# The Chemodynamical Nature of the Triangulum-Andromeda Overdensity

Yuri Abuchaim Universidade de São Paulo, Instituto de Astronomia, Geofísica e
Ciências Atmosféricas, Departamento de Astronomia, SP 05508-090, São Paulo,
Brazil  Hélio D. Perottoni Universidade de São Paulo, Instituto de Astronomia,
Geofísica e Ciências Atmosféricas, Departamento de Astronomia, SP 05508-090,
São Paulo, Brazil  Institut de Ciències del Cosmos, Universitat de Barcelona
(IEEC-UB), Martí i Franquès 1, E-08028 Barcelona, Spain  Silvia Rossi
Universidade de São Paulo, Instituto de Astronomia, Geofísica e Ciências
Atmosféricas, Departamento de Astronomia, SP 05508-090, São Paulo, Brazil
Guilherme Limberg Universidade de São Paulo, Instituto de Astronomia,
Geofísica e Ciências Atmosféricas, Departamento de Astronomia, SP 05508-090,
São Paulo, Brazil  Department of Astronomy & Astrophysics, University of
Chicago, 5640 S. Ellis Ave., Chicago, IL 60637, USA  Kavli Institute for
Cosmological Physics, University of Chicago, Chicago, IL 60637, USA  Angeles
Pérez-Villegas Instituto de Astronomía, Universidad Nacional Autónoma de
México, Apartado Postal 106, C. P. 22800, Ensenada, B. C., Mexico  Rafael M.
Santucci Universidade Federal de Goiás, Instituto de Estudos Socioambientais,
Planetário, Goiânia, GO 74055-140, Brazil  Universidade Federal de Goiás,
Instituto de Física, Goiânia, GO 74001-970, Brazil  Vinicius M. Placco NSF’s
NOIRLab, 950 N. Cherry Ave., Tucson, AZ 85719, USA  João V. Sales-Silva
Observatório Nacional/MCTIC, R. Gen. José Cristino, 77, 20921-400, Rio de
Janeiro, Brazil  Friedrich Anders Institut de Ciències del Cosmos, Universitat
de Barcelona (IEEC-UB), Martí i Franquès 1, E-08028 Barcelona, Spain  Helio J.
Rocha-Pinto Universidade Federal do Rio de Janeiro, Observatório do Valongo,
Lad. Pedro Antônio 43, 20080-090, Rio de Janeiro, Brazil

###### Abstract

We present a chemodynamical study of the Triangulum-Andromeda overdensity
(TriAnd) employing a sample of 31 candidate stars observed with the GRACES
high-resolution ($R$=40,000) spectrograph at the Gemini North (8.1 m)
telescope. TriAnd is a stellar substructure found toward the outer disk of the
Milky Way, located at $R_{\rm GC}\sim 18$ kpc from the Sun, toward Galactic
latitude $b\sim 25\degree$. Most stars in our sample have dynamical properties
compatible with a disk stellar population. In addition, by applying an
eccentricity cut, we are able to detect a stellar contamination that seems to
be consistent with an accreted population. In chemical abundance space, the
majority of our TriAnd candidates are similar to the outer thin-disk
population, suggesting that the overdensity has an in situ origin. Finally,
the found accreted halo interlopers spatially overlapping with TriAnd should
explain the historical discussion of the overdensity’s nature due to its
complex chemical patterns.

Chemical abundances (224), Galaxy kinematics (602), Milky Way disk(1050),
Milky Way stellar halo(1060), High resolution spectroscopy(2096), Galactic
archaeology(2178)

††facilities: Gemini:North (GRACES)††software:  OPERA (Martioli et al., 2012),
IRAF (Tody, 1986), MOOG (Sneden, 1973), qoyllur-quipu (Ramírez et al., 2014),
matplotlib (Hunter, 2007), numpy (van der Walt et al., 2011), pandas
(McKinney, 2010), scipy (Virtanen et al., 2020), StarHorse (Queiroz et al.,
2018).

##  1 Introduction

The Milky Way (MW) is teeming with substructures such as stellar streams and
overdensities whose origins are still under debate (Belokurov, 2013; Helmi,
2020). Among the various scenarios for the formation of our Galaxy (Eggen et
al., 1962; Searle & Zinn, 1978; Tinsley, 1980), the bottom-up scenario is the
most accepted one, taking into account the formation of these substructures.
First suggested by Searle & Zinn (1978), it is supported by theoretical
predictions from the $\Lambda$ Cold Dark Matter ($\Lambda$CDM) cosmological
paradigm (Spergel et al., 2007) and numerical simulations (Somerville & Davé,
2015).

In this hierarchical scenario, ancient mergers of dwarf galaxies with a still-
young MW likely caused the formation of substructures from the tidal
disruption of these satellites (Johnston, 1998; Bullock & Johnston, 2005;
Johnston et al., 2008; Cooper et al., 2010; Helmi et al., 2011; Gómez et al.,
2013). These events, however, did not only occur in ancient times; there are
also ongoing accretions like the Sagittarius dwarf spheroidal (Ibata et al.,
1994, 1995). Additionally, a profusion of other disrupted dwarf galaxies that
populate the Galactic halo can be seen in the form of stellar streams (Helmi &
White, 1999; Ivezić et al., 2000; Yanny et al., 2000; Belokurov et al., 2006,
2007a; Newberg et al., 2009; Grillmair, 2011; Bernard et al., 2016; Grillmair
& Carlin, 2016; Malhan et al., 2018; Mateu et al., 2018; Shipp et al., 2018;
Ibata et al., 2019, 2021; Li et al., 2022).

Stellar overdensities are among the different substructures permeating the
Galactic halo, e.g. Virgo (Newberg et al., 2002; Jurić et al., 2008), Pisces
(Sesar et al., 2007; Watkins et al., 2009), Eridanus-Phoenix (Li et al., 2016)
and Hercules-Aquila (Belokurov et al., 2007b). Ancient mergers seem to be the
main culprits for the formation of these substructures (Belokurov et al.,
2019; Donlon et al., 2019; Chang et al., 2020; Balbinot & Helmi, 2021; Naidu
et al., 2021; Chandra et al., 2022; Perottoni et al., 2022; Wang et al.,
2022), but they can also be found close to the Galactic plane with a
potentially different origin.

