11institutetext: Institut de Radioastronomie Millimétrique (IRAM), Av. Divina
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11email<EMAIL_ADDRESS>22institutetext: Asociación Astrofísica para la
Promoción de la Investigación, Instrumentación y su Desarrollo, ASPID, 38205
La Laguna, Tenerife, Spain 33institutetext: Centro de Astrobiología
(CSIC/INTA), 28692 ESAC Campus, Villanueva de la Cañada, Madrid, Spain
44institutetext: ISDEFE for European Space Astronomy Centre (ESAC)/ESA, P.O.
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Instituto de Astrofísica de Canarias (IAC), 38200 La Laguna, Tenerife, Spain
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Tierra y Astrofísica, Instituto de Física de Partículas y del Cosmos, IPARCOS.
Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain 88institutetext:
Instituto de Física de Cantabria (CSIC-Universidad de Cantabria), 39005,
Santander, Spain 99institutetext: Instituto de Astronomía, Universidad
Nacional Autónoma de México, Apdo. Postal 70-264, 04510 Ciudad de México,
México 1010institutetext: Fundación Galileo Galilei-INAF Rambla José Ana
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Centro de Estudios de ísica del Cosmos de Aragón (CEFCA), Plaza San Juan 1,
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(SSGI), Entoto Observatory and Research Center (EORC), Astronomy and
Astrophysics Research Division, PO Box 33679, Addis Abbaba, Ethiopia
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Granada, Spain 1414institutetext: Physics Department, Mbarara University of
Science and Technology (MUST), Mbarara, Uganda 1515institutetext: Addis Ababa
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Physics Department, Kotebe Metropolitan University (KMU) P.O.Box 31248, Addis
Ababa, Ethiopia 1717institutetext: Telespazio Vega UK for ESA, European Space
Astronomy Centre, Operations Departmen, E-28691, Villanueva de la Cañada,
Spain

# GLACE survey: OSIRIS/GTC tuneable imaging of the galaxy cluster ZwCl
0024.0+1652

II. The mass–metallicity relationship and the effect of the environment

Bernabé Cedrés 1122 Simon B. De Daniloff 11 Ángel Bongiovanni 1122 Miguel
Sánchez-Portal 1122 Miguel Cerviño 33 Ricardo Pérez-Martínez 4422 Ana María
Pérez-García 3322 Jordi Cepa 556622 Maritza A. Lara-López 77 Mauro González-
Otero 556622 Manuel Castillo-Fraile 11 José Ignacio González-Serrano 8822
Castalia Alenka Negrete 99 Camen P. Padilla-Torres 5566221010 Irene Pintos-
Castro 1111 Mirjana Pović 121213131414 Emilio Alfaro 1313 Zeleke Beyoro-Amado
121215151616 Irene Cruz-González 88 José A. de Diego 99 Rocío Navarro Martínez
22 Brisa Mancillas 11 Mónica I. Rodríguez 11 Iván Valtchanov 1717

###### Abstract

Aims. In this paper, we revisit the data for the galaxy cluster ZwCl
0024.0+1652 provided by the GLACE survey and study the mass–metallicity
function and its relationship with the environment.

Methods. Here we describe an alternative way to reduce the data from OSIRIS
tunable filters. This method gives us better uncertainties in the fluxes of
the emission lines and the derived quantities. We present an updated catalogue
of cluster galaxies with emission in H$\alpha$ and [N ii]
$\lambda\lambda$6548,6583. We also discuss the biases of these new fluxes and
describe the way in which we calculated the mass–metallicity relationship and
its uncertainties.

Results. We generated a new catalogue of 84 emission-line galaxies with
reliable fluxes in [N ii] and H$\alpha$ lines from a list of 174 galaxies. We
find a relationship between the clustercentric radius and the density of
galaxies. We derived the mass–metallicity relationship for ZwCl 0024.0+1652
and compared it with clusters and field galaxies from the literature. We find
a difference in the mass–metallicity relationship when compared to more
massive clusters, with the latter showing on average higher values of
abundance. This could be an effect of the quenching of the star formation,
which seems to be more prevalent in low-mass galaxies in more massive
clusters. We find little to no difference between ZwCl 0024.0+1652 galaxies
and field galaxies located at the same redshift.

###### Key Words.:

Galaxies: clusters: individual: ZwCl 0024.0+1652 – Galaxies: star formation –
Galaxies: abundances – Cosmology: observations

##  1 Introduction

The evolution of the star formation process in cluster galaxies is still an
open field. The pioneering works of Gisler (1978) and Dressler et al. (1985)
established that emission-line galaxies (ELGs) are more numerous in the field
than in clusters. Other works have delved deeper into the issue: for example
Balogh et al. (1997) found that cluster galaxies are likely to have less
significant star formation processes than field galaxies, finding that the
star formation rate (SFR) in cluster galaxies is lower when compared with
field galaxies; Cucciati et al. (2010) and Cantale et al. (2016) reported that
the fraction of blue galaxies is significantly lower in groups than in field
galaxies; Allen et al. (2016) found that the mass-normalised SFR for the
highest-mass galaxies in the field was larger than for galaxies in clusters;
Old et al. (2020) found that the star-forming main sequence for galaxies in
clusters is lower compared to that of the field galaxies, and this effect was
more significant for lower-mass galaxies; and Vaughan et al. (2020) found that
the average H$\alpha$-to-continuum-size ratio of ELGs is smaller in cluster
galaxies than in field galaxies with the same stellar mass.

The reasons for these differences may be associated with the quenching of star
formation in cluster galaxies. Boselli & Gavazzi (2006) list the possible
causes in their review: starvation (removal of the outer galaxy halo that
feeds the star formation, preventing further infall of gas into the disc
(Larson et al. 1980); ram-pressure stripping (removal of the galactic gas by
moving at high velocities in dense and hot intergalactic medium (Gunn & Gott
1972); or harassment (interactions of the galaxy with the potential of the
cluster as a whole (Moore et al. 1996), among others.

Metallicities have been found to be affected by this quenching (see Darvish et
al. 2015). There is a tight correlation between the abundance of galaxies and
their stellar mass: the more massive the galaxy, the higher the metallicity.
This is called the mass–metallicity relationship (hereafter, MZR). As proposed
by Lara-López et al. (2010), this relationship may be an indicator of a more
profound link between metallicity, stellar mass, and SFR, which has been
referred to as the ‘fundamental plane’.

It is assumed that pristine gas falls into galaxies and is then turned into
stars and released at the end of the life of the massive stars. Some of this
gas, enriched with metals, may escape the gravity well of its parent galaxy.
However, in more massive galaxies with larger gravity wells, this gas will
have a lesser tendency to escape when compared with galaxies of lower masses.
Therefore, in general, in the absence of interactions with other galaxies,
this may imply that galaxies with larger stellar masses will be more metallic.
Following this criterion, the MZR may be influenced by the environment:
galaxies located within the denser areas of a cluster could potentially be
subject to a greater number of interactions than field galaxies or galaxies in
the outskirts of the same cluster. These interactions may strip galaxies of
their less metallic gas by some of the processes mentioned above, and these
galaxies will therefore have a higher metallicity content than those in the
field or in less dense environments (see e.g. Maier et al. 2019, and
references therein). The MZR has been described in many different contexts;
for example, in field galaxies in the local Universe (Tremonti et al. 2004,
Lee et al. 2006 or Andrews & Martini 2013, among others), in field galaxies at
higher redshifts (i.e. Calabrò et al. 2017 for $0.1<z<0.9$ or Hayashi et al.
2009 for up to $z\sim 2$), and in galaxies in clusters (see e.g. Sobral et al.
2016, Ciocan et al. 2020, Maier et al. 2019, Lara-López et al. 2022 or Pérez-
Martínez et al. 2023, among others).

Here, we present a revisiting of the cluster ZwCl 0024.0+1652 (hereafter
Cl0024) using improved reduction techniques. The paper is organised as
follows. In Section 2, we describe the GLACE survey, explain the inverse
convolution method, and present the galaxy cluster Cl0024. In Section 3, we
discuss the way we parameterised the environment. Section 4 is devoted to the
MZR for Cl0024, where we also present a comparison with other clusters and
field galaxies from the literature. In Section 5 we discuss our main results
and present the conclusions of this work. Throughout this paper, we assume a
standard $\Lambda$-cold dark matter cosmology with $\Omega_{\Lambda}=0.7$,
$\Omega_{m}=0.3$, and
$\mathrm{H}_{0}=70\,\mathrm{km}\,\mathrm{s}^{-1}\,\mathrm{Mpc}^{-1}$.

##  2 The GLACE survey: Revisiting the pseudospectra

The GLACE (GaLAxy Cluster Evolution Survey) is a narrow-band survey of ELGs
and AGNs in a sample of galaxy clusters located at different redshifts. The
aim of GLACE is to study the variations in galaxy properties as a function of
environment. The main objectives and methods of this survey are described in
detail in Sánchez-Portal et al. (2015). Briefly, GLACE was developed as the
cluster counterpart of the OTELO survey (Bongiovanni et al. 2019), observing
clusters at $z\sim 0.4,0.63,$ and $0.86$, and mapping several rest-frame
optical emission lines, such as H$\alpha$, H$\beta$, [N ii], [O ii]$\lambda
3727,$ and [O iii]$\lambda 5007$.

