 0.29 | 0.01$\pm$0.004 | 99 | 0.32
Ni | 0.026$\pm$0.006 | 36 | 0.77 | 0.027$\pm$0.005 | 99 | 0.54
Cu | 0.016$\pm$0.008 | 36 | 0.52 | 0.024$\pm$0.006 | 78 | 0.38
Zn | 0.02$\pm$0.012 | 32 | 0.12 | 0.01$\pm$0.004 | 76 | 0.21
Sr | -0.07$\pm$0.008 | 35 | -0.77 | – | – | –
Y | -0.022$\pm$0.012 | 36 | -0.80 | -0.011$\pm$0.008 | 80 | -0.35
Zr | -0.027$\pm$0.013 | 35 | -0.7 | -0.023$\pm$0.007 | 78 | -0.38
Ba | -0.066$\pm$0.007 | 36 | -0.76 | -0.058$\pm$0.011 | 80 | -0.48
Ce | -0.044$\pm$0.015 | 36 | -0.69 | -0.022$\pm$0.008 | 94 | -0.47
Nd | -0.032$\pm$0.011 | 36 | -0.69 | -0.016$\pm$0.009 | 80 | -0.11
Table 9: Stellar parameters and chemical abundances for the 194 stars in this
work. Here only a sample of rows and columns is shown. The complete table with
all the atmospheric parameters and the 21 elements computed by EW and SS
methods is available at the CDS.

Cluster | source id Gaia DR3 |  $T_{\mathrm{eff}}$ (K) | $\log g$ | GALAF |  $[$Fe/H$]$EW |  $[$Mg/Fe$]$SS |  $[$Si/Fe$]$EW |  $[$Ca/Fe$]$SS |  $[$Ti/Fe$]$EW
---|---|---|---|---|---|---|---|---|---
NGC1907 | 183263711899696768 | 5215$\pm$55 | 2.77$\pm$0.10 | 1 | -0.05$\pm$0.01 | 0.02$\pm$0.03 | 0.11$\pm$0.03 | 0.06$\pm$0.04 | 0.10$\pm$0.03
NGC1907 | 183263097725025024 | 5030$\pm$55 | 2.60$\pm$0.09 | 1 | -0.03$\pm$0.01 | -0.05$\pm$0.03 | 0.08$\pm$0.03 | 0.04$\pm$0.03 | 0.03$\pm$0.03
NGC1907 | 183263127784145280 | 5201$\pm$43 | 2.67$\pm$0.12 | 1 | -0.06$\pm$0.01 | 0.02$\pm$0.03 | 0.072$\pm$0.03 | 0.04$\pm$0.03 | 0.03$\pm$0.03
NGC1907 | 183263127784146176 | 5273$\pm$55 | 3.04$\pm$0.11 | 4 | -0.03$\pm$0.01 | 0.03$\pm$0.04 | 0.06$\pm$0.04 | -0.01$\pm$0.04 | 0.11$\pm$0.03
NGC2099 | 3451181873619100160 | 5079$\pm$55 | 2.73$\pm$0.12 | 1 | 0.07$\pm$0.01 | -0.07$\pm$0.02 | 0.13$\pm$0.04 | 0.01$\pm$0.03 | -0.01$\pm$0.03
NGC2099 | 3451181766240577024 | 5047$\pm$40 | 2.60$\pm$0.08 | 1 | 0.04$\pm$0.01 | -0.07$\pm$0.02 | 0.08$\pm$0.02 | -0.01$\pm$0.03 | 0.00$\pm$0.03
NGC2099 | 3451180602308805120 | 5104$\pm$40 | 2.78$\pm$0.09 | 1 | 0.06$\pm$0.01 | -0.07$\pm$0.02 | 0.05$\pm$0.04 | 0.02$\pm$0.03 | 0.01$\pm$0.03
NGC2099 | 3451181216484770432 | 4975$\pm$41 | 2.54$\pm$0.10 | 1 | 0.03$\pm$0.01 | -0.07$\pm$0.03 | 0.06$\pm$0.03 | -0.01$\pm$0.03 | 0.00$\pm$0.03
NGC2099 | 3451181667460701440 | 5115$\pm$43 | 2.78$\pm$0.08 | 1 | 0.01$\pm$0.01 | -0.09$\pm$0.03 | 0.12$\pm$0.04 | 0.01$\pm$0.03 | 0.00$\pm$0.03
NGC2099 | 3451179949473813376 | 5052$\pm$46 | 2.61$\pm$0.06 | 4 | 0.06$\pm$0.01 | -0.05$\pm$0.03 | 0.08$\pm$0.03 | 0.01$\pm$0.03 | 0.03$\pm$0.03
NGC2099 | 3451201458669932032 | 5065$\pm$45 | 2.68$\pm$0.07 | 1 | 0.06$\pm$0.01 | -0.07$\pm$0.02 | 0.09$\pm$0.02 | -0.01$\pm$0.03 | 0.04$\pm$0.03

555GALAF is a quality flag assigned to GALAvalues derived in the study of how
the initial guesses of atmospheric parameters affect their derivation (see
Sect. 2.2.2).