Historically, an in situ origin for the stellar overdensities close to the
Galactic plane has been suggested (Rocha-Pinto et al., 2003; Momany et al.,
2004, 2006; López-Corredoira et al., 2007; Gómez et al., 2013; Dierickx et
al., 2014; Price-Whelan et al., 2015; Xu et al., 2015; Li et al., 2017, 2021;
Deason et al., 2018; Sheffield et al., 2018; Youakim et al., 2020; Carballo-
Bello et al., 2021; Ramos et al., 2021; Laporte et al., 2022). Those
structures are proposed to be formed as a result of interactions of dwarf
galaxies with the MW throughout its history, which can dynamically heat the
disk (Yanny & Newberg, 2016; Figueras, 2017; Schönrich & Dehnen, 2018),
supported by simulations (Laporte et al., 2018, 2019). Other authors, by
comparing both the chemical and dynamical patterns from those stellar
overdensities with satellite galaxies, conclude that their most probable
origin is extragalactic (Martin et al., 2004; Bellazzini et al., 2006; Chou et
al., 2011; Meisner et al., 2012; Deason et al., 2014; Morganson et al., 2016;
Guglielmo et al., 2018).

One of the stellar overdensities close to the Galactic plane is the
Triangulum-Andromeda overdensity (TriAnd), first identified by Rocha-Pinto et
al. (2004) using a photometric selection of M-giant star candidates from the
Two Micron All Sky Survey (2MASS; Skrutskie et al. 2006) between
$-100\degree<l<-150\degree$ and $-40\degree<b<-20\degree$ as a clumpy
cloudlike structure. Majewski et al. (2004) also observed TriAnd through a
deep photometric survey of M31 fields. Both works estimated a distance of
${\sim}$16–25 kpc from the Sun.

Since its discovery, TriAnd has been studied with different techniques to
better understand its characteristics and plausible origin. Martin et al.
(2014), utilizing MegaCam photometric data (Martin et al., 2007) from the Pan-
Andromeda Archaeological Survey (McConnachie et al., 2009), identified the
main sequence of TriAnd stars and other structures also in the same region. Xu
et al. (2015), with photometric data from the Sloan Digital Sky Survey (SDSS;
Stoughton et al. 2002), argued that TriAnd is, apparently, a concentric
structure located 21 kpc from the Galactic center. Perottoni et al. (2018),
also with SDSS data, observed fluctuations in the structure’s density being
limited at $b=-45\degree$, in agreement with previous kinematical
determinations (Sheffield et al., 2014).

Among the different techniques employed to better understand TriAnd’s
features, spectroscopy has been the most prominent to settle the debate about
the nature of the overdensity. In the first high-resolution ($R=$32,000 and
35,000) spectroscopic study of TriAnd, Chou et al. (2011) analyzed the spectra
of six candidate stars selected from Rocha-Pinto et al. (2004). Their results
indicated that TriAnd would have higher metallicity
($\rm[Fe/H]=-0.64\pm{0.08}$ dex) than previously estimated
($\rm[Fe/H]\sim-1.2$ dex) by Rocha-Pinto et al. (2004) using the Ca infrared
triplet spectral indices from lower-resolution ($R\sim 3300$) spectra.

Recently, with high-resolution ($R$=36,000 and 47,000) spectroscopy, Bergemann
et al. (2018) derived abundance patterns for O, Na, Mg, Ti, Ba, and Eu
considering TriAnd candidate star samples with mean $\rm[Fe/H]=-0.59\pm 0.12$
dex. Their main result was that these stars have an in situ disk origin and
suggested that the overdensity was formed by tidal interactions of the disk
with passing or merging dwarf galaxies. Shortly after this, using data from
the Apache Point Observatory Galactic Evolution Experiment (Majewski et al.,
2017) Fourteenth Data Release (APOGEE DR14; Abolfathi et al. 2018), Hayes et
al. (2018a) observed that the chemical patterns obtained from the TriAnd stars
(Mg, (C+N), K, Ca, Mn, and Ni) appear to be consistent with a metal-poor
extrapolation of the outer disk’s trend to a larger radius.

Sales Silva et al. (2019, 2020) obtained a sample of 13 TriAnd candidate stars
and studied a larger set of elements, including $\alpha$ (Mg, Ca, Si, and Ti),
iron-peak (Fe, Ni, and Cr), odd-$Z$ (Al and Na), and neutron-capture elements
(Ba and Eu). The authors concluded that the overdensity structure is composed
by stars with a unique chemical pattern not corresponding to stars present in
either the local Galactic disk or dwarf spheroidal galaxies.

The complex chemical profile observed for the TriAnd stars, combined with the
low number of candidates observed with high-resolution spectroscopy, and the
scarcity of studies with field stars in the outer disk region are the most
relevant limitations to establish the formation history of this overdensity.
In this work, we intend to overcome these challenges with a chemodynamical
investigation of a larger and updated sample of 31 TriAnd candidate stars, 13
of which are reanalyzed from the sample presented by Sales Silva et al. (2019,
2020), plus 18 new stars observed over the last 3 yr with the same
instrumentation as the previous works.

This paper is outlined as follows. Section 2 describes the sample of TriAnd
candidate stars studied in this work. Section 3 describes the methodologies
and techniques employed to derive orbital and atmospheric parameters, radial
velocities, and chemical abundances. The analyses and discussions of the
dynamical and chemical properties of our TriAnd candidate stars are described
in Section 4. Finally, Section 5 presents a summary with concluding remarks.

##  2 Data

Figure 1: Spatial projection in Galactic coordinates of TriAnd candidate stars
employed throughout this work, represented by blue circles. The yellow lines
identify the overdensity footprint ($-90\arcdeg<l<-160\arcdeg$ and
$-10\arcdeg>b>-45\arcdeg$). The stars are superimposed on the Schlegel et al.
(1998a) interstellar extinction map, while the color bar shows the reddening.

###  2.1 Target Selection and Observations

We selected an M-giant star sample using near-infrared photometric data from
the 2MASS catalog (Cutri et al., 2003), which was dereddened using the
extinction map from Schlegel et al. (1998b) and the extinction laws from
Majewski et al. (2003). We adopted the same photometric selection criteria
provided by Sales Silva et al. (2019), which efficiently separate M giants
from contaminant disk M dwarf stars. This method was successfully used to
select distant M giants that led to the discovery, for instance, of
Sagittarius tidal tails (Majewski et al., 2003), the mapping of the Monoceros
overdensity (Rocha-Pinto et al., 2003), and the discovery of TriAnd (Rocha-
Pinto et al., 2004), as well as spectroscopic follow-up by Sheffield et al.
(2014), Bergemann et al. (2018), Hayes et al. (2018a), and Sales Silva et al.
(2019). The TriAnd candidates were further restricted to the region covering
$-90\arcdeg<l<-160\arcdeg$ and $-10\arcdeg>b>-45\arcdeg$, which is associated
with TriAnd (Rocha-Pinto et al. 2004; Perottoni et al. 2019; Laporte et al.
2022).