We selected Cl0024 from the lower redshift observations; it has a precise
redshift of $z=0.395$. We obtained data through tunable filter tomography,
employing the instrument OSIRIS on the 10.4 meter GTC (Gran Telescopio
Canarias) at Roque de los Muchachos Observatory. The whole process of
obtaining and reducing the data is described in Sánchez-Portal et al. (2015).
Briefly, the data were obtained through the technique of TF tomography (Cepa
et al. 2013), where a series of tuned images were obtained, sampling the [N
ii] and H$\alpha$ lines. The result is a pseudospectrum for each ELG. This
pseudospectrum is a convolution of the real spectrum of the ELG with the
response function of the OSIRIS instrument. A total of 174 ELGs were detected
from the cluster. From there, fluxes from the H$\alpha$ and [N ii] lines were
derived, as well as redshifts. AGNs were detected and marked as such (see
Sánchez-Portal et al. 2015 for details).

###  2.1 The inverse convolution method

The method to derive the fluxes and their uncertainty for the 174 ELGs is
detailed in Eqs 10 and 11 of Sánchez-Portal et al. (2015); in Eq. 11, the
uncertainty is obtained using a first-order approximation of the variance in
uncertainty propagation theory. However, such first-order approximations lead
to larger, unrealistic variance estimates, especially when ratios of
quantities are used and the nominal variances of the quantities involved have
an associated relative standard deviation of greater than 10% (as is the case
in Eq. 10 of Sánchez-Portal et al. (2015)). Worse yet, for large relative
standard deviations, not only is the uncertainty overestimated, but the
nominal value of the inferred quantity is also biased if ratios or logarithm
operations are involved (see Cerviño & Valls-Gabaud, 2003, for a case of study
for different inference types).

Fortunately, since this first iteration with the data, a new method to obtain
the fluxes from the pseudospectra has been developed. This latter is based on
a Monte Carlo sampling of the multi-parametric probability distribution
function (PDF) of the intensity of the continuum fluxes and the shape of the
principal lines in the pseudo-spectrum produced by the tunable filters. The
multi-parametric PDFs are also used to obtain the associated PDFs of line
ratios and related quantities, such as metallicities, and this approach has
been employed in several works related to the OTELO Survey (i.e. Bongiovanni
et al. 2020, Nadolny et al. 2020, Cedrés et al. 2021, and Navarro Martínez et
al. 2021).

This method, named ‘inverse convolution’, is described in detail in Appendix A
of Nadolny et al. (2020). Briefly, a model spectrum as a rest-frame spectrum
is simulated by Gaussian profiles of the [N ii] and H$\alpha$ lines defined by
their amplitude, common line width for three lines, and a constant continuum
level. The model is then convolved with the spectral response of the OSIRIS
instrument and then compared with the observational data through a likelihood
function. This is repeated a total of $10^{5}$ times. After this process, we
obtain a PDF shape for each parameter, as well as all the required additional
quantities. This allows us to obtain more reliable values for the H$\alpha$
and [N ii] fluxes, which will lead to more informed estimates of the
metallicity. The inverse convolutions can be used to derive the redshift, the
continuum, the fluxes of the lines (H$\alpha$, [N ii]$\lambda 6548$ and [N
ii]$\lambda 6583$ in this case), and the full width at half maximum (FWHM) of
the lines. The uncertainties can be directly obtained from the PDF. In our
case, we use the 68% confidence interval around the mode of the PDF for each
parameter.

This new way of obtaining the fluxes from the emission lines allows us to
revisit the data from Cl0024 and study its properties in greater detail. The
results can be seen in Fig. 1, where present the pseudospectrum of the galaxy
id:424 (upper panel) and the deconvolved spectrum (lower panel) as well as the
68% confidence interval of the fit.

Figure 1: Pseudospectrum with fitting components (upper panel) and deconvolved
spectrum (lower panel) for the tier 1 galaxy id:424. The filled grey area
represents the 68% confidence interval.

After all the galaxies from Sánchez-Portal et al. (2015) were reprocessed by
the inverse convolution method, a classification of the fitted spectra —based
on careful visual inspection— was carried out, taking into account the quality
of the deconvolution as well as the uncertainties. In this new classification,
the galaxies were sorted into tiers. Tiers 1 to 3 are well-deconvolved
galaxies, with galaxies from tier 1 having the smallest uncertainties and
those from tier 3 galaxies having the largest. We also made sure that all
fluxes, redshifts, and derived quantities present PDFs that can be
approximated as Gaussians. This means that the mode, median, and mean of the
PDFs are very close, and so we can use any of them interchangeably. From the
total of 174 galaxies by Sánchez-Portal et al. (2015), we derived reliable
fluxes for 84 of them. For the remaining galaxies, in 47 cases we were unable
to separate the [N ii] lines from the H$\alpha$ line, 16 cases presented a
complex H$\alpha$ line made up of several superimposing components, and in 27
cases the algorithm did not converge. Of the cases with a composite H$\alpha$
line and those for which we were unable to separate H$\alpha$ and [N ii]
lines, 75% and 30% are classified as AGNs by Sánchez-Portal et al. (2015)
respectively. Table 1 contains a summary of the classification for all the
galaxies. In Table LABEL:Tablagorda, we present the results of the inverse
convolution for the 84 galaxies from tiers 1 to 3, indicating the H$\alpha$
and [N ii]$\lambda 6584$ fluxes as well as the metallicity. Hereafter, unless
otherwise stated, we only analyse and discuss galaxies from tiers 1 to 3.

Table 1: Number of galaxies in the sample segregated by quality.

Quality tier | Number of galaxies
---|---
Tier 1 | 14
Tier 2 | 38
Tier 3 | 32
Total deconvolved | 84
No deblending | 47
Composite H$\alpha$ line | 16
Unable to deconvolve | 27
Total galaxies | 174
111Only galaxies with tiers 1 to 3 are considered to have reliable fluxes for
the H$\alpha$ and [N ii] lines.

###  2.2 Galaxy cluster ZwCl 0024.0+1652

Cl0024 is a galaxy cluster located at mid-redshift ($z\sim 0.4$); it has been
described as having a rather complex structure that does not appear at visible
wavelengths (Czoske et al. 2002), and is low in X-ray flux and central
velocity dispersion (Johnson et al. 2016). Cl0024 is one of the original
clusters of the pioneering paper by Butcher & Oemler (1978), where the so-
called Butcher–Oemler effect was first described. Beyoro-Amado et al. (2021)
reported that the core is almost totally made up of early-type galaxies with
no emission lines up to $\sim 500$ kpc, with increasing numbers of late-type
galaxies in the outskirts. Sánchez-Portal et al. (2015) suggest that this
seems to indicate a quenching of star formation and AGN activity.

The cluster has been divided into at least two substructures by several
authors (i.e. Czoske et al. 2002, Moran et al. 2007, Sánchez-Portal et al.
2015): one that constitutes the main cluster and centred at $z=0.395$
(thereafter Structure A) and a second group of infalling galaxies at $z=0.381$
(thereafter Structure B). Czoske et al. (2002) suggest that these two
structures are colliding along the line of sight. Moreover, Czoske et al.
(2002) and Sánchez-Portal et al. (2015) propose the existence of a third
interacting structure at about $z\sim 0.42$.

In the upper panel of Fig.2, we show the distribution of redshifts derived
from the inverse convolution method (solid black line) and the redshifts from
Sánchez-Portal et al. (2015) (blue dashed line). The two structures are
clearly identified in the histogram. Moreover, the proposed third structure
can be seen at $z\sim 0.42$. The lower panel shows the residuals between the
new and the old determination. The coincidence between both methods is very
good, with the majority of the galaxies presenting differences of less than
0.001 in redshift.

Figure 2: Redshift distribution for the galaxies presented in this study of
Cl0024. The upper panel shows the distribution of redshifts derived from the
new determination (solid line) and the determination from Sánchez-Portal et
al. (2015) (dashed line). The lower panel shows the distribution of the
residuals between the old and the new redshift determination. This figure
includes all the galaxies (tiers 1 to 3, and ‘no deblending’ and ‘composite
H$\alpha$ lines’).

Figure 3 shows the H$\alpha$ flux derived from the inverse convolution method
versus the H$\alpha$ flux derived from Sánchez-Portal et al. (2015). There is
a very good correlation between the results of the two methods (as expected).
Nevertheless, it seems that the determination by Sánchez-Portal et al. (2015)
has marginally higher values when compared with those from the inverse
convolution method.

Figure 3: Comparison between the obtained values for the H$\alpha$ emission
line uncorrected for extinction from Sánchez-Portal et al. (2015) and the new
one from this work. The red continuous line indicates a 1:1 relation.

On the other hand, the real strength of the inverse convolution method is
shown in Fig. 4 where it is compared to the distribution of the relative
uncertainties obtained by Sánchez-Portal et al. (2015) and the new ones,
defined as the 68% confidence interval. The median value of the distribution
of relative uncertainties in the H$\alpha$ flux from the Sánchez-Portal et al.
(2015) determination is above 20%, while with the inverse convolution method
is just over 10%, with many galaxies having uncertainties in the H$\alpha$
line at 5% and below. This difference allows us to calculate all the derived
quantities (such as the abundance) with enough precision to to make robust
conclusions.

Figure 4: Histogram of the relative uncertainty in the flux determination in
the H$\alpha$ line. The red histogram is the error from Sánchez-Portal et al.
(2015). The red dotted line is the median value of the relative error. The
black histogram is the relative error from this work. The grey dashed line
represents the median value.

