11institutetext: Institute of Theoretical Physics and Astronomy, Vilnius
University, Saulėtekio al. 3, Vilnius, LT-10257, Lithuania
11email<EMAIL_ADDRESS>
22institutetext: Crimean Astrophysical Observatory, Nauchny 298409, Crimea

# Abundance of barium in the atmospheres of red giants
in the Galactic globular cluster NGC 104 (47 Tuc)††thanks: Based on
observations obtained at the European Southern Observatory (ESO) Very Large
Telescope (VLT) at Paranal Observatory, Chile.

V. Dobrovolskas 11 E. Kolomiecas 11 A. Kučinskas 11 J. Klevas 11 S. Korotin 22

(Received ; accepted)

###### Abstract

Context. While most (if not all) Type I Galactic globular clusters (GGCs) are
characterised by spreads in the abundances of light chemical elements (e.g.
Li, N, O, Na, Mg, Al), it is not yet well established whether similar spreads
may exist in s-process elements as well.

Aims. We investigated the possible difference in Ba abundance between the
primordial (1P) and polluted (2P) stars in the Galactic globular cluster (GGC)
47 Tuc (NGC 104). For this, we obtained homogeneous abundances of Fe, Na, and
Ba in a sample of 261 red giant branch (RGB) stars which is the largest sample
used for Na and Ba abundance analysis in any GGC so far.

Methods. Abundances of Na and Ba were determined using archival GIRAFFE/VLT
spectra and 1D non-local thermodynamic equilibrium (NLTE) abundance analysis
methodology.

Results. Contrary to the finding of Gratton et al. (2013), we did not detect
any significant Ba–Na correlation or 2P–1P Ba abundance difference in the
sample of 261 RGB stars in 47 Tuc. This corroborates the result of D’Orazi et
al. (2010) who found no statistically significant Ba–Na correlation in 110 RGB
stars in this GGC. The average barium-to-iron ratio obtained in the sample of
261 RGB stars, $\langle\left[\mathrm{Ba}/\mathrm{Fe}\right]_{\rm
1D\leavevmode\nobreak\ NLTE}\rangle=-0.01\pm 0.06$, agrees well with those
determined in Galactic field stars at this metallicity and may therefore
represent the abundance of primordial proto-cluster gas that has not been
altered during the subsequent chemical evolution of the cluster.

###### Key Words.:

Techniques: spectroscopic – Stars: abundances – Stars: late-type – globular
clusters: individual

##  1 Introduction

Most if not all Galactic globular clusters contain multiple stellar
populations which are characterised by different abundances of chemical
elements, radial distributions in the given GGC, and kinematic properties (see
e.g. Bastian & Lardo, 2018, for a review). It is usually assumed that a
fraction of the GGC stars, so-called second-generation stars (2P), were
enriched in certain chemical elements and depleted in others by some first-
generation (1P) polluters. A number of enrichment scenarios have been proposed
to explain the observed properties of the GGCs by involving various potential
polluters, such as fast-rotating massive stars (FRMS; e.g., Decressin et al.,
2007), asymptotic giant branch (AGB) stars (e.g., D’Antona et al., 2016),
super-massive stars (SMS, $\sim 10^{4}\,{\rm M}_{\odot}$; e.g., Denisenkov &
Hartwick, 2014; Gieles et al., 2018). Unfortunately, none of the proposed
scenarios is capable of explaining chemical and kinematic differences between
the 1P and 2P stars simultaneously (Bastian & Lardo, 2018; Gratton et al.,
2019).

The 1P–2P differences in the abundances of chemical elements have been
detected as correlations or anti-correlations between the abundances of light
elements: Na–Li (Bonifacio et al., 2007) correlation and O–Na, Mg–Al (Carretta
et al., 2009), Li–O (Pasquini et al., 2005; Shen et al., 2010) anti-
correlations. Except for the so-called Type II GGCs (see Marino et al. 2015,
2019 for details), the majority of Galactic GGCs (Type I) seem to be uniform
in their Fe-group, as well as s- and r-process element abundances.

As for the s-process elements, results of several studies have suggested that
there may be some variation in their abundances in several Type I GGCs, too.
For example, Gratton et al. (2013) reported a possible Na–Ba correlation in
the sample of 106 red horizontal branch (RHB) stars in NGC 104 (47 Tuc). While
statistical significance of the possible correlation was found to be high, the
authors have warned that, because of a small range in the [Ba/Fe] variation
and [Na/O] correlation with the effective temperature along the horizontal
branch (HB), their result needed to be confirmed in further studies for
claiming a definite detection (Gratton et al., 2013). Some other studies have
shown tentative signs for a possible correlation between the abundances of
light and s-process elements in other Type I GGCs, such as signatures for
Sr–Na and Y–Na relations in M 4 (Spite et al. 2016; Villanova & Geisler 2011;
but see also D’Orazi et al. 2013). Also, our recent analysis of Zr abundance
in 237 RGB stars in 47 Tuc suggests the existence of weak but statistically
significant Na–Zr correlation and 2P–1P Zr abundance difference of
$\Delta{\rm[Zr/Fe]_{\rm 2P-1P}}\approx 0.06$ (Kolomiecas et al., 2021). The
existence of such correlations would indicate that the light and s-process
elements should have been produced in the same polluters that have enriched
the 2P stars in some elements and depleted in others. Unfortunately, the data
obtained so far is inconclusive thus further analysis of more s-process
elements in the larger samples of GGC stars would be desirable.