As a second criterion, we used data from Gaia DR2 (Gaia Collaboration, 2018)
to select reliable candidates of TriAnd in proper-motion space. As identified
by Sales Silva et al. (2019), some sparse stars found in the TriAnd region
with $\mu_{\alpha}^{*}=\mu_{\alpha}\cos\delta>0.5\ {\rm mas}\ {\rm yr}^{-1}$
have characteristics similar to those from the halo population. In order to
avoid contamination by halo interlopers in the region associated with the
overdensity, we selected our targets inside an ellipsoid delimited by a
$2\sigma$ range around the centroid $(\mu_{\alpha}^{*},\mu_{\delta})$ =
$(-0.11,-0.57)$ mas yr-1 that was estimated with the TriAnd samples of Chou et
al. (2011), Sheffield et al. (2014) (which contains stars from Bergemann et
al. 2018 and Hayes et al. 2018a samples), and the stars classified as TriAnd
members from Sales Silva et al. (2019). In Figure 1, we present the sky
projection in Galactic coordinates of TriAnd candidate stars within the region
associated with the overdensity (Rocha-Pinto et al., 2004; Perottoni et al.,
2019; Laporte et al., 2022).

Our sample consists of 13 TriAnd candidates presented in Sales Silva et al.
(2019) and 18 new ones, totaling 31 stars. To ensure homogeneity in the
observations, we used the same instruments as in Sales Silva et al. (2019).
Namely, we observed the 18 TriAnd candidates with the Gemini Remote Access to
CFHT ESPaDOnS Spectrograph (GRACES; Chene et al. 2014), which is connected to
the 8.1 m Gemini North telescope on Maunakea in Hawai’i (USA).

All targets were observed in queue mode (GN-2019B-Q-211, GN-2020B-Q-112,
GN-2020B-Q-211; PI: Perottoni) in two-fiber (object$+$sky) configuration with
resolution ($R\sim$40,000) in the optical region ($4000<\lambda/$Å $<10000$).
The typical signal-to-noise ratio is $S/N\sim 50{\rm-}70$ pixel-1 at 6000 Å.
The $S/N$ individual values are listed in Table 1 with the respective target
information and exposure time.

Table 1: TriAnd star candidates employed in This work observed with
Gemini/GRACES.

2MASS ID | R.A. (deg) | Decl. (deg) |  $l$ (deg) |  $b$ (deg) |  $J$ (mag) |  $H$ (mag) |  $K$ (mag) | $S/N$ | Exp. (s)
---|---|---|---|---|---|---|---|---|---
23495808+3445569 | 357.4920 | 34.7658 | 108.8627 | -26.4210 | 11.74 | 10.90 | 10.73 | 56.02 | 880
00534976+4626089 | 13.4573 | 46.4358 | 123.3615 | -16.4339 | 11.91 | 11.03 | 10.87 | 57.52 | 1250
00594094+4614332 | 14.9206 | 46.2426 | 124.4193 | -16.6054 | 12.20 | 11.38 | 11.18 | 61.95 | 1270
… | … | … | … | … | … | … | … | … | …

  * •

Only a portion of this table is shown here to demonstrate its form and
content. A machine-readable version of the full table is available.

##  3 Methodology

###  3.1 Orbital Parameters

The spectrophotoastrometric heliocentric distances for our sample were
estimated with the Bayesian isochrone-fitting code StarHorse (Queiroz et al.,
2018, 2020) based on our derived atmospheric parameters. StarHorse did not
present a solution for the distance estimation of two stars from our sample.
These stars were removed from our analysis in Section 4.1.

We calculated the orbits of our star sample with the publicly available Python
library AGAMA (Vasiliev, 2019) for 5 Gyr forward. The Galactic potential model
employed is described in McMillan (2017). We adopted values for the solar
Galactocentric distance $R_{\odot}=8.2$ (Bland-Hawthorn & Gerhard, 2016), the
local circular velocity of $v_{c}=232.8$ km s-1(McMillan, 2017), and the solar
motion with respect to the local standard of rest
$(U_{\odot},V_{\odot},W_{\odot})=(11.10,12.24,7.25)$ km s-1(Schönrich et al.,
2010).

For each star, we performed 1000 Monte Carlo realizations of the orbit
according to Gaussian distributions of its uncertainties in distance, proper
motion, and radial velocity. The medians of the resulting distributions of the
dynamical parameters of interest were taken as our nominal values with 16th
and 84th percentiles as associated uncertainties.

###  3.2 Spectroscopic Analysis

####  3.2.1 Data reduction

All of the data were reduced with the OPERA pipeline (Martioli et al., 2012),
which includes bias subtraction, flat-field correction, and wavelength
calibration. The IRAF (Tody, 1986) package was employed for the spectral
normalization. Radial velocities were obtained by cross-correlation against
synthetic spectra from Munari et al. (2005) using IRAF’s fxcor task.

####  3.2.2 Atmospheric parameters

To calculate the atmospheric parameters and chemical abundances for our TriAnd
candidate stars, we employed the spectrum synthesis code
MOOG111http://www.as.utexas.edu/~chris/moog.html (Sneden, 1973; Sobeck et al.,
2011) and the $\mathtt{q^{2}}$ (qoyllur-
quipu222https://github.com/astroChasqui/q2) Python package (Ramírez et al.,
2014). The radial velocities and the results of the estimated atmospheric
parameters can be found in Table 2.

Table 2: Derived Atmospheric Parameters and Radial Velocities for TriAnd star
candidates employed in This work.

2MASS ID |  $T_{\rm eff}$ (K) |  $\sigma_{T_{\rm eff}}$ (K) | $\log g$ | $\sigma_{\log g}$ | vt (km s-1) |  $\sigma_{vt}$ (km s-1) | RV (km s-1) |  $\sigma_{\rm RV}$ (km s-1)
---|---|---|---|---|---|---|---|---
23495808+3445569 | 3931 | 46 | 1.30 | 0.22 | 1.94 | 0.10 | -21.76 | 0.73
00534976+4626089 | 3891 | 73 | 1.08 | 0.19 | 1.77 | 0.11 | -211.68 | 0.62
00594094+4614332 | 4044 | 29 | 0.34 | 0.17 | 1.80 | 0.08 | -131.08 | 0.75
… | … | … | … | … | … | … | … | …

  * •

Only a portion of this table is shown here to demonstrate its form and
content. A machine-readable version of the full table is available.

The atmospheric parameters, $v_{t}$, $\log g$, and $T_{\rm eff}$, where
$v_{t}$ is the microturbulent velocity, $\log g$ is the logarithm of the
surface gravity, and $T_{\rm eff}$ is the effective temperature, along with
the metallicity, were obtained from the equivalent widths of the Fe lines. The
$T_{\rm eff}$ is calculated from the excitation equilibrium and $\log g$ from
the ionization equilibrium between the Fe I and Fe II lines. The
microturbulent velocity is obtained from the independence between the Fe I
abundances and reduced equivalent width. Finally, [Fe/H] is derived from the
ionization equilibrium obtained under the local thermodynamic equilibrium
(LTE) atmosphere model.