Table 10: Comparison of radial gradients between OCCASO, OCCASO+ and the literature reanalysis of Sect. 4.3. [X/Fe] | Group | OCCASO | OCCASO+ | GES23 | Myers et al. (2022) | Spina et al. (2021)
---|---|---|---|---|---|---
|  |  $\delta$[X/Fe]/$\delta R_{\rm GC}$ |  $\delta$[X/Fe]/$\delta R_{\rm GC}$ |  $\delta$[X/Fe]/$\delta R_{\rm GC}$ |  $\delta$[X/Fe]/$\delta R_{\rm GC}$ |  $\delta$[X/Fe]/$\delta R_{\rm GC}$
|  | [dex kpc-1] | [dex kpc-1] | [dex kpc-1] | [dex kpc-1] | [dex kpc-1]
Mg | $\alpha$ | 0.017$\pm$0.011 | 0.006$\pm$0.005 | 0.008$\pm$0.006 | 0.001$\pm$0.001 | 0.001$\pm$0.002
Si | $\alpha$ | 0.008$\pm$0.006 | 0.000$\pm$0.003 | 0.000$\pm$0.001 | -0.0$\pm$0.001 | 0.0$\pm$0.001
Ca | $\alpha$ | 0.013$\pm$0.005 | 0.016$\pm$0.004 | 0.033$\pm$0.006 | 0.009$\pm$0.002 | -0.003$\pm$0.003
Ti | $\alpha$ | 0.016$\pm$0.007 | 0.025$\pm$0.006 | 0.024$\pm$0.006 | 0.009$\pm$0.006 | 0.011$\pm$0.008
Na | Odd-Z | -0.027$\pm$0.008 | -0.021$\pm$0.006 | -0.018$\pm$0.007 | -0.058$\pm$0.014 | -0.007$\pm$0.004
Al | Odd-Z | -0.013$\pm$0.007 | -0.001$\pm$0.005 | 0.017$\pm$0.007 | 0.005$\pm$0.004 | -0.002$\pm$0.001
Sc | Fe-peak | 0.001$\pm$0.007 | 0.01$\pm$0.003 | 0.007$\pm$0.005 |  |
V | Fe-peak | -0.01$\pm$0.006 | 0.005$\pm$0.003 | 0.013$\pm$0.005 | -0.0$\pm$0.001 |
Cr | Fe-peak | 0.017$\pm$0.008 | 0.02$\pm$0.006 | 0.020$\pm$0.005 | -0.001$\pm$0.005 |
Mn | Fe-peak | -0.007$\pm$0.010 | 0.002$\pm$0.003 | 0.006$\pm$0.005 | -0.017$\pm$0.004 | -0.008$\pm$0.002
Co | Fe-peak | -0.007$\pm$0.005 | -0.001$\pm$0.002 | 0.003$\pm$0.007 | -0.039$\pm$0.012 | -0.014$\pm$0.004
Ni | Fe-peak | 0.000$\pm$0.004 | -0.003$\pm$0.002 | -0.001$\pm$0.005 | -0.001$\pm$0.003 | -0.01$\pm$0.003
Cu | Fe-peak | -0.005$\pm$0.010 | 0.009$\pm$0.008 | 0.041$\pm$0.009 |  |
Zn | Fe-peak | 0.026$\pm$0.012 | 0.015$\pm$0.004 | 0.001$\pm$0.004 |  |
Sr | n-capture | 0.007$\pm$0.012 |  |  |  |
Y | n-capture | -0.005$\pm$0.011 | 0.015$\pm$0.009 | 0.003$\pm$0.009 |  |
Zr | n-capture | 0.009$\pm$0.015 | 0.015$\pm$0.011 | 0.015$\pm$0.007 |  |
Ba | n-capture | 0.026$\pm$0.018 | 0.032$\pm$0.014 | 0.025$\pm$0.007 |  |
Ce | n-capture | 0.001$\pm$0.013 | 0.017$\pm$0.007 | 0.007$\pm$0.008 | 0.065$\pm$0.013 |
Nd | n-capture | 0.032$\pm$0.016 | 0.05$\pm$0.008 | 0.061$\pm$0.015 |  |