With an aim of shedding more light on the possible 1P–2P differences in
s-process element abundances, in this study we determined Ba abundances in 261
RGB stars in 47 Tuc, a Type I GGC (Marino et al., 2019). To the best of our
knowledge, this is the only GGC in which a tentative detection of
statistically significant relation between the light and s-process element
abundances has been reported. Therefore, our primary goal was to verify
whether the Na–Ba correlation and/or 1P–2P Ba abundance difference could be
detected in our RGB star sample which is the largest one in which Ba abundance
was determined in any GGC so far.

The paper is structured as follows: after a short introduction we provide a
brief description of the observational data (Sect. 2) and abundance
determination methodology (Sect. 3). The obtained results are discussed in
Sect. 4 and the main findings are summarized in the Conclusions.

##  2 Observational data

In this work we studied a sample of RGB stars using the high-resolution
spectra that were obtained with GIRAFFE/VLT during three observing programs,
072.D-0777(A), PI: François; 073.D-0211(A), PI: Carretta; and 088.D-0026(A),
PI: McDonald (Table 1). Individual spectra were retrieved for the analysis
from the ESO Advanced Data Products
Archive111http://archive.eso.org/wdb/wdb/adp/phase3_spectral/form. Our stellar
sample overlaps with that utilized by Kolomiecas et al. (2021) in their study
of Zr in 47 Tuc (228 stars in common) but it also includes 33 additional
targets that were not analysed in Kolomiecas et al. (2021).

Median-averaged sky spectrum was obtained from the dedicated sky fibres and
subsequently was subtracted from the individual spectra of all target stars.
In the case of the program 088.D-0026(A) where three exposures for the same
targets were available, spectra were co-added to increase signal-to-noise
ratio, $S/N$, with the latter determined at the continuum level in the
vicinity of the investigated Na i and Ba ii lines ($\sim 619.7$ nm). The $S/N$
ratio of the final spectra varied from $S/N\sim 220$ for the targets at
$T_{\mathrm{eff}}\sim 4200$ K to $S/N\sim 60$ at $T_{\mathrm{eff}}\sim 4700$
K. All target spectra were continuum normalized using the splot task under
IRAF (Tody, 1986). Radial velocities were determined using the fxcor task in
IRAF and cross-correlation technique. For this, we used synthetic spectrum
computed with the model atmosphere having $T_{\mathrm{eff}}=4500$ K, $\log
g=1.90$, and $\left[\mathrm{Fe}/\mathrm{H}\right]=-0.7$ to represent a typical
RGB star in 47 Tuc. The obtained radial velocities were then used to shift the
wavelengths to the rest frame with the dopcor task in IRAF.

Only RGB stars were selected for the abundance analysis, by discarding HB and
AGB stars based on their position in the $V-(V-I)$ colour magnitude diagram.
Before performing abundance analysis, we verified that all targets are members
of 47 Tuc which was done based on their proper motions taken from the Gaia
EDR3 catalogue (Gaia Collaboration et al., 2021). Following Milone et al.
(2018), we required that the proper motions of the cluster member stars did
not deviate by more than 1.5 mas yr-1 from the mean cluster proper motion,
$\mu_{\rm RA}=5.25$ mas yr-1 and $\mu_{\rm DEC}=-2.53$ mas yr-1 (Baumgardt et
al., 2019).

Table 1: Spectroscopic data used in this work.  Programme | Date of | Setting |  $\lambda_{\mathrm{central}}$, | R | Exposure, | Number of
---|---|---|---|---|---|---
| observations |  | nm |  | s | stars
072.D-0777(A) | 2003-10-21 | HR13 | 627.3 | 22 500 | 1500 | 112
| 2003-10-21 | HR13 | 627.3 | 22 500 | 3600 | 121
073.D-0211(A) | 2004-07-07 | HR13 | 627.3 | 22 500 | 1600 | 113
088.D-0026(A) | 2011-11-26 | HR13 | 627.3 | 22 500 | $3\times 700$ | 113
| 2011-11-26 | HR14A | 651.5 | 17 740 | $3\times 340$ | 113

Stars common to several programs were identified by cross-matching their
coordinates within the 1 arcsec radius. This resulted in 14 stars in common to
the 073.D-0211(A) and 088.D-0026(A) samples, 23 stars to 072.D-0777(A) and
088.D-0026(A), 17 stars to 072.D-0777(A) and 073.D-0211(A), and 5 stars in
common to all three samples (Table 6). Abundances of these stars were computed
by averaging those obtained using spectra of individual observing programs.