In Figure 2, we show the respective $\log g$ and $T_{\rm eff}$ values for each
star in our sample with overlaid isochrones from the Darthmouth Stellar
Evolution Database (Dotter et al., 2008). Our obtained atmospheric parameters
show that the criteria presented in Section 2 correctly selected M-giant
stars. It is worth noting that one common obstacle when working with
low-$T_{\rm eff}$ stars in spectroscopy, like M giants, is the presence of
molecular bands that can affect the chemical abundance calculations. However,
the M-giant stars in our sample are on the upper $T_{\rm eff}$ limit for
M-type stars, almost on K-type classification, and the presence of molecular
bands is not strong enough to affect the precision of our abundance analyses.

Figure 2: Stellar parameters $\log g$ and $T_{\rm eff}$ obtained for the
TriAnd candidate stars analyzed in this work color-coded by metallicity. The
error bars indicate the corresponding uncertainties. Field stars from the
APOGEE DR17 database are shown as the gray-scale background. Isochrones with 8
Gyr, $[\alpha/Fe]=0$ and varying metallicities from the Darthmouth Stellar
Evolution Database are overlaid in red.

####  3.2.3 Chemical abundances

We determined the chemical abundances of 11 elements for our stars: Na, Mg,
Al, Si, Ca, Ti, Cr, Fe, Ni, Ba, and Eu. We employed the line list presented in
Sales Silva et al. (2019), with additional lines from the Southern Stellar
Stream Spectroscopic Survey (Ji et al., 2020) and GALactic Archaeology with
HERMES (GALAH; Buder et al. 2021) survey, due to the low number of nonblended
absorption lines in Mg, Si, Ca, and Na. The line list with the respective
measured equivalent widths can be found in Table 3.

Table 3: Equivalent widths for TriAnd star candidates employed in This work.

|  |  |  | 2MASS ID
---|---|---|---|---
Wavelength (Å) | Element | EP (eV) | $\log gf$ | 23495808+3445569 | 00534976+4626089 | 00594094+4614332 | …
5682.63 | Na I | 2.1 | $-0.7$ | 127.2 | 149.1 | - | …
5688.2 | Na I | 2.1 | $-0.4$ | 133.5 | 149 | - | …
6154.22 | Na I | 2.1 | $-1.51$ | 54.6 | 91.4 | 71.1 | …
… | … | … | … | … | … | … | …

  * •

EP - excitation potential; $\log gf$ \- transition probability.

  * •

Only a portion of this table is shown here to demonstrate its form and
content. A machine-readable version of the full table is available.

The equivalent widths of the absorption lines in the analyzed spectra were
measured with IRAF’s splot task corresponding to each element measured star-
by-star on a line-by-line basis. These equivalent widths were then utilized to
derive the chemical abundances with MOOG wrapped by the $\mathtt{q^{2}}$ code.
As the resulting abundances are given in absolute values, we employed Grevesse
et al. (2007) solar values to calculate the corresponding abundances in
reference to the Sun. All of the calculations were done assuming LTE with
ODFNEW (Castelli & Kurucz, 2003) atmosphere models.

Due to the hyperfine structure and contribution of different isotopes for the
neutron-capture elements Ba (lines 5853, 6141, and 6496 Å) and Eu (line 6645
Å), we employed the spectral synthesis method for these elements instead, also
with MOOG (Sneden et al., 2008). The results of the derived abundances can be
found in Table 4.

Table 4: Derived Abundances for TriAnd star candidates employed in This work.

2MASS ID | [Fe/H]LTE | [Fe/H]NLTE | $\sigma_{{[}Fe/H{]}}$ | [Na/H]LTE | $\sigma_{{[}Na/H{]}}$ | [Mg/H]LTE | [Mg/H]NLTE | $\sigma_{{[}Mg/H{]}}$ | …
---|---|---|---|---|---|---|---|---|---
23495808+3445569 | $-0.56$ | $-0.55$ | 0.08 | $-0.93$ | 0.08 | $-0.57$ | $-0.57$ | 0.06 | …
00534976+4626089 | $-0.39$ | $-0.38$ | 0.10 | $-0.53$ | 0.10 | $-0.47$ | $-0.47$ | 0.08 | …
00594094+4614332 | $-0.72$ | $-0.70$ | 0.05 | $-0.55$ | 0.04 | $-0.59$ | $-0.56$ | 0.09 | …
… | … | … | … | … | … | … | … | … | …

  * •

Only a portion of this table is shown here to demonstrate its form and
content. A machine-readable version of the full table is available.

As discussed in Ramírez et al. (2014), the errors related to the stellar
parameters calculated by the $\mathtt{q^{2}}$ code consider the relationship
between the parameters fulfilling the equilibrium conditions as described by
Epstein et al. (2010) and Bensby et al. (2014). For $[Fe/H]$, the formal error
is computed by propagating the errors from the other atmospheric parameters by
adding them in quadrature and including the standard error of the mean line-
to-line [Fe/H] abundance. For the other chemical abundances, the errors are
calculated taking into account both the estimated observed errors, given by
the standard deviation from the mean abundance calculated for each set of
measured lines, and the error introduced by each atmospheric parameter. The
radial velocity errors employing IRAF’s fxcor task are computed based on the
fitted peak height and the antisymmetric noise, as described in Tonry & Davis
(1979).

The radial velocities and metallicity ($[Fe/H]$) calculated for our sample
have a typical uncertainty of $\pm$0.91 km s-1and $\pm$0.11 dex, respectively.
The total error budget for the atmospheric parameters was derived by adding in
quadrature the statistical errors calculated by the $\mathtt{q^{2}}$ code with
adopted systematic errors of $\Delta T_{\rm eff}=200$ K and $\Delta\log
g=0.2$. The average uncertainty for each atmospheric parameter is as follows:
$T_{\rm eff}$, $\pm$216 K; $\log g$, $\pm$0.39 dex; and $vt$, $\pm$0.19 km
s-1.

The derived chemical abundances for our sample with the equivalent-width
method have a typical uncertainty of $\pm$0.14 dex. The average uncertainty
for each element is as follows: Na I, $\pm$0.16 dex; Mg I, $\pm$0.11 dex; Al
I, $\pm$0.10 dex; Si I, $\pm$0.15 dex; Ca I, $\pm$0.17 dex; Ti I, $\pm$0.18
dex; Cr I, $\pm$0.17 dex; Ni I, $\pm$0.11 dex.

To account for possible departures from LTE for our derived abundances, we
performed NLTE corrections for our sample using spectral models from
https://nlte.mpia.de. Based on the studies from Mashonkina et al. (2007),
Bergemann & Cescutti (2010), and Bergemann et al. (2012, 2013, 2017), we
applied NLTE corrections for Fe, Mg, Ca, Cr, and Si, correcting our derived
LTE abundances line by line. We explored NLTE corrections for Ti, but, as
observed by Bergemann et al. (2018), NLTE models for Ti do not give consistent
solutions with one-dimensional hydrostatic models (Bergemann, 2011). The
average NLTE departure for each element is as follows: Mg I, $-0.01$ dex; Si
I, $-0.02$ dex; Ca I, $0.03$ dex; Cr I, $0.06$ dex; Fe I, $0.02$ dex. The
corresponding NLTE-corrected value of each element described can be found in
Table 4.