##  3 Parameterisation of the environment

We characterise the local environment of our sample by applying a modified
version of the method first described in Dressler (1980). We calculated the
projected surface density of the galaxies in our data set, estimated as the
source density in the area encircled between the object and the tenth nearest
galaxy above a certain magnitude threshold ($\Sigma_{10}$) according to the
following Eq.:

$\Sigma_{10}=\frac{10+1}{\pi R_{10}^{2}},$ (1)

where $R_{10}$ is the projected distance in megaparsecs from the considered
galaxy to the tenth closest object. In this case, we take objects brighter
than I $\sim$ 21.5 measured in GLACE ancillary data (Sánchez-Portal et al.
2015), where the completeness of the spectroscopic member catalogue is above
70% along the full area covered by the observations (Perez-Martinez et al. in
prep). We also take into account the two main structures described in section
2.

Figure 5: Logarithm of $\Sigma_{10}$ as a function of the clustercentric
radius for all the galaxies from Sánchez-Portal et al. (2015). Open circles
and downward-pointing filled triangles are the galaxies from Structure A and
B, respectively. The grey and red dashed lines show the median value for
galaxies in Structures A and B, respectively. The filled grey and red areas
represent the median absolute deviation for Structures A and B, respectively.

Figure 5 shows the logarithm of the $\Sigma_{10}$ as a function of the
clustercentric radius for all galaxies from Sánchez-Portal et al. (2015). We
take the centre of the cluster to be that suggested in Sánchez-Portal et al.
(2015). The median values for the Structure A and B galaxies are indicated by
the grey dashed line and the red dashed line, respectively. A tight
correlation exists, with galaxies with a lower clustercentric radius having a
larger value in $\log(\Sigma_{10})$. This is not surprising because, we expect
to find a denser environment for galaxies in the inner parts of the cluster.
The galaxies from the infall Structure show, in general, lower values in
density when compared with galaxies in the main structure and can be clearly
identified in Fig. 5. We note that the clustercentric radius used in this case
originates from the centre of Structure A, and so, in principle, we would
expect a greater scattering of galaxies in Structure B. However, the figure
shows a tight correlation for those galaxies. This seems to indicate that the
centres of the two structures must be relatively closely projected onto the
sky, as suggested by Czoske et al. (2002).

##  4 The mass–metallicity relationship

To determine the abundance of the ELGs of Cl0024, and keeping in mind that we
are restricted to the H$\alpha$ and [N ii] lines, we employed the recipe given
by Pettini & Pagel (2004) using the ratio $N2$. This ratio is defined as

$N2\equiv\log\left(\frac{\left[\mathrm{N}\,\textsc{ii}\right]\lambda
6583}{\mathrm{H}\alpha}\right).$ (2)

Then, the metallicity is derived using the following equation:

$12+\log\left(\mathrm{O}/\mathrm{H}\right)=8.90+0.57\times N2.$ (3)

No extinction correction is necessary given the proximity in wavelength of the
two lines (H$\alpha$ and [N ii]$\lambda 6583$). To avoid the systematic effect
of different abundance calibrations, when comparing the MZR with data from the
literature, all the metallicities presented in this work are calculated using
the $N2$ method. We only calculated the abundance for galaxies marked as star-
forming galaxies in the catalogue presented in Sánchez-Portal et al. (2015),
taking out all those classified as AGNs.

We obtained stellar mass estimations for star-forming galaxies by modelling
SEDs based on the broad-band ($BVRIJK_{\rm s}$), publicly available optical
and near-infrared data described by Moran et al. (2005), and fixing redshift
to the values derived from the inverse convolution method. We used a set of
low-resolution, composite stellar population templates with delayed
exponential star formation histories (SFHs) obtained from the stellar
population synthesis models of Bruzual & Charlot (2003), adopting four stellar
metallicity values (from $Z=0.0004$ to $Z_{\odot}$; Padova 1994 tracks) and an
initial mass function (IMF) according to Chabrier (2003). The age of star
formation was constrained to between 10 Myr and 10 Gyr (about the age of the
Universe at the mean redshift of the cluster) and the e-folding times in the
$0.01<\tau<30\,{\rm Gyr}$ range. The best fit of the models was obtained using
LePhare (Arnouts et al. 1999; Ilbert et al. 2006), assuming the Calzetti et
al. (2000) extinction law with intrinsic reddenning $E(B-V)$ varying between 0
and 0.5 mag. The median $\chi^{2}_{\rm red}$ value obtained from SED fitting
is 1.57 and the medians of stellar mass estimations are distributed in the
$8.4\leq\log\,(M_{*}\,[M_{\odot}])\leq 11$ range, with a mean uncertainty of
about 0.3 dex. Those estimations are fully consistent with those obtained from
exponentially declining SFH and the same prescriptions as those indicated
above.

In order to make a meaningful comparison with other MZRs from the literature,
we follow the recipe suggested by Curti et al. (2020), where the data were
binned and fitted to the equation adapted from Zahid et al. (2014), which in
turn is an evolution of the functional relationship presented in Moustakas et
al. (2011):

$12+\log(\mathrm{O}/\mathrm{H})=\mathrm{Z}_{0}+\log\left(1-10^{-\left(\frac{M_{*}}{M_{0}}\right)^{\gamma}}\right),$
(4)

where according to Curti et al. (2020), $M_{*}$ is the stellar mass in solar
masses; $Z_{0}$ is the saturation metallicity, which gives the upper
metallicity limit of the MZR; $M_{0}$ is the turnover mass; and $\gamma$ is
the power law that controls the MZR at $M_{*}<M_{0}$. This fit is applied to
binned data of $\log\,(M_{*})$. Curti et al. (2020) used 0.15 dex stellar mass
bins. In order to improve the dispersion of the fits, we decided to fix the
saturation abundance. According to Marino et al. (2013), the $N2$ ratio begins
to saturate at $N2\simeq-0.4$, meaning it no longer presents a linear
relationship with oxygen abundance. This is equivalent to 12+$\log$(O/H)$\sim
8.67$, and so we assume that this is the asymptotic metallicity of the MZR for
galaxies with $M_{*}>>M_{0}$. In this way, the parameters to fit are reduced
to two: the turnover mass $M_{0}$, and the exponent of the power law $\gamma$.

To obtain the parameters of the fitting, a set of Monte Carlo simulations were
carried out. Each galaxy had a simulated random value of $\log(M_{*})$ and of
$12+\log(\mathrm{O/H})$ inside the uncertainty of both quantities of the given
object. Then, a new MZR was constructed with these simulated values. After
that, the MZR was binned in stellar mass. Due to our smaller number of
galaxies in clusters at larger redshift, when compared with the Curti et al.
(2020) set, our bins need to be larger in order to include a significant
number of galaxies. In the end, we employed 0.62 dex stellar mass bins for our
data. Also, this same binning was employed for all the comparison clusters
from the literature. This process was repeated 100,000 times, resulting in a
set of binned MZRs. Each binned MZR was then fitted using a least-squares
minimisation algorithm based on the Levenberg–Marquardt method. The mean and
standard deviation were calculated for all the fits. The mean was considered
the best fit for the MZR, and the standard deviation was assumed to be the
main uncertainty for the MZR. Moreover, the resultant parameters ($M_{0}$ and
$\gamma$) and their uncertainty were calculated as the mean of all the values
obtained in every fit and its standard deviation, respectively.

In Fig. 6 we show the MZR for the ELGs detected in the cluster Cl0024. The
grey dashed line represents the mean fitted Eq. 4. The filled grey area is the
1$\sigma$ uncertainty on the fits. As expected, there is a tight correlation
between the stellar mass and the metallicity of the galaxies, with lower-mass
galaxies having lower values of oxygen abundance.

Figure 6: MZR for the SFGs of Cl0024. Our data are the open circles, the
black-dashed line represents the mean of the fitted values employing the
recipe by Curti et al. (2020), and the grey area represents the 1$\sigma$
deviation of the fits.

In Table 2 we summarise the properties of the clusters used in this study.
Table 3 lists the fit parameters of Eq. 4 for all the clusters, local SDSS
galaxies in clusters, and field galaxies at $z\sim 0.4$, which we use for
comparison in the following sections.

Table 2: Main characteristics of the galaxy clusters employed in this study.

Cluster name | Redshift |  $M_{200}$ [$M_{\odot}$] |  $R_{200}$ [Mpc] | $<\\!Z/Z_{\odot}\\!>$
---|---|---|---|---
Hercules Supercluster | 0.033 |  $2.1\times 10^{15}$(1) | N/A | N/A
RX J2248–443 | 0.348 |  $2.81\times 10^{15}$ (2) | 2.6 (3) | 0.26 (4)
Cl 0024 (this work) | 0.395 |  $5.85\times 10^{14}$ (5) | 1.73 (5) | 0.22 (6)
MACS J0416.1–2403 | 0.397 |  $1\times 10^{15}$ (7) | 1.8 (7) | 0.24 (8)
Cl 0939+4713 | 0.41 |  $1.7\times 10^{15}$ (3) | 2.13 (3) | 0.2 (9)
XMMXCS J2215.9–1738 | 1.5 |  $6.3\times 10^{14}$ (10) | 1.23 (10) | N/A
XMM–LSS J02182–05102 | 1.62 |  $7.7\times 10^{13}$ (11) | 0.49 (11) | N/A
PKS 1138–262 | 2.16 |  $1.71\times 10^{14}$ (12) | 0.53 (12) | N/A
222First column is the cluster name, second column is the redshift of the
cluster, third column is the $M_{200}$ in solar masses, fourth column is the
$R_{200}$ in Mpc, and fith column is the mean value of the iron abundance in
the intracluster gas in units of solar metallicity.

(1) Monteiro-Oliveira et al. (2022); (2) Kesebonye et al. (2023); (3) Koyama
et al. (2011); (4) De Filippis et al. (2003); (5) Sánchez-Portal et al.
(2015); (6) Zhang et al. (2005); (7) Bonamigo et al. (2018); (8) Bonamigo et
al. (2017); (9) Rahaman et al. (2021); (10) Maier et al. (2019); (11) Pierre
et al. (2012); (12) Shimakawa et al. (2014).