The final target sample consisted of 261 unique RGB stars for which we
determined Fe, Na, and Ba abundances. Our RGB sample is therefore somewhat
larger than that of Kolomiecas et al. (2021, 237 objects) because it includes
stars with $T_{\mathrm{eff}}>4800$ K which were discarded from their analysis.

Figure 1: Target RGB stars in the $T_{\mathrm{eff}}-\log g$ plane. The red
line is Yonsei-Yale (Kim et al., 2002) isochrone (12 Gyr,
$\left[\mathrm{M}/\mathrm{H}\right]=-0.68$, ${\rm[\alpha/Fe]}=+0.4$). Table 2:
Atomic parameters of Na i and Ba ii lines used in this study.

Element |  $\lambda$, nm |  $\chi$, eV |  log gfa |  log $\gamma_{rad}$b |  log $\frac{\gamma_{4}}{N_{e}}$c |  log $\frac{\gamma_{6}}{N_{H}}$d
---|---|---|---|---|---|---
Na i | 615.4225 | 2.102 | $-1.547$ | 7.85 | $-4.39$ | $-7.28$
Na i | 616.0747 | 2.104 | $-1.246$ | 7.85 | $-4.39$ | $-7.28$
Ba ii | 614.1730 | 0.704 | $-0.076$ | 8.20 | $-5.48$ | $-7.47$
Ba ii | 649.6910 | 0.604 | $-0.377$ | 8.10 | $-5.48$ | $-7.47$

  * a

Wiese & Martin (1980); b Mashonkina & Bikmaev (1996); c Kupka et al. (2000); d
Korotin et al. (2011)

###  2.1 Atmospheric parameters

Atmospheric parameters for the majority of targets (228 stars) were determined
by Kolomiecas et al. (2021). For the remaining 33 stars we used the same
procedure as utilised by the latter authors. With this approach, effective
temperatures were determined using photometry from Bergbusch & Stetson (2009)
and color-$T_{\mathrm{eff}}$ calibration of Ramirez & Melendez (2005). The
obtained values were verified by checking trends in the iron abundance – lower
excitation potential plane. In most cases, the slopes were consistent with
zero thereby suggesting a good agreement of photometric and spectroscopic
$T_{\mathrm{eff}}$ values.

Surface gravities were obtained by using a classical relation between stellar
mass, luminosity, effective temperature, and surface gravity. This decision
was made because only a few Fe ii lines were available in the stellar spectra
(typically, 1–4 lines). To compute gravities from photometry, we assumed an
identical mass of 0.87 M⊙ for all RGB stars investigated in this work, as
indicated by the Yonsei-Yale isochrone of 12 Gyr and
$\left[\mathrm{M}/\mathrm{H}\right]=-0.68$ (Kim et al., 2002). Bolometric
luminosities were computed using relation between the bolometric correction,
$T_{\mathrm{eff}}$, and metallicity from Alonso et al. (1999).

Microturbulence velocities were obtained by enforcing zero trend in the iron
abundance – equivalent width plane while keeping photometric effective
temperature fixed. In this procedure strong lines were neglected ($W>15$ pm)
because of their reduced sensitivity to microturbulence velocity. Due to small
number (17-28) of iron lines, the estimated accuracy of microturbulence
velocity was $\approx 0.2$ km s-1 and represents the main source of
uncertainty in the determined Ba abundances (Section 3.5).

The location of the target RGB stars in the $T_{\mathrm{eff}}-\log g$ plane is
shown in Fig. 1. Atmospheric parameters of the individual target stars are
listed in Table 8.

##  3 Abundance analysis

Because the stellar sample used in the present study overlaps with that
investigated by Kolomiecas et al. (2021), abundances of Fe and Na in targets
common to the two studies (228 stars) were taken from Kolomiecas et al.
(2021). In addition, we determined Fe and Na abundances in 33 RGB stars and Ba
abundances in 261 stars. In both studies abundance analysis of Fe and Na was
performed following strictly identical procedures. Abundances of Na and Ba
were determined using ATLAS9 model atmospheres (Kurucz, 1993; Sbordone et al.,
2004; Sbordone, 2005) and spectral synthesis, under the assumption of non-
local thermodynamic equilibrium (NLTE), while local thermodynamic equilibrium
(LTE) was assumed in the analysis of Fe. Procedures involved in the abundance
determination are described below.

###  3.1 Reference abundances in the Sun

Reference abundances in the Sun were determined using the Kitt Peak Solar Flux
atlas (Kurucz et al., 1984). To briefly summarize, Fe abundance was obtained
using the equivalent width method, with the latter determined by fitting the
Voigt profiles to the observed spectral line profiles. The Fe i and Fe ii line
list used for abundance determination is provided in Table 7. Oscillator
strength data were obtained from the VALD3 database (Ryabchikova et al.,
2015).