##  4 Discussions

###  4.1 Orbital Parameters

The history of spectroscopic studies of TriAnd has been controversial since
its discovery, with completely opposite results appearing in the literature;
Chou et al. (2011) originally argued for an extragalactic origin for the
overdensity, but Bergemann et al. (2018) and Hayes et al. (2018a) suggested an
in situ nature. With the help of the Gaia mission (Gaia Collaboration et al.,
2016), we can now complement the spectroscopic results with the
kinematic/dynamical counterpart, allowing us to better understand the
complexity of this overdensity.

The orbital parameters of the stars studied in this work, as well as their
spatial projections and velocity vectors, are shown in Figure 3. In panel (a),
we present the orbital eccentricity vs. inclination333inclination =
$\arccos{(L_{z}/L)}$, where $L$ is the total angular momentum. We notice that
most of our stars exhibit low eccentricity, which is characteristic of the
Galactic disk(s) (Freeman & Bland-Hawthorn, 2002; van der Kruit & Freeman,
2011; Bland-Hawthorn & Gerhard, 2016). We classify those stars as
representative of TriAnd, which will be represented in blue for the remainder
of this paper. We also included, as gray circles, stars from the APOGEE DR17
database (Abdurro’uf et al., 2022) for comparison but cleaned of globular
cluster and dwarf galaxy stars following Limberg et al. (2022).

A contamination of high-eccentricity stars in our sample was also detected.
Stars on highly eccentric orbits are typically associated with either the in
situ (Di Matteo et al., 2019; Gallart et al., 2019; Belokurov et al., 2020;
Bonaca et al., 2020) or accreted (Koppelman et al., 2018; Mackereth et al.,
2019; Naidu et al., 2020; Limberg et al., 2021a; Myeong et al., 2022) halo. As
will be discussed in Section 4.3, these probable halo interlopers also have
chemical abundance patterns typical of halo populations (Nissen & Schuster,
2010, 2011; Hayes et al., 2018b).

In order to select the above-described high-eccentricity contamination, we
applied a cut in eccentricity to our sample ($e>0.4$), represented in red
throughout this work. This selection classified six stars from our sample (red
circles) as possible contamination of ex situ stars at the same distance and
location as TriAnd.

Figure 3(b), total orbital energy ($E$) versus the $z$-component of the
angular momentum ($L_{z}$), often employed to characterize MW dynamical groups
(e.g., Helmi & White 1999; Chiba & Beers 2000; Gómez et al. 2010; Helmi et al.
2018; Myeong et al. 2018), reinforces that part of those stars are possibly
accreted because they have smaller $L_{z}$ compared to the TriAnd/disk ones
(blue circles/gray dots) at the same $E$, a typical characteristic of accreted
populations (e.g., Naidu et al. 2020). On the other hand, TriAnd candidates
present $E$ and $L_{z}$ overlapping with the region of the plot occupied by
the outer disk, indicating a common in situ origin.

In Figure 3(c) and (d), we show the $X_{\rm GC}$ vs. $Y_{\rm GC}$ and $X_{\rm
GC}$ vs. $Z_{\rm GC}$ projections, respectively. We observe that the velocity
vectors from the majority of the sample, in blue, show a corotation with the
Galactic disk. However, the red vectors, representing the accreted candidates
in our sample, appear to be randomly distributed and do not follow the
direction of the Galactic disk, as expected for ex situ populations. Our
analysis indicates that the majority of the TriAnd members present in situ-
like orbits, in agreement with other works from the literature (Bergemann et
al., 2018; Hayes et al., 2018a).

Figure 3: (a) Orbital eccentricity vs. inclination; (b) $(E,L_{Z})$; (c) and
(d) $(X_{\rm GC},Y_{\rm GC})$ and $(X_{\rm GC},Z_{\rm GC})$ projections,
respectively, with vectors representing their velocities in the $X$, $Y$, and
$Z$ Galactocentric Cartesian directions. The orbital parameters of TriAnd
candidate stars employed in this work are represented in blue. The red circles
represent the stars classified with a possible accreted origin. The kinematic
criterion applied here is represented by a red line in panel (a). The APOGEE
DR17 sample is shown in gray for comparison; panels (a) and (b) show the full
sample cleaned of globular cluster and dwarf galaxy stars, and panels (c) and
(d) show our restricted outer disk selection ($5<Y_{\rm GC}/{\rm kpc}<15$,
$\absolutevalue{Z_{\rm GC}}<2\ {\rm kpc}$, and $R_{\rm GC}>15\ {\rm kpc}$).

###  4.2 Metallicity Distribution Function

In order to understand the metallicity distribution function (MDF) of the
TriAnd population, we analyzed and compared the $[Fe/H]$ derived from this
work with other recent spectroscopic analyses. The stars classified as TriAnd
in our sample have a mean metallicity of $[Fe/H]\sim-0.8$ dex, a similar value
to that found by the Hayes et al. (2018a) and Sales Silva et al. (2019)
samples but more metal-poor than the Bergemann et al. (2018) sample. The MDFs
for different TriAnd samples are shown in Figure 4.

This mean metallicity value indicates that, on average, our TriAnd candidates
are a more metal-poor stellar population than our selected APOGEE outer disk
sample. We speculate that the TriAnd population was probably dynamically
heated before enriching, maintaining a lower mean metallicity. In this
scenario, the younger stellar populations were not dynamically heated enough
to reach a $Z_{GC}$ similar to TriAnd, thus explaining the lower mean
metallicity in comparison to the outer disk.

Figure 4: The MDFs for our selected APOGEE outer disk sample and TriAnd stars
from the samples of this work, Hayes et al. (2018a), and Bergemann et al.
(2018). The violin plots represent the distribution shape of each sample, the
white dots are the respective metallicity values, the mean value is
represented by a black circle with a centered white dot, the thick bar is the
interquartile range, and the thin bar represents the 95% confidence interval.

As we can see from Figure 4, the Bergemann et al. (2018) sample stands out
with a higher mean metallicity population when compared to other derived
TriAnd samples, with an offset of $\sim 0.2$ dex from our sample. We checked
if this feature could be attributed to different sample distances, where stars
with a lesser $R_{\rm GC}$ are expected to be more metal-rich. TriAnd
candidates from our sample are distributed in the range $12<R_{\rm GC}<25$
kpc, whereas most of our stars are found in the range $16<R_{\rm GC}<20$ kpc.
The Bergemann et al. (2018) sample is found at a similar distance ($R_{\rm
GC}=18\pm 2$ kpc), excluding this alternative.