Table 3: Parameters fitted in Eq. 4 for different clusters and field galaxies.

Cluster | $\log(M_{0}/M_{\odot})$ | $\gamma$
---|---|---
SDSS local clusters | 9.50$\pm$0.17 | 0.32$\pm$0.07
Hercules Supercluster | 9.72$\pm$0.22 | 0.49$\pm$0.05
RX J2248–443 | 9.32$\pm$0.45 | 0.41$\pm$0.04
Cl0024 (This work) | 10.68$\pm$0.34 | 0.36$\pm$0.09
MACS J0416.1–2403 | 10.46$\pm$0.05 | 0.42$\pm$0.08
Field galaxies at $z\sim 0.4$ | 11.66$\pm$0.51 | 0.21$\pm$0.05
Cl 0939+4713 | 9.42$\pm$1.0 | 0.12$\pm$0.08
XMMXCS J2215.9–1738 | 10.59$\pm$0.06 | 0.63$\pm$0.17
XMM–LSS J02182–05102 | 11.08$\pm$0.52 | 0.32$\pm$0.07
PKS 1138–262 | 10.48$\pm$0.26 | 0.30$\pm$0.08
333First column is the cluster name; second column is the logarithm of the
turnover mass expressed in solar masses; column 3 is the exponent of the power
law $\gamma$.

###  4.1 The effect of the environment

(a)

(b)

Figure 7: MZR for Cl0024. Left panel: Open black circles and red filled
circles are the galaxies with $R<0.5\,R_{200}$ and galaxies in Structure B,
respectively. Right panel: Open red circles and open black circles are the
galaxies from Cl0024 within medium and high-density zones
($\log(\Sigma_{10})\geqq 1.7$) and low-density zones
($\log(\Sigma_{10})<1.7$), respectively.

Figure 7 shows the MZR for the galaxies of Cl0024. In the left panel, we only
show the galaxies of the main structure inside $0.5\,R_{200}$ (black open
circles) and the galaxies of the infall group (red circles). In the right
panel, we separate Cl0024 galaxies into medium and high-density zones,
$\log(\Sigma_{10})\geqq 1.7$, following the criteria suggested by Sobral et
al. (2016); these are represented by open red circles, and galaxies in low-
density zones are represented by open black circles. In order to eliminate
some of the noise in the figure, we only consider the galaxies with an
uncertainty of 1% in the abundance determination.

Maier et al. (2019) found that the galaxies with $0.5\,R_{200}$ have enhanced
metallicities when compared to the infall galaxies of the same cluster.
However, we find no such differences between the infall group and the galaxies
inside $0.5\,R_{200}$. An explanation for this difference could be that XMMXCS
J2215.9–1738 infall galaxies come from the field, and are therefore poorer in
metals when compared with the galaxies from the core. On the other hand, the
infall group in Cl0024 is, in reality, a subgroup of galaxies with a certain
density above that of the field galaxies.

The right panel of Fig. 7 also shows that there is no difference between
galaxies in high-density zones and those in low-density zones. All these
results seem to agree with Sobral et al. (2016), who found no significant
dependence of the MZR on the environment inside the same cluster.

Figure 8: Metallicity as a function of clustercentric radius for Cl0024. The
open black circles are the galaxies from Structure A. The downward black-
filled triangles are the galaxies from Structure B. The grey dashed line is
the median of the values for Structure A. The blue dotted line indicates the
position of the virial radius for Cl0024 from Sánchez-Portal et al. (2015).

Figure 8 shows the abundance as a function of the clustercentric radius for
galaxies on Structures A and B. The median value is represented by a grey
dashed line and only for galaxies in Structure A. This median value and its
uncertainty were calculated in the same way as the binned median MZR was
computed, as described in section 4. The filled grey zone represents the
uncertainty on the median value of $1\sigma$. The blue dotted line represents
the position of the $R_{\mathrm{virial}}$ from Sánchez-Portal et al. (2015).
There is a slight dependence of the metallicity on clustercentric radius, with
inner galaxies presenting, at least on average, larger abundances. We find no
clear differences between galaxies in Structures A and B. However, Maier et
al. (2015) found that the metallicities of galaxies inside the virialized part
of the cluster they studied presented enhanced metallicities by about 0.1 dex
when compared to infalling galaxies. Here, the difference in median abundance
between galaxies well inside the virialized part and galaxies in the outside
zones is about $\sim 0.25$ dex, which is slightly larger than the difference
reported by Maier et al. (2015). This is a result similar to the one presented
in Ellison et al. (2009), where it is reported that the galaxies are slightly
more metal-rich if they are located in overdensity zones. Lara-López et al.
(2022) also find a dependence of the metallicity on projected clustercentric
radius for galaxies in the Fornax cluster. On the other hand, Mouhcine et al.
(2007) and Cooper et al. (2008) found weak or no connection between abundance
and environment in galaxies. However, Cooper et al. (2008) expanded the study,
and after removing the mean colour–luminosity–environment relation, found a
residual relationship between environment and metallicity.

###  4.2 Effect of the mass of the cluster

In Fig. 9 we make a comparison with data from cluster RX J2248-4431
(thereafter RXJ2248) from Ciocan et al. (2020), cluster MACS J0415.1–2403
(thereafter MACS0415) from Maier et al. (2016), and cluster Cl0939+4713
(thereafter Cl0939) from Sobral et al. (2016). All clusters are at a similar
redshift.

Figure 9: Mass–metallicity relationship for ELGs in clusters at similar
redshift. Black circles represent our data. The cyan downward-pointing
triangles are the galaxies from Ciocan et al. (2020) for the cluster RX
J2248-4431. The blue pentagons represent the galaxies from Sobral et al.
(2016) for the cluster Cl 0939+4713. The red upward-pointing triangles are the
galaxies from Maier et al. (2016) for the cluster MACSJ0416.1–2403. Figure 10:
Comparison of MZR between Cl0024 and clusters at the same redshift, as well as
galaxies from clusters at low redshift from Tempel et al. (2017). The colour
coding is reported in the legend. All data were fitted following the recipe
presented in Curti et al. (2020).

Figure 10 shows the fitted functions described in Eq. 4 with one standard
deviation (filled regions) of the MZR for the four clusters and the SDSS
galaxy clusters (black thick line) from Tempel et al. (2017) at local
redshift. It should be noted that all four clusters have been chosen in such a
way that they are in a similar non-relaxed status (i.e. Sánchez-Portal et al.
2015 and Czoske et al. 2002 for Cl0024; Schindler et al. 1998 for Cl0939; Mann
& Ebeling 2012 for MACS0416; and Rahaman et al. 2021 and Kesebonye et al. 2023
for RXJ2248). We can see that the fitted value of the abundance for RXJ2248 is
systematically larger than the abundance for our data of Cl0024. On the other
hand, the data from MACS0416 follow, inside uncertainties, the same path as
Cl0024. The data from Cl0939 seem to follow a shallower path when compared
with RXJ2248. However, the fact that the data from Sobral et al. (2016) do not
include uncertainties should be taken into account. In the end, we decided to
add a $5\%$ error to each measured flux. Therefore, it is possible that, in
this case, we are underestimating the total uncertainty of the fit.
Nevertheless, the value of the abundance for Cl0939 is larger than Cl0024 for
all ranges in stellar mass. Moreover, most of the time, the abundance fitted
for RX J2248 and Cl0939 is larger than or equal to that in local clusters, and
the abundances for Cl0024 and MACS0416 are below them. If we take into account
the fitted parameters presented in Table 3, we can see that the main
difference between the clusters lies in the turnover mass,
$\log(M_{0}/M_{\odot})$, with Cl0024 and MACS0416 having a large value (10.68
and 10.46 respectively) when compared to Cl0939 and RXJ2248 clusters. This
indicates that $\gamma$, the low-mass end slope, is dominant for almost all
stellar masses sampled in Cl0024 and MACS0416. The shallow behaviour of
Cl0939, represented by the low value of $\gamma=0.12\pm 0.008$, may be due to
the lack of low-stellar-mass galaxies when compared with the other two
clusters, which generates a poor constraint when fitting. On the other hand,
the $\gamma$ obtained for Cl0024, RXJ2248, MACS0416, and local clusters are
similar within the uncertainties.

From Table 2, it can be seen that the $M_{200}$ masses for RXJ2248 and Cl0939
are $2.81\times 10^{15}M_{\odot}$ and $1.7\times 10^{15}M_{\odot}$,
respectively. Both are an order of magnitude higher than the reported mass for
Cl0024. On the other hand, the $M_{200}$ for MACS0416 is somewhat lower, less
than two times larger than the mass for Cl0024.

According to Calabrò et al. (2017), the MZR is a consequence of the conversion
of gas into stars inside galaxies, a process regulated by gas exchanges with
the environment. There should therefore be a correlation between the total
abundance of the gas in the galaxies and the metallicity of the intracluster
gas. However, in Table 2 we can see that the average values of $Z/Z_{\odot}$
for Cl0024, Cl0939, MACS0416, and RXJ2248 are similar.

Nevertheless, we have to take into account that the metallicity is not
constant within each cluster, and here we are comparing average values.
According to De Filippis et al. (2003), Cl0939 shows a high-metallicity region
(they called it M1, with $Z/Z_{\odot}\simeq 0.33$) with a peak in galaxy
number density. These authors suggest that the galaxies there may have
enriched the gas in the zone compared with others with a lower galaxy number
density. For RXJ2248, Rahaman et al. (2021) gives $Z/Z_{\odot}\simeq 0.36$ for
the centremost zone of the cluster. On the other hand, Zhang et al. (2005)
give a value of $Z/Z_{\odot}\simeq 0.25$ for the most metallic zone in Cl0024.
If we take this into account, it is clear that Cl0939 and RXJ2248 show zones
where the intracluster gas has larger abundances than the most metallic zone
of Cl0024.