Microturbulence velocity was obtained by iteration until there was zero trend
of iron abundance with the line strength. Our determined value of
$\xi_{\mathrm{micro}}$= 0.93 km s-1 is in good agreement with the typical
value of 0.9-1.0 km s-1 available in the literature (Doyle et al., 2014). The
average solar iron abundance obtained with the determined microturbulence
velocity was A(Fe) = $7.55\pm 0.01$ ($\sigma=0.06$; here $\pm 0.01$ is the
error of the mean, $\sigma$ denotes standard deviation due to the line-to-line
abundance variation) which was obtained from 29 lines with W¡10.5 pm (Table
7). Although the scatter is significant, the average abundance is in good
agreement with those available in the literature, e.g. $A({\rm Fe})=7.51\pm
0.06$ from Caffau et al. (2011).

For the solar Na abundances, we used $A({\rm Na})=6.17$ which was determined
with the NLTE spectral line synthesis methodology (Sect. 3.3) and by using the
same Na i lines as utilised in the analysis of the target stars in 47 Tuc.
This value agrees well with those obtained in the earlier studies, e.g.
$A({\rm Na})_{\odot}=6.21\pm 0.04$ derived by Scott et al. (2015a).

Solar abundance of Ba was obtained by utilising the NLTE methodology (Sec.
3.4). The determined value, A(Ba) = 2.17, agrees reasonably well with $A({\rm
Ba})_{\rm 1D\leavevmode\nobreak\ NLTE}=2.07$ and $A({\rm Ba})_{\rm
3D\leavevmode\nobreak\ NLTE}=2.28$ derived by Gallagher et al. (2020) and
coincides with the meteoritic value of $2.17\pm 0.02$ from Lodders (2021).

Figure 2: Examples of the observed (black dots) and best-fitted synthetic Na i
line profiles (red lines) in the GIRAFFE spectra of the target stars
characterised by different Na abundances and effective temperatures
($T_{\mathrm{eff}}\approx 4250$ K, top row; $T_{\mathrm{eff}}\approx 4750$ K,
bottom row). The continuum level is shown as the gray dashed line.  Figure 3:
Examples of the observed (black dots) and best-fitted synthetic spectra in the
vicinity of Ba ii lines (red lines) in the GIRAFFE spectra of the target stars
characterised by different effective temperatures ($T_{\mathrm{eff}}\approx
4250$ K, top panel; $T_{\mathrm{eff}}\approx 4750$ K, bottom panel). The
continuum level is shown as the gray dashed line.

###  3.2 Determination of Fe abundance

Abundances of Fe in the target RGB stars were obtained using a set of 17–28 Fe
i lines ($612.79-691.67$ nm), with the line lower level excitation potentials
in the range of 2.18–4.61 eV (Table 7). The mean value obtained in the sample
of 261 stars was $\langle{\rm[Fe/H]}\rangle=-0.75\pm 0.05$ (the error denotes
standard deviation due to the star-to-star abundance scatter) which agrees
well with the values determined in the earlier studies (cf.
$\langle{\rm[Fe/H]}\rangle=-0.74\pm 0.05$, Carretta et al. 2009, 114 RGB
stars). The Fe abundances obtained in 261 target RGB stars are listed in Table
8.

###  3.3 Determination of Na abundance

Two Na i lines located at 615.42 nm and 616.07 nm were used in the
determination of Na abundances (Table 2). The model atom of Na used in this
work was the same as described in Dobrovolskas et al. (2014). It consisted of
the first 20 energy levels of Na i and the ground level of Na ii, with a total
of 46 radiative transitions taken into account. The rate coefficients of
collisions with hydrogen atoms for the lower 9 levels of Na i were taken from
Barklem et al. (2010). Examples of the best-fitted Na i line profiles in the
Na-poor and Na-rich stars are shown in Fig. 2, while the Na abundances
determined in all target stars are plotted in the middle row of Fig. 4 and are
listed in Table 8. The Na NLTE–LTE abundance corrections,
$\Delta_{\mathrm{\mathrm{1D\,NLTE-
LTE}}}=A(\mathrm{Na})_{\mathrm{1D\,NLTE}}-A(\mathrm{Na})_{\mathrm{1D\,LTE}}$,
were in the range $-0.07...-0.27$, depending on the strength of the spectral
line and effective temperature of the target star (Table 3).