We suggest that one of the most plausible explanations for this difference in
metallicity found in Bergemann et al. (2018) is the method employed by the
authors to estimate the atmospheric parameters for their sample, where $T_{\rm
eff}$ was estimated by combining the infrared flux method with photometric
data from the AAVSO Photometric All-Sky Survey and 2MASS. The use of
photometry combined with spectroscopy can lead to an overall higher derived
value for ${\rm[Fe/H]}$ when compared to a purely spectroscopic analysis. The
use of NLTE models by Bergemann et al. (2018) is another possible explanation
behind the differences, as both this work and Hayes et al. (2018a) employ LTE
models.

###  4.3 Chemical abundances

As we can note from Section 4.1, the analysis of the orbital parameters of our
TriAnd candidates indicates that they display characteristics of a mixture
between in situ and accreted stars. With the benefit of chemical analysis, we
expect that a more holistic chemodynamical approach can enlighten our
understanding of the nature of TriAnd.

Throughout this section, APOGEE DR17 data are utilized for the comparison
between our derived TriAnd abundances and MW’s accreted and in situ, in
particular the outer disk, populations. To select outer disk stars, we
employed the following criteria: $5<Y_{\rm GC}/{\rm kpc}<15$,
$\absolutevalue{Z_{\rm GC}}<2\ {\rm kpc}$, and $R_{\rm GC}=\sqrt{X_{\rm
GC}^{2}+Y_{\rm GC}^{2}}$ $>15\ {\rm kpc}$. These criteria are more
conservative than those applied by Hayes et al. (2018a) for APOGEE DR14
(Abolfathi et al., 2018) and hence more representative of the outer disk
population. To check if TriAnd shares a chemical composition with accreted
objects, we use the chemodynamical criteria from Limberg et al. (2022), which
were constructed for APOGEE-available abundances and designed to yield minimal
contamination from in situ stars, to select Gaia-Sausage/Enceladus (GSE;
Belokurov et al. 2018; Haywood et al. 2018, also Helmi et al. 2018) members.

In Figures 5 and 6, we show the estimated abundances for $\alpha$ (Mg, Si, Ca,
and Ti), odd-$Z$ (Al and Na), and iron-peak (Cr and Ni) elements for our
TriAnd sample. For comparison, we also show the results from other TriAnd
spectroscopic studies, namely, Bergemann et al. (2018) and Hayes et al.
(2018a), as well as the aforementioned APOGEE outer disk sample. For
consistency with APOGEE DR17, we choose to represent the NLTE-corrected values
only for Mg and Ca abundances.

The abundances of Na, Al, Si, Ti, and Cr are not included in Hayes et al.’s
(2018a) analysis. Since the authors used APOGEE data, we cross-matched their
sample with APOGEE DR17 to obtain the missing chemical abundances and update
the Mg, Ca, and Ni values. In addition, Bergemann et al. (2018) did not
analyze the abundance of Al, Si, Ca, Cr, or Ni. Therefore, they are not
included in our comparison. For our interpretations, we also consider the
classification into either in situ or accreted origin from the eccentricity
selection presented in Section 4.1 and verify if chemical abundances confirm
(or reject) our initial conjecture that a mixture of populations can be found
at TriAnd’s location.

Figure 5: [X/Fe] ratio vs. [Fe/H] for the $\alpha$ elements: (a) Mg, (b) Si,
(c) Ca, and (d) Ti. The TriAnd candidate stars analyzed in this work are
represented by blue circles, whereas the possible accreted stars are
represented by red ones. The Hayes et al. (2018a) and Bergemann et al. (2018)
TriAnd samples are represented by orange squares and green triangles,
respectively. Outer disk stars from the APOGEE DR17 database (Abdurro’uf et
al., 2022) are shown in gray. Isodensity contours associated with GSE are
represented by yellow lines. Figure 6: The [X/Fe] ratio vs. [Fe/H] for the
odd-$Z$ and iron-peak elements: (a) Al, (b) Na, (c) Cr, and (d) Ni. The TriAnd
candidate stars employed in this work are represented by blue circles, whereas
the possible accreted stars are represented by red circles. The Hayes et al.
(2018a) and Bergemann et al. (2018) samples are represented by orange squares
and green triangles, respectively. Outer disk stars from the APOGEE DR17
database (Abdurro’uf et al., 2022) are shown in gray, and isodensity contours
associated with GSE are shown in yellow.

####  4.3.1 $\alpha$ elements

The $\alpha$ elements (Mg, Si, Ca, and Ti) are mainly formed in explosive
events such as Type II supernovae (SNe II) in high-mass stars (Woosley &
Weaver, 1995). These events occur on a shorter timescale ($\sim$Myr) when
compared to other enrichment sources such as Type Ia supernovae (SNe Ia;
$\sim$Gyr), formed by merging white dwarf binaries, which are the main source
for the iron-peak elements (Cr, Ni, and Fe; Iwamoto et al. 1999). Therefore,
the [$\alpha$/Fe] vs. [Fe/H] relation of a stellar population provides useful
information about the relative contribution of SNe II and Ia to the
interstellar medium where these stars were formed (Matteucci & Brocato, 1990).

In Figure 5, we can see that the stars kinematically classified as part of
TriAnd follow a behavior expected for in situ stars. The [$\alpha$/Fe] ratio
for this TriAnd population is similar to the APOGEE outer disk sample as well
as the Bergemann et al. (2018) and Hayes et al. (2018a) TriAnd samples. This
behavior can be better appreciated in the chemical abundances of Mg, Si, and
Ca, even with an overall low metallicity that sometimes overlaps with the GSE
footprint.

The stars classified as likely having an accreted origin fall on top of the
contours associated with GSE, showing a higher [$\alpha$/Fe] ratio
(${\gtrsim}0.2$ dex) when compared to the thin-disk population. If we look at
our full sample, ignoring the eccentricity selection, a “knee” pattern can be
identified in the relation between the [$\alpha$/Fe] ratio and [Fe/H], as
suggested in the literature (Sales Silva et al., 2020).

One star, also studied by Chou et al. (2011), stands out in our sample.
Represented by a blue circle outlined in red in Figures 3, 5, 6, and 7, this
star is not marked as being accreted when taking into account the eccentricity
selection method employed, while showing an accreted chemical pattern. As we
can observe for this star in particular, these apparent ex situ chemical
characteristics from the Chou et al. (2011) sample could have led the authors
to suggest an accreted origin for TriAnd. Given that this star is located at
$R_{\rm GC}$ = 14 kpc and based on its chemistry, we speculate that it belongs
to the thick/splashed disk population.