Figure 11 shows the logarithm of $M_{0}$ as a function of M200 for all the
clusters presented in Table 2. The filled black circle is the cluster Cl0024,
and the open black circles are the clusters at about $z\sim 0.4$. The open red
triangles are the clusters at larger redshift. The filled black star
represents the Hercules Supercluster at $z\sim 0.033$. The figure indicates
that the drive for the change in the turnover mass, and therefore in the
larger abundance for several clusters, lies in the M200 of the cluster: the
more massive the cluster, the higher the density of galaxies in certain zones,
even if the mean value of the metallicity of the intracluster gas is the same.

Figure 11: Fitted turnover parameter from Eq. 4 for all the clusters in the
study as a function of M200. The black-filled and open circles are, from left
to right, the clusters at $z\sim 0.4$, Cl0024, MACS0416, Cl0939, and RXJ2248,
respectively. The open red triangles are, from left to right, the clusters at
$z>1$, XMM–LSS J02182–05102, PKS 1138–262, and XMMXCS J2215.9–1738. The black
star is the Hercules Supercluster at $z\sim 0.033$.

###  4.3 Effect of the redshift

Figure 12: MZR for ELGs in clusters at different redshifts. We show the
galaxies from Cl0024 with errors in the determination of the metallicity below
0.3 dex and stellar masses over $\log(\mathrm{M}/M_{\odot})=9.5$ with filled
black circles, the data from Maier et al. (2019) for the cluster XMMXCS
J2215.9–1738 at $z\sim 1.5$ with blue circles, the data from Tran et al.
(2015) for XMM–LSS J02182–05102 at $z\sim 1.62$ with magenta circles, and the
data from Pérez-Martínez et al. (2023) for the cluster PKS 1138–262 at $z\sim
2.16$ with orange circles. Figure 13: MZR for ELGs in clusters at different
redshifts. We show the fitted values from Eq. 4 and the 1$\sigma$ deviation
for the three clusters (filled regions) employing the same colours as in Fig.
12.

Figure 12 shows the MZR for clusters at different redshifts: cluster XMMXCS
J2215.9-1738, hereafter XCS2215, at $z\sim$1.5 from Maier et al. (2019),
cluster XMM–LSS J02182–05102, hereafter LSS2182, at $z\sim 1.62$ form Tran et
al. (2015), and the cluster PKS 1138-262, hereafter PKS1138, at $z\sim$2.16
from Pérez-Martínez et al. (2023). XCS2215 and PKS1138 have similar values for
$M_{200}$. On the other hand, LSS2182 has a somewhat lower value of $M_{200}$,
but with large uncertainty, $(7.7\pm 3.8)\times 10^{13}M_{\odot}$ (Pierre et
al. 2012), and so we can consider that, in this case, we are mainly comparing
the effect of the redshift between the three clusters. In order to carry out a
meaningful comparison with the high-redshift clusters, which are limited to
high-stellar-mass galaxies, we restricted the galaxies of Cl0024 to stellar
masses larger than $\log(M_{*}/M_{\odot})>9.5$.

Figure 13 shows the fit drawn using Eq. 4. All the clusters are approximately
superimposed. The fitted values for PKS1138 and Cl0024 are the same within
uncertainties. From Table 3, we can see that the values of the turnover mass
are also compatible within the uncertainties, as shown in Fig. 11. The only
difference between the three clusters is on the $\gamma$ parameter, which in
this case is somewhat higher for XCS2215 when compared with the other
clusters.

For a fixed stellar mass, Ly et al. (2016) show that the abundance of field
galaxies evolves with redshift as
$\log(\mathrm{O}/\mathrm{H})\propto(1+z)^{-2.32}$. Moreover, Maiolino et al.
(2008) found an evolution on the MZR with redshift, and found it to be faster
at $2.2<z<3.5$ than at lower redshifts ($z<2.2$). Also Huang et al. (2019)
also found that the more evolved galaxies have higher metallicities at fixed
stellar masses when compared with less evolved galaxies. Also, Troncoso et al.
(2014) found that at $z\sim 3.4$, galaxies are more metal poor when compared
with lower-redshift galaxies. We do not find a similar relationship with
galaxy clusters, although we do not have data over $z>2$. This may imply that
the effect of the environment, such as the total mass of the cluster, may have
a larger influence than the canonical evolution of the cluster.

###  4.4 Comparison with field galaxies at the same redshift

Figure 14: MZR for Cl0024 and field galaxies at similar redshift. Cl0024
galaxies are represented as open grey circles, and field galaxies are
represented as filled blue triangles. The field galaxies come from Nadolny et
al. (2020), Amorín et al. (2015), and SDSS galaxies at $0.35<z<0.45$. The
fitted value for the MZR is represented by the grey dashed line and the blue
dashed line for Cl0024 and field galaxies, respectively. The filled areas
represent a 1$\sigma$ deviation of the fit.

Figure 14 shows the MZR for our galaxies in Cl0024 and field galaxies at the
same redshift from several authors: Amorín et al. (2015), Nadolny et al.
(2020), and SDSS field galaxies at $0.35<z<0.45$ from the Tempel et al. (2017)
catalogue, as well as SFGs with $0.35<z<0.45$ from VIMOS VLT Deep Survey
Database (see Le Fèvre et al. 2005 for the description of the survey). From
Table 3, we can see that the turnover mass for the field galaxies is the
largest one: $\log(M_{*}/M_{\odot})=11.66\pm 0.51$ when compared with
$\log(M_{*}/M_{\odot})=10.68\pm 0.34$ for Cl0024 or
$\log(M_{*}/M_{\odot})=9.50\pm 017$ for SDSS local clusters. This means that
the power-law part of Eq. 4 is dominant for a longer range.

In this case, for lower stellar masses, the abundance of Cl0024 is compatible
with the field galaxies at the same redshift, although the abundance is
slightly lower for Cl0024. However, at masses over
$\log(M_{*}/M_{\odot})>9.75$, the metallicity of the cluster is slightly
larger than that of the field galaxies. This seems to agree with the results
from Kacprzak et al. (2015), where the authors found no difference larger than
0.02 dex in the MZR of field galaxies at $z\sim 2$ when compared with cluster
galaxies at the same redshift.

##  5 Discussion and conclusions

Figure 15: MZR fits for galaxy clusters and field galaxies at $z\sim 0.4$. The
meaning of the colour code and shape of the lines is explained in the inset
key. Figure 16: Offsets from the fitted value of MZR of field galaxies at
$z\sim 0.4$ for clusters at similar redshift. The filled area represents a
1$\sigma$ deviation of the fit for field galaxies. The colour coding and shape
of the lines are explained in the inset key.

Figure 15 shows all the fits calculated for Eq. 4 for all the clusters
presented in this work and field galaxies at $z\sim 0.4$ from several sources.
We also include data from the Hercules Supercluster, a massive cluster
(M${}_{200}=2.1\times 10^{15}M_{\odot}$ according to Monteiro-Oliveira et al.
2022) at an almost local redshift of $z\sim 0.033$. These data were obtained
from Petropoulou et al. (2011) for Abell 2151 (the Hercules cluster proper)
and from Petropoulou (2012) for the rest of the subclusters that make up the
Hercules Supercluster.

Figure 16 shows the offsets from the fit of the MZR of field galaxies at
$0.35<z<0.45$. It is clear that Cl0024 and MACS0416 clusters both have lower
metallicity at lower stellar masses when compared with field galaxies; they
also present lower metallicities at all stellar masses when compared with the
two more massive clusters Cl0939 and RXJ2248. It should be noted that the two
massive clusters RXJ2248 and Cl0939 present a larger value of the turnover
parameter $M_{0}$ when compared with the rest of the clusters. The same also
happens at local values of the redshift for the Hercules Supercluster (see
Fig. 11).

The difference presented in Fig. 16 can be explained if we consider that more
massive clusters should present larger zones with high density, where the
galaxy–galaxy encounters as well as galaxy–cluster interactions (due to a
large virial radius) may be more effective than in smaller clusters. For this
reason, low-mass galaxies immersed in massive clusters tend to be more
metallic than those in smaller clusters. Therefore, in all these cases, the
value of the $M_{0}$ parameter may be a proxy for the mass of the cluster, and
therefore an indicator of the quenching of the star formation in clusters.
However, a proper study of the SFR is being carried out (De Daniloff et al. in
prep.) to confirm whether or not we are seeing a genuine quenching of the star
formation.

Regarding the dynamical state of the cluster, gas metallicity estimations
appear not to be affected by the membership of the galaxy to any of the two
identified structures confirmed by Sánchez-Portal et al. (2015). So far, we
can simply conclude that such structural duality only appears to affect
density indicators, in the sense that the median projected surface density
($\Sigma_{10}$) of Structure A is $\sim$0.7 dex denser than that of Structure
B (see Section 3).

On the other hand, as seen in Figs. 14 and 16, the less massive clusters
(Cl0024 and MACS0416) present values for MZR closer to those of field galaxies
at the same redshift, with differences in abundance at lower masses of about
-0.15 dex and 0.1 dex when compared to the more massive galaxies. This seems
to indicate that the conditions in these clusters are much closer to those of
field galaxies than to those of more massive clusters.