Table 3: 1D NLTE-LTE abundance corrections for Na and Ba.  Element |  $\lambda$, nm |  W, pm | $\Delta_{\mathrm{1D\,NLTE-LTE}}$
---|---|---|---
$T_{\mathrm{eff}}=4250$ K, $\log g=1.35$
Na-poor
Na i | 615.42 | 5.7 | $-0.08$
Na i | 616.07 | 8.7 | $-0.14$
Ba ii | 614.17 | 19.0 | $-0.06$
Na-rich
Na i | 615.42 | 9.6 | $-0.19$
Na i | 616.07 | 11.8 | $-0.27$
$T_{\mathrm{eff}}=4750$ K, $\log g=2.40$
Na-poor
Na i | 615.42 | 3.6 | $-0.07$
Na i | 616.07 | 5.4 | $-0.08$
Ba ii | 614.17 | 14.8 | $-0.10$
Na-rich
Na i | 615.42 | 6.3 | $-0.12$
Na i | 616.07 | 8.7 | $-0.15$

###  3.4 Determination of Ba abundance

As in the case of Na, abundances of Ba were determined using spectral
synthesis approach, under the assumption of NLTE. Two Ba ii lines located at
614.17 nm and 649.69 nm were used for the analysis whenever possible though in
most cases only the 614.17 nm line was available for the analysis (Table 2).
The level departure coefficients and spectral line profiles were computed
using the version of MULTI code (Carlsson, 1986) modified by (Korotin et al.,
1999). To take into account the fact that 614.17 nm line is blended with Fe i
line, the spectra were computed with the SynthV code (Tsymbal, 1996). For
this, we used NLTE departure coefficients of Ba computed with the MULTI code
while the Fe i line was synthesized under the assumption of LTE. The model
atom of Ba used in this work was the same as described in Andrievsky et al.
(2009). It consisted of 31 levels of Ba i, 101 levels of Ba ii ($n<50$), and
the ground level of Ba iii. In total, 91 bound-bound transitions between the
lowest 28 energy levels ($n<12$, $l<5$) of Ba ii were taken into account. Fine
structure was included for the levels 5d2D and 6p2P0, according to the
prescription given in Andrievsky et al. (2009). Following the latter study,
the hyperfine structure of the 649.69 nm line was approximated using three
components (lines).

Figure 4: Fe (top row), Na (middle row), and Ba (bottom row) abundances
plotted against the microturbulence velocity (left column) and effective
temperature (right column). In all panels the linear fits to the data are
shown as the solid red lines. Table 4: Typical Fe, Na, and Ba abundance
measurement errors.

Element |  $\sigma$(Teff) |  $\sigma$(logg) |  $\sigma$($\xi_{\mathrm{t}}$) |  $\sigma$(cont) |  $\sigma$(fit) |  $\sigma$A(X)(total)
---|---|---|---|---|---|---
| dex | dex | dex | dex | dex | dex
$T_{\mathrm{eff}}=4250$ K, $\log g=1.35$
Fe | 0.03 | 0.02 | 0.12 | 0.10 | 0.02 | 0.16
Na | 0.06 | 0.01 | 0.05 | 0.03 | 0.04 | 0.09
Ba | 0.01 | 0.04 | 0.10 | 0.04 | 0.03 | 0.12
$T_{\mathrm{eff}}=4750$ K, $\log g=2.40$
Fe | 0.04 | 0.02 | 0.11 | 0.10 | 0.05 | 0.16
Na | 0.06 | 0.01 | 0.03 | 0.03 | 0.05 | 0.09
Ba | 0.02 | 0.02 | 0.08 | 0.05 | 0.06 | 0.12

Only the 088.D-0026(A) sample contained spectra where both Ba ii lines were
available for every target star. In this case, the final Ba abundance value
was taken as an average of estimates obtained from the two lines. In all other
cases Ba abundance was determined using a single 614.17 nm line. Since Ba ii
lines are strong, we did not take into account their possible contamination by
CN lines and their influence on the Ba abundance determination. We
nevertheless estimate that this effect should not exceed 0.01 dex. The
obtained Ba abundances are shown in the bottom row of Fig. 4 and are listed in
Table 8. Typical examples of the 1D NLTE synthetic spectrum fits to the Ba ii
614.17 nm spectral line in the target stars characterised by different
effective temperatures are provided in Fig. 3. The $\Delta_{\mathrm{1D\,NLTE-
LTE}}$ abundance corrections for Ba were in the range $-0.06...-0.10$,
increasing in magnitude at higher effective temperatures (Table 3).

The average barium-to-iron abundance ratio determined in the sample of 261 was
$\langle\left[\mathrm{Ba}/\mathrm{Fe}\right]_{\rm 1D\leavevmode\nobreak\
NLTE}\rangle=-0.01\pm 0.06$ where the error is standard deviation due to star-
to-star abundance variation.