####  4.3.2 Odd-$Z$ and Iron-peak Elements

In Figure 6, we show the odd-$Z$ (Al and Na) and iron-peak (Cr and Ni)
elements. Odd-$Z$, as for the $\alpha$ elements, are majorly synthesized by
the evolution of massive stars but by different nucleosynthesis processes,
often present in the red giant branch and asymptotic giant branch (AGB) phases
of stellar evolution (Herwig, 2005; Ventura et al., 2011; Depalo & LUNA
Collaboration, 2016). These different processes are reflected in the stellar
population of the MW, where a clear difference between the abundances of
odd-$Z$ and $\alpha$ elements can be observed (Zasowski et al., 2019).

In the top panels of Figure 6, we show the abundances for Al and Na. In the
APOGEE field samples, we can observe the difference in the distribution of Al
and Na in the stellar populations of the Galaxy. The chemical profiles of
[Al/Fe] (top left) shows a clear distinction between the APOGEE outer disk
sample (gray) and the isodensity contours associated with GSE (yellow), while
the [Na/Fe] chemical profiles show a higher dispersion with substantial
overlap between both field populations.

The distinction mentioned between the APOGEE field samples (outer disk vs.
GSE) can be observed for our TriAnd candidates in the [Al/Fe] ratio. Although
an apparent offset between our derived abundances and the APOGEE ones is
present, it is possible to observe a clear difference in the [Al/Fe] vs.
[Fe/H] relation between the stars with in situ characteristics and the high-
eccentricity (likely accreted) ones. This offset has already been mentioned in
the literature (e.g., Griffith et al. 2019) when comparing chemical abundance
data for the odd-$Z$ elements in the APOGEE database with the GALAH database.

The [Na/Fe] presented in Figure 6 for the TriAnd stars presents a dispersion
(${\sim}0.25$ dex) very similar to the chemical profiles observed by Bergemann
et al. (2018), reinforcing the in situ characteristics of the TriAnd
population. On the other hand, the suggested accreted stars from our sample
occupy the same region as the contours associated with GSE and present a
higher overall dispersion in [Na/Fe] and metallicity than the TriAnd stars.

As already mentioned, SNe Ia formed by low-mass stars are the main sources of
interstellar medium enrichment for the iron-peak elements. The MW in situ
populations are expected to present chemical characteristics linked to an
extended star-formation history, where the interstellar medium enrichment
lasts for long time periods ($\sim$1 Gyr; Matteucci & Francois 1989). Galaxies
with extended star formation histories have higher [Ni/Fe] and [Cr/Fe]
abundances (Kirby et al., 2019; Larsen et al., 2022).

The chemical profiles from iron-peak elements of our sample can be observed in
the bottom panels of Figure 6. The TriAnd stars follow the same profile as the
outer disk population, showing a small dispersion, as can be seen in the
APOGEE data (Hayes et al., 2018a).

The [Cr/Fe] (bottom left panel of Figure 6) shows only a minor overlap with
stars from our TriAnd sample with the GSE contours, at the edge of the
distribution. On the other hand, the accreted selection essentially overlaps
the GSE position.

In the bottom right panel of Figure 6, the [Ni/Fe] presents a clear
distinction between the APOGEE outer disk sample and the GSE contours, with no
overlap between these samples. The TriAnd stars and stars with a suggested
accreted origin in our sample also present very distinct characteristics. This
behavior reinforces the scenario where the TriAnd region is formed by a
majority of in situ stars and a small contribution of ex situ interlopers.

####  4.3.3 Neutron-capture elements

For elements heavier than Fe (atomic number $Z>26$), nucleosynthesis typically
takes place via neutron-capture processes. We can distinguish two major
processes: the “slow” ($s$-process) and “rapid” ($r$-process) neutron-capture
processes (see Sneden et al. 2008 for a review).

The traditional main $s$-process produces elements from Sr ($Z=38$) to Pb
($Z=82$) majorly in the H-rich intershell of low-mass AGB stars (Busso et al.,
1999; Herwig, 2005; Karakas & Lattanzio, 2014). For the occurrence of
$r$-process nucleosynthesis, highly energetic events are expected, such as
binary compact mergers (Lattimer & Schramm, 1974; Rosswog et al., 1999; Wanajo
et al., 2009; Drout et al., 2017; Thielemann et al., 2017, 2020; Cowan et al.,
2021) and/or magnetorotational supernovae (Winteler et al., 2012; Mösta et
al., 2014; Reichert et al., 2021), synthesizing heavy elements with two major
peaks around Xe ($Z=54$) and Pt ($Z=78$).

In Figure 7, the abundances of Ba and Eu are presented, elements
representative of the $s$\- and $r$-processes, respectively, within the
metallicity range considered ($-1.5<\rm[Fe/H]<-0.5$). For comparison with our
TriAnd sample, we obtained data from the Stellar Abundances for Galactic
Archaeology (SAGA) database (Suda et al., 2008, 2017), shown as gray dots,
since the APOGEE DR17 database does not contain Ba and Eu abundances. We could
not apply the same outer disk selection as presented in Figures 5 and 6 as the
stars from the SAGA database do not reach large $R_{\rm GC}$. Therefore, we
display all data for Ba and Eu for MW stars contained in this database,
excluding upper limits. We also display isodensity contours representing the
thin ($\rm[Mg/Fe]<0.2$) and thick ($\rm[Mg/Fe]\geq 0.2$) disks (Li et al.,
2018; Mackereth et al., 2019; Beraldo e Silva et al., 2021; Myeong et al.,
2022; Queiroz et al., 2023) in purple and coral, respectively.

Figure 7: The [X/Fe] and [Ba/Eu] ratio vs. [Fe/H] for the neutron-capture
elements (Ba and Eu), with each chemical element depicted in its respective
panel. The TriAnd candidate stars employed in this work are represented by
blue circles, the eccentricity selection is represented by red circles, the
Bergemann et al. (2018) sample is represented by green triangles, field stars
from the SAGA database (Suda et al., 2008, 2017) are in gray, and isodensity
contours associated with the thin and thick disk in are purple and coral,
respectively.

Ratcliffe & Ness (2023) analyzed the chemical trends of the MW disk in
different Galactocentric distances. Their results for the neutron-capture
elements presented the largest variation from all of the analyzed elements,
showing that the evolution and enrichment sites of these heavy elements do not
follow a simple trend along the disk. Indeed, in the top panels of Figure 7,
it is shown that both the [Ba/Fe] from this work and the disk stars from the
SAGA database present a high dispersion, making it difficult to distinguish
between different stellar populations.

Even amidst a high dispersion, the majority of our calculated abundances can
be found in the range 0.1 < [Ba/Fe] < 0.3. These high values of [Ba/Fe], when
compared to the local disk stars from SAGA database in the same metallicity
range, agree with the works from Bergemann et al. (2018) and Sales Silva et
al. (2019) for their sample of TriAnd candidates. This scenario can probably
be explained by the positive gradient from the $[s/Fe]$ ratio present in the
Galactic disk. The TriAnd stellar population is located in a higher $R_{\rm
GC}$, presenting a higher $[s/Fe]$ ratio than stars in the local disk (see,
e.g. Sales-Silva et al. 2022).