###  5.1 Conclusions

The main conclusions we can derive from this work are as follows:

  * •

We revisited the work of Sánchez-Portal et al. (2015) and applied the inverse
convolution method to the galaxy cluster Cl0024. We obtain a total of 84
galaxies (classified in tiers 1 to 3) from the original 174, where the method
yields better results in the determination of the fluxes and redshifts, and we
are able to reduce the uncertainties from over 20% in the old catalogue to
less than 10% for the 40% of ELGs, and even below 5% for the 20% of the ELGs.

  * •

From the redshift distribution, we find the two clearly differentiated
structures previously mentioned in Czoske et al. (2002) and Moran et al.
(2007), as well as the third interacting structure at about $z\sim 0.42$
suggested in Sánchez-Portal et al. (2015).

  * •

There is a relationship between the clustercentric radius and the logarithm of
the density, with galaxies with larger radii being located in zones with lower
density when compared with galaxies in the central zones. This effect happens
for the two main structures. This seems to imply that both structures may have
their nucleus in the line of sight, and therefore may be superimposed.

  * •

We obtain the MZR for the ELGs of Cl0024 after removing AGNs, indicated as
such in Sánchez-Portal et al. (2015).

  * •

We fitted Cl0024 MZR with the function proposed by Curti et al. (2020), as
well as several other clusters and field galaxies.

  * •

The MZR for Cl0024 presents a tight correlation between the metallicity and
the stellar mass of the galaxies, with galaxies with higher masses having
higher values of oxygen abundance.

  * •

We separated the galaxies of Structure A and Structure B in the MZR of Cl0024,
as well as the galaxies in high-density zones and low-density zones, and find
no differences between them. These results agree with Sobral et al. (2016),
where we find no significant dependence of the MZR on the environment inside
the same cluster.

  * •

We find a slight gradient of the metallicity with the clustercentric radius
for galaxies of Structure A. Galaxies tend to be more metallic in the inner
part of the cluster when compared with galaxies on the outskirts.

  * •

We find a difference in MZRs for galaxy clusters at the same redshift. Cl0939
and RXJ2248 data from Sobral et al. (2016) and Ciocan et al. (2020),
respectively, present larger values of abundance up to stellar masses over
$\log(M_{*}/M_{\odot})>10.5$. This difference seems to depend on the M200 of
each cluster, in the sense that clusters with larger masses have lower values
of the turnover mass, $M_{0}$. This is the case even if the intracluster gas
abundance is similar for the clusters. However, the clusters Cl0939 and
RXJ2248 present high-density zones with larger values of intracluster gas
abundance when compared with Cl0024 and MACS0416. This may imply that the more
massive clusters present more galaxy–galaxy as well as cluster–galaxy
encounters, which may cause a quenching of the star-forming processes.

  * •

No appreciable difference was found in the MZR of clusters of similar or lower
M200 but different redshift than Cl0024. This seems to suggest that the way in
which clusters evolve is heavily influenced by the total mass of the cluster.

  * •

When comparing Cl0024 with field galaxies at the same redshift, it we find
that the MZR fit of field galaxies presents a larger turnover mass, although
there is little difference in the abundance of lower stellar masses.

###### Acknowledgements.

B.C., M.S.P. and A.B. acknowledge the support of the Spanish Ministry of
Science, Innovation and Universities through the project
PID-2021-122544NB-C43. J.C. and M.G.-O. acknowledges the support of the
Spanish Ministry of Science, Innovation and Universities through the project
PID-2021-122544NB-C41. M.A.L.L. acknowledges support from the Spanish grant
PID-2021-123417OB-I00, and the Ramón y Cajal program funded by the Spanish
Government (RYC2020-029354-I). MC acknowledges the support of
PID2019-107408GB-C41 and PID2022-136598NB-C33 grants funded by
MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. B.C.
wishes to thank Carlota Leal Álvarez by her support during the development of
this paper. Based on observations made with the Gran Telescopio Canarias
(GTC), installed at the Spanish Observatorio del Roque de los Muchachos of the
Instituto de Astrofísica de Canarias, on the island of La Palma. This research
uses data from the VI-MOSS VLT Deep Survey, obtained from the VVDS database
operated by Cesam Laboratoire d’Astrophysique de Marseille, France. This
research has made use of NASA’s Astrophysics Data System. This research has
made use of the NASA/IPAC Extragalactic Database (NED), which is funded by the
National Aeronautics and Space Administration and operated by the California
Institute of Technology.

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##  Appendix A List of galaxies from tiers 1 to 3