###  3.5 Errors in the determined Fe, Na, and Ba abundances

Uncertainties in the determined Fe, Na, and Ba abundances were determined
using the prescription given in Černiauskas et al. (2018), by utilising ATLAS9
models computed with $T_{\mathrm{eff}}=4250$ K, $\log g=1.35$; and
$T_{\mathrm{eff}}=4750$ K, $\log g=2.40$. In essence, we first estimated
errors in the effective temperature, surface gravity, microturbulence
velocity, continuum determination and line profile fitting. For all sample
stars, we assumed identical uncertainties in the effective temperature,
surface gravity, and microturbulence velocity, which were 80 K, 0.1 dex, and
0.1 km s-1, respectively. Error of the continuum placement was determined by
computing standard deviation of the continuum, $\sigma_{\rm cont}$, in the
vicinity of the investigated spectral lines and then changing continuum level
by $\pm 1\sigma_{\rm cont}$ from the adopted level and re-determining
abundances. The fit error was estimated by computing standard deviation of the
difference between the observed and best-fitted spectral line profiles. Again,
the synthetic spectrum was changed by $\pm 1\sigma$ and the resulting
difference in the abundance was computed. These individual errors were then
added in quadratures and were used further as uncertainties in the determined
abundances of Fe, Na, and Ba. The typical abundance errors are provided in
Table 4.

##  4 Results and discussion

###  4.1 Possible Ba–Na anti-correlation in the RGB stars in 47 Tuc?

The barium-to-iron abundance ratios, $\left[\mathrm{Ba}/\mathrm{Fe}\right]$,
determined in 261 RGB stars show a weak but statistically significant anti-
correlation with the sodium-to-iron abundance ratios,
$\left[\mathrm{Na}/\mathrm{Fe}\right]$ (Fig 5), with the Pearson’s correlation
coefficient $r_{\mathrm{P}}=-0.18$. Assuming a null-hypothesis that there is
no correlation between the two abundance ratios, the Student’s $t$-test gives
a probability of $p_{\mathrm{P}}=2.9\times 10^{-3}$ that such $r_{\mathrm{P}}$
value could be obtained by chance (Table 5). The Spearman’s rank correlation
coefficient and the $p$-value are, respectively, $\rho_{S}=-0.20$ and
$p_{\mathrm{S}}=1.3\times 10^{-3}$, while for the Kendall’s rank correlation
the two values are $\tau_{K}=-0.14$ and $p_{\mathrm{K}}=6.9\times 10^{-4}$,
respectively.

Figure 5: [Ba/Fe] abundance ratios determined in 261 RGB stars in 47 Tuc,
plotted versus their [Na/Fe] abundance rations. Typical abundance error bars
are shown in the bottom left corner. The linear fit to the data is shown as
the solid red line. Figure 6: [Ba/Fe] abundance ratios obtained in 261 RGB
stars in 47 Tuc, plotted versus relative radial distance from the cluster
center. Cluster half-mass radius, $r_{\mathrm{h}}=174$ arcsec, was taken from
Trager et al. (1993). Linear fit to the data is marked as the solid red line.
Typical abundance error is shown as the vertical bar in the bottom left
corner. Table 5: The
$\left[\mathrm{Ba}/\mathrm{Fe}\right]-\left[\mathrm{Na}/\mathrm{Fe}\right]$
abundance correlation coefficients and their in the full sample of 261 RGB
stars in 47 Tuc and sub-samples divided into $\Delta T_{\mathrm{eff}}=100$
K-wide bins. $N$ is the number of stars in the given (sub-)sample.

Pearson | Spearman | Kendall
---|---|---
$r$ | $p$ | $\rho$ | $p$ | $\tau$ | $p$
Full sample, N = 261
$-0.18$ | $2.93\times 10^{-3}$ | $-0.20$ | $1.30\times 10^{-3}$ | $-0.14$ | $6.88\times 10^{-4}$
$4200\leq T_{\mathrm{eff}}<4300$, N = 23
$-0.11$ | 0.61 | $-0.09$ | 0.68 | $-0.06$ | 0.68
$4300\leq T_{\mathrm{eff}}<4400$, N = 15
$-0.15$ | 0.60 | $-0.17$ | 0.54 | $-0.16$ | 0.42
$4400\leq T_{\mathrm{eff}}<4500$, N = 35
$-0.38$ | 0.024 | $-0.34$ | 0.048 | $-0.24$ | 0.043
$4500\leq T_{\mathrm{eff}}<4600$, N = 42
$-0.15$ | 0.35 | $-0.15$ | 0.32 | $-0.11$ | 0.31
$4600\leq T_{\mathrm{eff}}<4700$, N = 48
$-0.41$ | $3.77\times 10^{-3}$ | $-0.41$ | $3.19\times 10^{-3}$ | $-0.28$ | $4.87\times 10^{-3}$
$4700\leq T_{\mathrm{eff}}<4800$, N = 66
$-0.11$ | 0.39 | $-0.16$ | 0.20 | $-0.12$ | 0.15
$4800\leq T_{\mathrm{eff}}<4800$, N = 32
0.15 | 0.42 | 0.04 | 0.83 | 0.03 | 0.84

We also checked whether such anti-correlation may exist between the determined
[Ba/Fe] abundance ratios and the distance of the target RGB stars from the
cluster center. For this, we adopted the coordinates of the 47 Tuc center,
$\alpha_{0}(J2000)$ = 6.023625 deg and $\delta_{0}(J2000)$ = -72.081276 deg,
from Baumgardt et al. (2019) and the half-mass radius of $r_{\mathrm{h}}=174$
arcsec from Trager et al. (1993). Our data suggest a weak correlation in the
${\rm[Ba/Fe]}-r_{\mathrm{h}}$ plane, with $r_{\rm P}=0.14$ and $p_{\rm
P}=0.019$ (Fig. 6); similar values were obtained for the Spearman’s and
Kendall’s rank correlation coefficients.