Table 11: [X/Fe] radial gradients studied through MCMC at Sect. 4.3 in the OCCASO+ sample. The columns b1 and m1 are the y-intercept and slope of the first line fitted, respectively. The column m2 is the slope of the second line. The position of the knee, the number of OCs and Spearman correlation coefficient are shown in the last columns. Element | b1 | m1 | m2 | Knee | N | Spearman
---|---|---|---|---|---|---
| [dex] | [dex kpc-1] | [dex kpc-1] | [kpc] |  |
Mg | -0.118$\pm$0.021 | 0.008$\pm$0.002 |  |  | 99 | 0.29
Si | 0.029$\pm$0.018 | 0.005$\pm$0.002 |  |  | 99 | 0.21
Ca | -0.07$\pm$0.022 | 0.008$\pm$0.002 |  |  | 99 | 0.42
Ti | -0.145$\pm$0.047 | 0.019$\pm$0.006 | -0.003$\pm$0.009 | 10.7$\pm$1.1 | 99 | 0.40
Na | 0.287$\pm$0.082 | -0.026$\pm$0.01 | 0.015$\pm$0.008 | 9.9$\pm$0.8 | 99 | -0.28
Al | -0.034$\pm$0.029 | 0.005$\pm$0.003 |  |  | 99 | 0.19
Sc | 0.001$\pm$0.019 | 0.008$\pm$0.002 |  |  | 76 | 0.38
V | 0.02$\pm$0.022 | -0.002$\pm$0.002 |  |  | 99 | 0.09
Cr | -0.246$\pm$0.058 | 0.029$\pm$0.007 | -0.008$\pm$0.005 | 9.5$\pm$0.5 | 99 | 0.33
Mn | -0.146$\pm$0.019 | 0.003$\pm$0.002 |  |  | 99 | 0.04
Co | 0.019$\pm$0.027 | 0.002$\pm$0.003 |  |  | 99 | 0.11
Ni | 0.008$\pm$0.018 | -0.003$\pm$0.002 |  |  | 99 | -0.11
Cu | -0.235$\pm$0.054 | 0.003$\pm$0.006 |  |  | 78 | 0.13
Zn | -0.152$\pm$0.021 | 0.01$\pm$0.002 |  |  | 76 | 0.54
Y | -0.129$\pm$0.037 | 0.011$\pm$0.004 |  |  | 80 | 0.38
Zr | -0.353$\pm$0.079 | 0.038$\pm$0.01 | -0.02$\pm$0.007 | 9.6$\pm$0.4 | 78 | 0.18
Ba | -0.321$\pm$0.111 | 0.036$\pm$0.014 | -0.016$\pm$0.018 | 10.3$\pm$1.2 | 80 | 0.25
Ce | -0.107$\pm$0.075 | 0.027$\pm$0.009 | 0.006$\pm$0.012 | 10.7$\pm$0.9 | 94 | 0.36
Nd | -0.382$\pm$0.074 | 0.049$\pm$0.009 | 0.016$\pm$0.016 | 10.7$\pm$1.3 | 80 | 0.71
Table 12: Change with age of the [Fe/H] radial and azimuthal gradients studied with a multilinear regression at Sect. 4.5. We analyse the OCCASO and OCCASO+ samples and reanalyse the others. Number of OCs and Spearman correlation coefficient (sixth and seventh columns). |  | Radial gradient | Radial and Azimuthal Gradients |  |
---|---|---|---|---|---
Sample | Age bin |  $\delta$[Fe/H]/$\delta R_{\rm GC}$ |  $\delta$[Fe/H]/$\delta\Phi$ | $N$ | Spearman |
| [Ga] | [dex kpc-1] | [dex deg-1] |  |  |
OCCASO | 0.1 - 1 | -0.022$\pm$0.007 | -0.0034$\pm$0.0016 | 15 | 0.33 |