Contrary to the [Ba/Fe], the [Eu/Fe] ratio, presented in the middle panels of
Figure 7, decreases with an increasing metallicity, as expected for
$r$-process elements that are linked to high-mass stars. From our calculated
[Eu/Fe] abundances, we can observe that the majority of our sample is in good
agreement with the disk stars from the SAGA database and Bergemann et al.
(2018) sample, favoring an in situ origin.

Conversely, the high-eccentricity stars present chemical patterns similar to
accreted populations, usually presenting an $r$-process enrichment
($\rm[Eu/Fe]\geq 0.5$). Interestingly, we also identified that the three most
metal-poor stars in our sample ($-1.50<\rm[Fe/H]<-1.25$) are possible $r$-II
stars (Beers & Christlieb, 2005), a class of highly $r$-process enhanced stars
that are thought to have originated in rare, neutron-rich sites, presenting
low [Ba/Eu] (${<}0$) and high [Eu/Fe] (${\gtrsim}+0.7$) values (Holmbeck et
al., 2020).

In the bottom panels of Figure 7 we can observe the [Ba/Eu] ratio; these
abundances reflect the relative number of high- to low-mass stars enriching
the interstellar medium where these stars were formed. Even with a high
dispersion, we can observe that the majority of our true TriAnd stars present
an overall higher [Ba/Eu] ratio, meaning a predominant enrichment from the
$s$-process over the $r$-process. Alternatively, the stars with a possible ex
situ origin show lower [Ba/Eu], whereas our most metal-poor stars with
accreted characteristics present [Ba/Eu] ${\sim}-0.5$ expected for an ex situ
population (Aguado et al., 2021a, b; Limberg et al., 2021b; Matsuno et al.,
2021; Ji et al., 2022; Naidu et al., 2022).

##  5 Summary

Divergent suggestions about the nature of TriAnd haven been raised since its
discovery. In this work, taking advantage of the largest homogeneous sample of
TriAnd candidate stars analyzed with high-resolution spectra, we performed a
chemodynamical investigation of an expanded sample of 31 TriAnd candidate
stars in order to better understand the origin of its stellar population.

From the orbital parameter analysis of our TriAnd candidates, we observed that
the majority of our sample falls within the range of the orbital parameters
typical of stars from the Galactic disk. Moreover, through an eccentricity cut
($e>0.4$), we identified that the subsample with higher eccentricity presents
similar properties to MW accreted populations, such as GSE, indicating an
accreted origin for these stars.

The abundance analysis identified that the majority of our TriAnd candidates
present chemical patterns similar to the outer thin-disk population.
Reassuringly, out of our TriAnd members, those chemically more akin to an in
situ nature are specifically those on low-eccentricity orbits ($e<0.4$). On
the contrary, the high-eccentricity subsample exhibits an abundance profile
similar to accreted MW populations, such as GSE.

Our chemodynamical study indicates an in situ origin for TriAnd, as the
majority of the sample analyzed in this work presents properties, both
dynamical and chemical, similar to the outer thin-disk population. We also
attributed the suggested “knee” pattern in the relation between the
[$\alpha$/Fe] ratio and [Fe/H] as a contamination of ex situ stars at the same
distance and location of TriAnd in past literature samples. Finally, our
analysis strongly suggests that the contradictory interpretations found in the
literature about the origin of such an overdensity, including extragalactic
(e.g., Chou et al. 2011; Deason et al. 2014), in situ (e.g., Bergemann et al.
2018; Hayes et al. 2018a), and “unique" (e.g., Sales Silva et al. 2019, 2020),
were mainly due to the smaller number of stars available for analysis in
previous works.

Y.A. would like to acknowledge financial support from CAPES (proc.
88887.604784/2021-00). H.D.P. thanks FAPESP (proc. 2018/21250-9 and
2022/04079-0). S.R. would like to acknowledge partial financial support from
FAPESP (procs. 2015/50374-0 and 2014/18100-4), CAPES, and CNPq. G.L.
acknowledges FAPESP (procs. 2021/10429-0 and 2022/07301-5). A.P.-V.
acknowledges DGAPA-PAPIIT grant IA103122. R.M.S. acknowledges CNPq (proc.
306667/2020-7). The work of V.M.P. is supported by NOIRLab, which is managed
by the Association of Universities for Research in Astronomy (AURA) under a
cooperative agreement with the National Science Foundation. J.V.S.-S.
acknowledges support from FAPERJ under grant 203.899/2022. F.A. acknowledges
financial support from MICINN (Spain) through the Juan de la Cierva-
Incorporación program under contract IJC2019-04862-I. Y.A and H.D.P. thank
Katia Cunha for its support with the proposal and discussions in the early
phase of the project. Y.A. and S.R. thank Chris Sneden for the helpful
insights about using MOOG and how to make a better spectral synthesis;
Henrique Reggiani and Jhon Galarza for discussions about the spectral
analysis; the Gemini help desk team, especially Eder Martioli, for help with
GRACES data and reduction, and Ulisse Munari for providing the synthetic
spectra. This work was enabled by observations made from the Gemini North
telescope, located within the Maunakea Science Reserve and adjacent to the
summit of Maunakea. We are grateful for the privilege of observing the
universe from a place that is unique in both its astronomical quality and its
cultural significance. Based on observations obtained through the Gemini
Remote Access to CFHT ESPaDOnS Spectrograph (GRACES). ESPaDOnS is located at
the Canada-France-Hawaii Telescope (CFHT), which is operated by the National
Research Council of Canada, the Institut National des Sciences de l’Univers of
the Centre National de la Recherche Scientifique of France, and the University
of Hawai’i. ESPaDOnS is a collaborative project funded by France (CNRS,
MENESR, OMP, LATT), Canada (NSERC), CFHT, and ESA. ESPaDOnS was remotely
controlled from the international Gemini Observatory, a program of NSF’s
NOIRLab, which is managed by the Association of Universities for Research in
Astronomy (AURA) under a cooperative agreement with the National Science
Foundation on behalf of the Gemini partnership: the National Science
Foundation (United States), the National Research Council (Canada), Agencia
Nacional de Investigación y Desarrollo (Chile), Ministerio de Ciencia,
Tecnología e Innovación (Argentina), Ministério da Ciência, Tecnologia,
Inovações e Comunicações (Brazil), and Korea Astronomy and Space Science
Institute (Republic of Korea). IRAF was distributed by the National Optical
Astronomy Observatory, which was managed by the Association of Universities
for Research in Astronomy (AURA) under a cooperative agreement with the
National Science Foundation.

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