Table 4: Catalogue of deconvolved galaxies with quality tiers 1 to 3. The first column is the identification number from Sánchez-Portal et al. (2015); column 2 is the new derived redshift; column 3 and 4 are the integrated H$\alpha$ flux and [N ii]$\lambda 6583,$ respectively, in erg cm-2 s${}^{-1}\times 10^{-17}$; column 5 is the derived abundance employing the N2 index; column 6 is the logarithm of the stellar mass in solar masses, and column 7 is the flag for AGN from Sánchez-Portal et al. (2015). The uncertainties are taken as one standard deviation of the value of each parameter. ID (#) | $z$ |  $F$(H$\alpha$) |  $F$([N ii]${}_{\,\lambda 6583}$) | $12+\log(\mathrm{O}/\mathrm{H})$ | $\log(M_{*})$ | isAGN
---|---|---|---|---|---|---
|  | [$10^{-17}$ erg cm-2 s-1] | [$10^{-17}$ erg cm-2 s-1] |  | [$M_{\odot}$] |
73 | 0.3966 $\pm$ 0.0004 | 9.85 ${}^{+1.69}_{-1.46}$ | 4.44 ${}^{+0.98}_{-1.19}$ | N.A. | 9.30 ${}^{+0.28}_{-0.15}$ | True
87 | 0.3801 $\pm$ 0.0003 | 12.34 ${}^{+2.22}_{-2.13}$ | 1.33 ${}^{+1.18}_{-1.33}$ | N.A. | N.A. | True
105 | 0.3829 $\pm$ 0.0002 | 31.60 ${}^{+4.29}_{-3.91}$ | 4.21 ${}^{+2.46}_{-2.16}$ | 8.40 ${}^{+0.14}_{-0.25}$ | 9.40 ${}^{+0.04}_{-0.04}$ | False
106 | 0.3810 $\pm$ 0.0002 | 25.74 ${}^{+3.22}_{-3.12}$ | 9.98 ${}^{+2.39}_{-2.14}$ | 8.67 ${}^{+0.08}_{-0.09}$ | 10.09 ${}^{+0.06}_{-0.09}$ | False
138 | 0.3814 $\pm$ 0.0002 | 42.46 ${}^{+4.89}_{-4.58}$ | 7.87 ${}^{+2.78}_{-3.14}$ | 8.48 ${}^{+0.11}_{-0.17}$ | 9.77 ${}^{+0.11}_{-0.05}$ | False
146 | 0.3913 $\pm$ 0.0001 | 42.91 ${}^{+0.61}_{-1.45}$ | 7.75 ${}^{+0.96}_{-0.87}$ | N.A. | 9.23 ${}^{+0.08}_{-0.09}$ | True
147 | 0.3890 $\pm$ 0.0022 | 8.37 ${}^{+1.87}_{-6.73}$ | 2.20 ${}^{+6.26}_{-1.38}$ | 8.57 ${}^{+0.75}_{-0.28}$ | 8.67 ${}^{+0.64}_{-0.45}$ | False
219 | 0.3984 $\pm$ 0.0004 | 40.25 ${}^{+9.93}_{-8.35}$ | 19.86 ${}^{+6.30}_{-7.32}$ | N.A. | 10.57 ${}^{+0.05}_{-0.10}$ | True
227 | 0.4005 $\pm$ 0.0002 | 11.56 ${}^{+1.87}_{-1.61}$ | 6.09 ${}^{+1.20}_{-1.45}$ | N.A. | 9.42 ${}^{+0.10}_{-0.06}$ | True
263 | 0.3995 $\pm$ 0.0002 | 54.41 ${}^{+5.41}_{-5.56}$ | 13.70 ${}^{+3.32}_{-3.27}$ | N.A. | 9.68 ${}^{+0.04}_{-0.05}$ | True
265 | 0.4006 $\pm$ 0.0002 | 26.58 ${}^{+2.73}_{-2.75}$ | 6.50 ${}^{+1.73}_{-1.86}$ | N.A. | 9.43 ${}^{+0.08}_{-0.14}$ | True
282 | 0.3802 $\pm$ 0.0001 | 12.72 ${}^{+0.78}_{-0.72}$ | 0.76 ${}^{+0.61}_{-0.26}$ | 8.20 ${}^{+0.10}_{-0.11}$ | 8.92 ${}^{+0.31}_{-0.31}$ | False
288 | 0.3911 $\pm$ 0.0001 | 23.85 ${}^{+0.01}_{-1.32}$ | 7.78 ${}^{+1.02}_{-0.67}$ | N.A. | 10.37 ${}^{+0.07}_{-0.08}$ | True
290 | 0.3797 $\pm$ 0.0004 | 8.81 ${}^{+1.46}_{-1.40}$ | 0.44 ${}^{+0.94}_{-0.44}$ | N.A. | 8.61 N.A. | True
291 | 0.3891 $\pm$ 0.0001 | 15.04 ${}^{+0.80}_{-0.67}$ | 6.23 ${}^{+0.69}_{-0.64}$ | N.A. | 9.29 ${}^{+0.23}_{-0.28}$ | True
308 | 0.3998 $\pm$ 0.0002 | 39.78 ${}^{+3.96}_{-3.65}$ | 3.87 ${}^{+1.84}_{-1.96}$ | 8.32 ${}^{+0.12}_{-0.23}$ | N.A. | False
317 | 0.4013 $\pm$ 0.0003 | 7.51 ${}^{+1.44}_{-1.77}$ | 3.49 ${}^{+1.26}_{-1.25}$ | 8.71 ${}^{+0.15}_{-0.17}$ | 9.33 ${}^{+0.27}_{-0.27}$ | False
336 | 0.3795 $\pm$ 0.0001 | 12.70 ${}^{+0.76}_{-0.89}$ | 1.93 ${}^{+0.66}_{-0.75}$ | 8.43 ${}^{+0.09}_{-0.13}$ | 9.89 ${}^{+0.12}_{-0.19}$ | False
338 | 0.3915 $\pm$ 0.0004 | 34.63 ${}^{+6.85}_{-6.71}$ | 11.02 ${}^{+5.33}_{-5.04}$ | 8.62 ${}^{+0.16}_{-0.27}$ | 10.62 ${}^{+0.03}_{-0.03}$ | False
339 | 0.3901 $\pm$ 0.0002 | 11.08 ${}^{+1.16}_{-1.48}$ | 0.29 ${}^{+1.03}_{-0.29}$ | 8.00 ${}^{+0.21}_{-0.36}$ | 9.72 ${}^{+0.04}_{-0.06}$ | False
341 | 0.3938 $\pm$ 0.0003 | 36.31 ${}^{+5.60}_{-5.53}$ | 7.75 ${}^{+3.75}_{-3.67}$ | N.A. | 10.40 ${}^{+0.06}_{-0.08}$ | True
343 | 0.3999 $\pm$ 0.0002 | 22.04 ${}^{+2.97}_{-2.66}$ | 7.86 ${}^{+2.15}_{-1.92}$ | N.A. | 10.40 ${}^{+0.05}_{-0.05}$ | True
345 | 0.3955 $\pm$ 0.0002 | 15.61 ${}^{+2.63}_{-2.72}$ | 5.07 ${}^{+1.98}_{-1.91}$ | 8.62 ${}^{+0.13}_{-0.17}$ | 10.35 ${}^{+0.04}_{-0.04}$ | False
353 | 0.3949 $\pm$ 0.0002 | 8.15 ${}^{+1.28}_{-1.23}$ | 1.79 ${}^{+0.84}_{-0.83}$ | 8.52 ${}^{+0.14}_{-0.25}$ | 9.23 ${}^{+0.23}_{-0.28}$ | False
366 | 0.3996 $\pm$ 0.0001 | 62.84 ${}^{+5.62}_{-5.36}$ | 11.90 ${}^{+2.95}_{-3.51}$ | 8.49 ${}^{+0.07}_{-0.09}$ | 9.74 ${}^{+0.05}_{-0.06}$ | False
384 | 0.3930 $\pm$ 0.0002 | 12.78 ${}^{+0.92}_{-0.96}$ | 2.21 ${}^{+0.70}_{-0.75}$ | N.A. | N.A. | True
405 | 0.3940 $\pm$ 0.0003 | 111.77 ${}^{+19.97}_{-18.56}$ | 74.77 ${}^{+14.71}_{-15.12}$ | 8.80 ${}^{+0.08}_{-0.10}$ | 10.96 ${}^{+0.03}_{-0.03}$ | False
422 | 0.3935 $\pm$ 0.0002 | 68.49 ${}^{+7.88}_{-8.92}$ | 25.64 ${}^{+5.67}_{-5.02}$ | 8.66 ${}^{+0.07}_{-0.08}$ | 10.02 ${}^{+0.04}_{-0.03}$ | False
424 | 0.3908 $\pm$ 0.0002 | 68.03 ${}^{+7.25}_{-7.15}$ | 12.45 ${}^{+3.92}_{-4.59}$ | 8.48 ${}^{+0.10}_{-0.14}$ | 10.22 ${}^{+0.04}_{-0.03}$ | False
433 | 0.4005 $\pm$ 0.0002 | 16.44 ${}^{+1.87}_{-1.65}$ | 1.03 ${}^{+0.73}_{-0.89}$ | 8.21 ${}^{+0.15}_{-0.40}$ | 8.87 ${}^{+0.42}_{-0.70}$ | False
443 | 0.3968 $\pm$ 0.0003 | 10.26 ${}^{+1.72}_{-1.84}$ | 1.72 ${}^{+1.09}_{-1.15}$ | 8.46 ${}^{+0.17}_{-0.48}$ | N.A. | False
451 | 0.3911 $\pm$ 0.0002 | 29.11 ${}^{+4.03}_{-3.86}$ | 3.02 ${}^{+2.03}_{-2.07}$ | 8.34 ${}^{+0.17}_{-0.39}$ | 10.09 ${}^{+0.08}_{-0.09}$ | False
456 | 0.3967 $\pm$ 0.0006 | 55.76 ${}^{+11.60}_{-14.53}$ | 29.93 ${}^{+12.46}_{-7.86}$ | N.A. | 10.70 ${}^{+0.04}_{-0.04}$ | True
457 | 0.3922 $\pm$ 0.0001 | 13.10 ${}^{+0.95}_{-0.92}$ | 11.13 ${}^{+0.92}_{-0.96}$ | N.A. | 10.60 ${}^{+0.03}_{-0.04}$ | True
485 | 0.3935 $\pm$ 0.0001 | 12.62 ${}^{+0.78}_{-0.74}$ | 2.98 ${}^{+0.75}_{-0.64}$ | 8.54 ${}^{+0.06}_{-0.07}$ | 9.08 ${}^{+0.29}_{-0.25}$ | False
501 | 0.3791 $\pm$ 0.0002 | 8.84 ${}^{+0.88}_{-0.80}$ | 3.69 ${}^{+0.67}_{-0.78}$ | 8.68 ${}^{+0.07}_{-0.07}$ | 10.24 ${}^{+0.12}_{-0.16}$ | False
560 | 0.3947 $\pm$ 0.0002 | 30.94 ${}^{+2.85}_{-3.15}$ | 4.50 ${}^{+1.76}_{-2.05}$ | 8.42 ${}^{+0.13}_{-0.21}$ | 9.59 ${}^{+0.05}_{-0.05}$ | False
574 | 0.3931 $\pm$ 0.0002 | 11.39 ${}^{+1.17}_{-1.14}$ | 1.50 ${}^{+0.71}_{-0.80}$ | 8.40 ${}^{+0.14}_{-0.26}$ | 9.20 ${}^{+0.50}_{-0.30}$ | False
582 | 0.3959 $\pm$ 0.0003 | 10.66 ${}^{+1.59}_{-1.53}$ | 2.68 ${}^{+1.07}_{-1.06}$ | 8.56 ${}^{+0.14}_{-0.20}$ | N.A. | False