On the one hand, these results may suggest that in 47 Tuc the 1P stars (lower
Na abundances) are more abundant in Ba than the 2P stars (higher Na
abundances; Fig. 5). Because the 2P stars tend to concentrate towards the
cluster center in this and other GGCs (e.g. Kučinskas et al., 2014), this
should lead to a weak correlation in the ${\rm[Ba/Fe]}-r_{\mathrm{h}}$ plane.
This is exactly what our data suggest (Fig. 6). However, this result would be
difficult to explain from the nucleosynthesis point of view. It is well known
that Ba could be synthesised either in the low- and intermediate-mass AGB
stars via the main s-process or during the central He-burning phase in the
massive rotating stars. However, the trends suggested by our data would
require a mechanism which would destroy Ba in the 2P stars instead of
synthesising it like, e.g. Na.

On the other hand, no Ba–Na anti-correlation is seen in the absolute abundance
plane, $A({\rm Ba})-A({\rm Na})$ (Fig. 7). In this case we obtained
$r_{\mathrm{P}}=0.04$ and $p_{\mathrm{P}}=0.52$, with similar results also for
the Spearman’s and Kendall’s rank correlation coefficients: $\rho_{S}=0.03$
and $p_{\mathrm{S}}=0.63$ for the former, and $\tau_{K}=0.03$ and
$p_{\mathrm{K}}=0.53$ for the latter. No trend was detected in the
${\rm[Ba/Fe]}-r_{\mathrm{h}}$ plane either, with $r_{\rm P}=-0.002\pm 0.006$
and $p_{\rm P}=0.984$. This therefore suggests that the anti-correlation in
the ${\rm[Ba/Fe]}-{\rm[Na/Fe]}$ plane and correlation in the
${\rm[Ba/Fe]}-r_{\mathrm{h}}$ plane may be caused by the variation in Fe
abundance alone.

Figure 7: Absolute abundances of Ba and Na in the $A({\rm Ba})-A({\rm Na})$
plane. Typical abundance error bars are shown in the bottom left corner. The
linear fit to the data is shown as the solid red line.

A closer inspection of the data has shown that the Ba–Na anti-correlation in
the
$\left[\mathrm{Ba}/\mathrm{Fe}\right]-\left[\mathrm{Na}/\mathrm{Fe}\right]$
plane has been detectable only in a single effective temperature interval
(bin), $4600\leq T_{\mathrm{eff}}<4700$ K, with a possible weaker trend seen
also in the $4400\leq T_{\mathrm{eff}}<4500$ K bin (Table 5). No statistically
significant correlation was detected in other $\Delta=100$ K wide effective
temperature bins. It therefore seems that the anti-correlation in the
$4600\leq T_{\mathrm{eff}}<4700$ K bin was caused by several outlying stars
with the somewhat higher and lower Ba abundances at the lowest and highest Na
abundance ends, respectively (see Sect. C). No residual correlation is seen if
these outliers are excluded.

It is also important to stress that abundances of all there elements analysed
in this study show trends with the microturbulence velocity (Fig 4). The most
likely explanation for this is that microturbulence velocities were slightly
underestimated in the hotter ($T_{\mathrm{eff}}\gtrapprox 4700K$) stars which
are fainter and therefore their spectra are of lower quality. For these stars,
a slight overestimate of the continuum during the determination of
microturbulence velocities would lead to a systematically larger abundances
determined from the weaker Fe i lines with respect to those obtained from the
stronger lines. This, in turn, would require a lower microturbulence velocity
value to eliminate the trend of iron abundance with the equivalent width.
Because the Ba ii lines are the strongest of all three elements studied, it is
not surprising that there is the strongest trend of abundance versus
microturbulence velocity for barium. Thus, overestimated Ba abundances in
stars with the lowest microturbulence velocities (i.e. those with the highest
effective temperatures) could also contribute to the spurious
$\left[\mathrm{Ba}/\mathrm{Fe}\right]-\left[\mathrm{Na}/\mathrm{Fe}\right]$
anti-correlation.