OCCASO | 1 - 2 | -0.069$\pm$0.016 | 0.0048$\pm$0.0026 | 11 | 0.83 |
OCCASO | 2 - 3 | -0.026$\pm$0.038 | -0.0024$\pm$0.0049 | 4 | 0.34 |
OCCASO | 3 - 7.3 | -0.101$\pm$0.019 | 0.0003$\pm$0.0043 | 5 | 0.87 |
OCCASO+ | 0.1 - 1 | -0.051$\pm$0.004 | 0.0001$\pm$0.0007 | 39 | 0.79 |
OCCASO+ | 1 - 2 | -0.060$\pm$0.004 | -0.0003$\pm$0.0007 | 29 | 0.87 |
OCCASO+ | 2 - 3 | -0.041$\pm$0.004 | 0.0026$\pm$0.0008 | 14 | 0.92 |
OCCASO+ | 3 - 7.3 | -0.088$\pm$0.008 | 0.003$\pm$0.0019 | 15 | 0.83 |
Myers et al. (2022) | 0.1 - 1 | -0.053$\pm$0.002 | 0.0016$\pm$0.0002 | 39 | 0.74 |
Myers et al. (2022) | 1 - 2 | -0.056$\pm$0.002 | 0.0000$\pm$0.0004 | 20 | 0.91 |
Myers et al. (2022) | 2 - 3 | -0.026$\pm$0.005 | 0.0063$\pm$0.0007 | 11 | 0.92 |
Myers et al. (2022) | 3 - 7.3 | -0.046$\pm$0.002 | 0.0074$\pm$0.0006 | 12 | 0.84 |
Spina et al. (2021) | 0.1 - 1 | -0.064$\pm$0.002 | 0.0007$\pm$0.0002 | 57 | 0.80 |
Spina et al. (2021) | 1 - 2 | -0.062$\pm$0.005 | 0.0003$\pm$0.0007 | 22 | 0.77 |
Spina et al. (2021) | 2 - 3 | -0.041$\pm$0.003 | 0.0052$\pm$0.0005 | 10 | 0.97 |
Spina et al. (2021) | 3 - 7.3 | -0.060$\pm$0.003 | 0.0037$\pm$0.0009 | 16 | 0.78 |
GES23 | 0.1 - 1 | -0.044$\pm$0.006 | -0.0014$\pm$0.0011 | 23 | 0.76 |
GES23 | 1 - 2 | -0.058$\pm$0.005 | 0.0005$\pm$0.0011 | 17 | 0.88 |
GES23 | 2 - 3 | -0.048$\pm$0.015 | 0.0057$\pm$0.0026 | 7 | 0.86 |
GES23 | 3 - 7.3 | -0.038$\pm$0.004 | 0.0072$\pm$0.0012 | 12 | 0.77 |
Gaia DR3 | 0.1 - 1 | -0.050$\pm$0.003 | 0.0002$\pm$0.0004 | 303 | 0.43 |
Gaia DR3 | 1 - 2 | -0.056$\pm$0.008 | -0.0012$\pm$0.0008 | 48 | 0.52 |
Gaia DR3 | 2 - 3 | -0.079$\pm$0.012 | 0.0000$\pm$0.0014 | 25 | 0.57 |
Gaia DR3 | 3 - 7.3 | -0.067$\pm$0.019 | 0.0041$\pm$0.0017 | 13 | 0.71 |
Figure 20: [Fe/H] versus Galactocentric radius for OCCASO+ sample separated in
four age bins 0.1-1, 1-2, 2-3, ¿3 Ga. The different surveys are colour-coded
as in Fig. 1. The grey vertical lines represent the uncertainties, and the
black line is our best fit. Figure 21: Dependence of [X/Fe] on Galactocentric
radius for the clusters in the OCCASO+ sample. The original sources of the
abundances are indicated with different symbols and colours. Figure 22:
Comparison of the abundances for OCs in common with high-resolution ($R>$20
000) spectroscopic studies (this work-literature) for elements not shown in
Fig. 8.