612 | 0.3956 $\pm$ 0.0004 | 18.24 ${}^{+3.25}_{-2.52}$ | 4.39 ${}^{+1.58}_{-2.40}$ | N.A. | 10.00 ${}^{+0.05}_{-0.06}$ | True
651 | 0.3945 $\pm$ 0.0001 | 27.06 ${}^{+1.00}_{-1.02}$ | 8.48 ${}^{+0.87}_{-0.78}$ | N.A. | 10.42 ${}^{+0.06}_{-0.07}$ | True
657 | 0.3896 $\pm$ 0.0001 | 20.63 ${}^{+0.75}_{-0.79}$ | 6.99 ${}^{+0.71}_{-0.83}$ | 8.63 ${}^{+0.03}_{-0.04}$ | 9.83 ${}^{+0.08}_{-0.10}$ | False
658 | 0.3950 $\pm$ 0.0002 | 11.21 ${}^{+1.14}_{-1.22}$ | 0.36 ${}^{+0.61}_{-0.36}$ | N.A. | 9.53 ${}^{+0.12}_{-0.12}$ | True
664 | 0.3949 $\pm$ 0.0001 | 28.42 ${}^{+1.25}_{-1.13}$ | 8.90 ${}^{+0.99}_{-0.85}$ | N.A. | 10.33 ${}^{+0.07}_{-0.11}$ | True
675 | 0.3943 $\pm$ 0.0002 | 70.70 ${}^{+8.33}_{-8.22}$ | 25.60 ${}^{+5.26}_{-6.03}$ | 8.65 ${}^{+0.07}_{-0.08}$ | 10.38 ${}^{+0.04}_{-0.03}$ | False
677 | 0.3911 $\pm$ 0.0006 | 60.72 ${}^{+16.14}_{-16.58}$ | 21.82 ${}^{+12.43}_{-12.69}$ | N.A. | 10.75 ${}^{+0.08}_{-0.05}$ | True
700 | 0.3819 $\pm$ 0.0002 | 15.86 ${}^{+1.01}_{-1.08}$ | 3.30 ${}^{+0.89}_{-0.85}$ | N.A. | N.A. | True
708 | 0.3965 $\pm$ 0.0001 | 12.40 ${}^{+0.80}_{-0.61}$ | 1.72 ${}^{+0.64}_{-0.78}$ | 8.41 ${}^{+0.09}_{-0.15}$ | 9.72 ${}^{+0.17}_{-0.18}$ | False
714 | 0.3962 $\pm$ 0.0002 | 26.64 ${}^{+3.25}_{-3.29}$ | 8.08 ${}^{+2.20}_{-2.16}$ | 8.60 ${}^{+0.09}_{-0.10}$ | 10.00 ${}^{+0.08}_{-0.10}$ | False
728 | 0.3938 $\pm$ 0.0003 | 17.70 ${}^{+2.52}_{-2.45}$ | 7.60 ${}^{+1.68}_{-1.80}$ | N.A. | 9.87 ${}^{+0.10}_{-0.10}$ | True
758 | 0.3970 $\pm$ 0.0004 | 16.90 ${}^{+3.27}_{-3.24}$ | 9.79 ${}^{+2.35}_{-2.64}$ | N.A. | 10.42 ${}^{+0.08}_{-0.08}$ | True
773 | 0.3952 $\pm$ 0.0002 | 15.95 ${}^{+1.91}_{-1.87}$ | 0.74 ${}^{+0.98}_{-0.56}$ | 8.14 ${}^{+0.15}_{-0.32}$ | 8.94 ${}^{+0.51}_{-0.38}$ | False
775 | 0.3922 $\pm$ 0.0005 | 5.92 ${}^{+1.10}_{-0.95}$ | 0.96 ${}^{+0.58}_{-0.67}$ | 8.45 ${}^{+0.15}_{-0.47}$ | 8.86 ${}^{+0.29}_{-0.44}$ | False
777 | 0.3806 $\pm$ 0.0002 | 43.72 ${}^{+4.53}_{-4.32}$ | 10.48 ${}^{+2.39}_{-2.84}$ | 8.55 ${}^{+0.08}_{-0.10}$ | N.A. | False
783 | 0.4010 $\pm$ 0.0009 | 6.22 ${}^{+2.44}_{-2.59}$ | 1.41 ${}^{+1.64}_{-1.41}$ | 8.53 ${}^{+0.27}_{-0.79}$ | N.A. | False
811 | 0.4042 $\pm$ 0.0005 | 7.33 ${}^{+1.13}_{-1.23}$ | 0.71 ${}^{+0.72}_{-0.71}$ | N.A. | 10.45 ${}^{+0.08}_{-0.10}$ | True
826 | 0.3964 $\pm$ 0.0003 | 39.67 ${}^{+4.01}_{-4.27}$ | 9.71 ${}^{+2.72}_{-2.56}$ | N.A. | 9.57 ${}^{+0.24}_{-0.05}$ | True
847 | 0.3962 $\pm$ 0.0001 | 31.59 ${}^{+0.55}_{-0.56}$ | 2.41 ${}^{+0.56}_{-0.74}$ | 8.26 ${}^{+0.07}_{-0.09}$ | 9.41 ${}^{+0.07}_{-0.09}$ | False
850 | 0.3922 $\pm$ 0.0002 | 89.29 ${}^{+8.62}_{-9.25}$ | 24.36 ${}^{+5.94}_{-5.71}$ | N.A. | 10.48 ${}^{+0.05}_{-0.05}$ | True
869 | 0.3949 $\pm$ 0.0004 | 17.21 ${}^{+3.35}_{-2.34}$ | 5.64 ${}^{+1.72}_{-2.30}$ | N.A. | 9.95 ${}^{+0.11}_{-0.10}$ | True
872 | 0.3921 $\pm$ 0.0004 | 7.13 ${}^{+1.86}_{-1.87}$ | 4.34 ${}^{+1.73}_{-1.54}$ | 8.78 ${}^{+0.16}_{-0.18}$ | 10.41 ${}^{+0.07}_{-0.08}$ | False
875 | 0.3898 $\pm$ 0.0002 | 33.48 ${}^{+3.32}_{-3.53}$ | 3.54 ${}^{+1.87}_{-2.10}$ | 8.34 ${}^{+0.13}_{-0.29}$ | 9.89 ${}^{+0.08}_{-0.12}$ | False
882 | 0.4045 $\pm$ 0.0012 | 6.01 ${}^{+3.04}_{-3.39}$ | 2.54 ${}^{+2.58}_{-2.30}$ | 8.69 ${}^{+0.35}_{-0.74}$ | 10.24 ${}^{+0.15}_{-0.07}$ | False
889 | 0.3959 $\pm$ 0.0003 | 5.79 ${}^{+1.36}_{-1.34}$ | 2.79 ${}^{+1.19}_{-1.15}$ | N.A. | 10.27 ${}^{+0.12}_{-0.18}$ | True
919 | 0.3949 $\pm$ 0.0001 | 15.90 ${}^{+0.80}_{-0.82}$ | 1.73 ${}^{+0.64}_{-0.66}$ | 8.35 ${}^{+0.10}_{-0.15}$ | 10.10 ${}^{+0.09}_{-0.10}$ | False
927 | 0.3971 $\pm$ 0.0002 | 40.35 ${}^{+4.60}_{-4.48}$ | 11.91 ${}^{+2.78}_{-3.27}$ | N.A. | 10.08 ${}^{+0.05}_{-0.08}$ | True
929 | 0.3806 $\pm$ 0.0004 | 49.44 ${}^{+9.89}_{-8.84}$ | 20.88 ${}^{+6.70}_{-7.35}$ | 8.69 ${}^{+0.13}_{-0.18}$ | N.A. | False
938 | 0.3991 $\pm$ 0.0004 | 8.50 ${}^{+1.14}_{-1.21}$ | 1.73 ${}^{+0.62}_{-0.71}$ | 8.51 ${}^{+0.13}_{-0.20}$ | N.A. | False
966 | 0.3966 $\pm$ 0.0002 | 52.99 ${}^{+6.31}_{-5.55}$ | 24.49 ${}^{+3.54}_{-4.54}$ | 8.71 ${}^{+0.06}_{-0.07}$ | 10.29 ${}^{+0.05}_{-0.07}$ | False
977 | 0.3915 $\pm$ 0.0003 | 8.55 ${}^{+1.55}_{-1.61}$ | 3.51 ${}^{+1.24}_{-1.32}$ | N.A. | 9.84 ${}^{+0.05}_{-0.06}$ | True
998 | 0.3914 $\pm$ 0.0001 | 55.39 ${}^{+1.27}_{-1.20}$ | 10.26 ${}^{+1.14}_{-1.02}$ | N.A. | 10.22 ${}^{+0.08}_{-0.08}$ | True
1015 | 0.3936 $\pm$ 0.0001 | 83.94 ${}^{+0.01}_{-1.48}$ | 16.44 ${}^{+0.84}_{-0.64}$ | 8.50 ${}^{+0.02}_{-0.01}$ | 10.02 ${}^{+0.08}_{-0.07}$ | False
1039 | 0.3956 $\pm$ 0.0001 | 50.48 ${}^{+1.37}_{-1.32}$ | 23.94 ${}^{+0.30}_{-1.38}$ | N.A. | 10.82 ${}^{+0.06}_{-0.06}$ | True
1057 | 0.3933 $\pm$ 0.0001 | 20.64 ${}^{+0.82}_{-0.72}$ | 5.54 ${}^{+0.63}_{-0.60}$ | 8.57 ${}^{+0.03}_{-0.03}$ | 9.96 ${}^{+0.07}_{-0.11}$ | False
1097 | 0.3946 $\pm$ 0.0001 | 12.84 ${}^{+0.50}_{-0.56}$ | 2.40 ${}^{+0.48}_{-0.37}$ | 8.48 ${}^{+0.05}_{-0.05}$ | 9.44 ${}^{+0.32}_{-0.27}$ | False
1103 | 0.3959 $\pm$ 0.0001 | 11.65 ${}^{+0.72}_{-0.71}$ | 0.83 ${}^{+0.50}_{-0.50}$ | 8.25 ${}^{+0.11}_{-0.22}$ | 8.60 ${}^{+0.71}_{-0.39}$ | False
1125 | 0.3917 $\pm$ 0.0001 | 9.36 ${}^{+0.76}_{-0.69}$ | 2.61 ${}^{+0.58}_{-0.55}$ | N.A. | 9.16 ${}^{+0.15}_{-0.17}$ | True
1130 | 0.3918 $\pm$ 0.0001 | 10.76 ${}^{+0.60}_{-0.54}$ | 3.32 ${}^{+0.44}_{-0.38}$ | 8.61 ${}^{+0.04}_{-0.04}$ | 9.47 ${}^{+0.11}_{-0.12}$ | False
1165 | 0.3950 $\pm$ 0.0001 | 27.32 ${}^{+1.14}_{-1.13}$ | 8.58 ${}^{+0.91}_{-0.95}$ | 8.61 ${}^{+0.04}_{-0.04}$ | 10.56 ${}^{+0.04}_{-0.04}$ | False
1173 | 0.3901 $\pm$ 0.0001 | 39.05 ${}^{+0.69}_{-1.09}$ | 1.65 ${}^{+1.02}_{-0.54}$ | 8.12 ${}^{+0.08}_{-0.10}$ | 9.02 ${}^{+0.54}_{-0.19}$ | False
1204 | 0.3950 $\pm$ 0.0001 | 13.21 ${}^{+0.72}_{-0.81}$ | 1.73 ${}^{+0.65}_{-0.71}$ | 8.40 ${}^{+0.10}_{-0.15}$ | 8.61 ${}^{+0.52}_{-0.36}$ | False
1219 | 0.3822 $\pm$ 0.0001 | 33.56 ${}^{+0.01}_{-1.02}$ | 9.30 ${}^{+0.92}_{-0.96}$ | N.A. | 10.40 ${}^{+0.04}_{-0.04}$ | True
1220 | 0.3995 $\pm$ 0.0001 | 27.69 ${}^{+1.21}_{-1.13}$ | 10.76 ${}^{+0.80}_{-0.91}$ | 8.67 ${}^{+0.03}_{-0.03}$ | 9.92 ${}^{+0.07}_{-0.09}$ | False
41000 | 0.3809 $\pm$ 0.0002 | 19.24 ${}^{+1.53}_{-1.30}$ | 2.06 ${}^{+1.00}_{-1.12}$ | N.A. | N.A. | True