Therefore, the most likely reason for the anti-correlation seen in the
$\left[\mathrm{Ba}/\mathrm{Fe}\right]-\left[\mathrm{Na}/\mathrm{Fe}\right]$
plane and correlation in the
$\left[\mathrm{Ba}/\mathrm{Fe}\right]-r_{\mathrm{h}}$ plane may be a random
fluctuation in Ba abundances in several stars with the extreme Na abundances
in the $4600\leq T_{\mathrm{eff}}<4700$ K effective temperature bin. This
deviation in a single $T_{\mathrm{eff}}$ bin can not be explained by e.g. the
lower quality of the spectra, because for the hotter and therefore fainter
stars there is no such correlation. Similarly, there is no plausible
astrophysical reason why such correlation should exist in a single
$T_{\mathrm{eff}}$ bin only. We also note that no Ba–Na correlation was
detected by D’Orazi et al. (2010) in their analysis of 110 RGB stars in 47 Tuc
which indirectly supports our assertion that the
$\left[\mathrm{Ba}/\mathrm{Fe}\right]-\left[\mathrm{Na}/\mathrm{Fe}\right]$
anti-correlation detected in our study is in fact spurious. This said, it
would be nevertheless interesting to see whether the Ba–Na - or correlation
between any other s-process and light elements - could be seen in other GGCs,
especially given the tentative detection of Zr–Na correlation in the RGB stars
of 47 Tuc suggested in the analysis of Kolomiecas et al. (2021).

###  4.2 Average Ba abundance in the RGB stars in 47 Tuc

Until now, Ba abundance in 47 Tuc has been determined in a number of studies.
In one of the earlier attempts, James et al. (2004) investigated Ba in the
atmospheres of turn-off (TO) and subgiant (SGB) stars in 47 Tuc. The average
Ba LTE abundance determined in eight SGB stars was
$\left[\mathrm{Ba}/\mathrm{Fe}\right]$=+0.35, with only slightly lower value
of $\left[\mathrm{Ba}/\mathrm{Fe}\right]$=+0.22 obtained in three TO stars.
The average value derived for these stars is therefore slightly larger than
the average abundance obtained in our study, given that we determined
$\langle\left[\mathrm{Ba}/\mathrm{Fe}\right]_{\rm 1D\leavevmode\nobreak\
NLTE}\rangle=-0.01\pm 0.06$ and that the mean 1D NLTE–LTE Ba abundance
correction is $\sim-0.10$ dex.

One of the most comprehensive Ba abundance studies in the GGCs was performed
by D’Orazi et al. (2010) who studied 1200 stars in 15 GGCs. In case of 47 Tuc
they obtained a mean value of
$\langle\left[\mathrm{Ba}/\mathrm{Fe}\right]_{\rm 1D\leavevmode\nobreak\
LTE}\rangle=0.15\pm 0.06$ in the sample of 110 RGB stars (the error is
standard deviation due to star-to-star abundance variation). Again, this value
agrees well with that obtained in the present study if the NLTE–LTE correction
($\sim-0.10$ dex) is taken into account. Similarly, a reasonable agreement is
obtained with the average Ba LTE abundances obtained by Worley et al. (2010,
$\left[\mathrm{Ba}/\mathrm{Fe}\right]=0.34\pm 0.33$, 3 RGB stars) and Worley &
Cottrell (2012, $\left[\mathrm{Ba}/\mathrm{Fe}\right]=0.32\pm 0.05$, 13 RGB
stars) if the NLTE–LTE abundance correction is applied.

In their analysis of 13 RGB stars in 47 Tuc Thygesen et al. (2014) determined
an average Ba NLTE abundance $\left[\mathrm{Ba}/\mathrm{Fe}\right]=0.28\pm
0.07$ which is significantly higher than that obtained in our study. A
possible reason for this discrepancy is differences in the determined surface
gravities and microturbulence velocities. Judging from the analysis of 6 stars
that were available both in their and our samples we find that on average our
microturbulence velocities and gravities were higher by 0.33 km/s and 0.3 dex,
respectively. Correction for these differences would make our abundances
higher by 0.20 dex and would bring them into a good agreement with the value
obtained by Thygesen et al. (2014).

Figure 8: Abundance of barium in the Galactic globular clusters (compilation
by Marsakov et al. (2019)) and Galactic field stars (thin/thick disk stars:
Guiglion et al. (2018), halo stars: Lai et al. (2007); Cohen et al. (2013);
Roederer et al. (2014); Jacobson et al. (2015)). Average [Ba/Fe] ratio
obtained in 261 RGB stars in 47 Tuc is marked by the red symbol where the
25th, 75th percentiles and the average values are indicated by the horizontal
bars while the extent of the whiskers correspond to the minimum and the
maximum abundance values observed in this cluster.

###  4.3 Implications for the chemical evolution scenarios of 47 Tuc

Results of Ba abundance analysis in 261 RGB stars suggest that there have been
no enrichment or depletion of Ba in the 2P stars of 47 Tuc. Similarly, no
Ba–Na correlation have been detected by D’Orazi et al. (2010) in their
analysis of 110 RGB stars. These findings contradict the result obtained by
Gratton et al. (2013) who observed a weak but statistically significant Ba–Na
correlation in the sample of 114 RHB stars in 47 Tuc. However, as was
indicated by Grevesse et al. (2015), their detection of a possible Ba–Na
correlation should be taken with caution because the [Na/O] correlation with
effective temperature along the HB could partly or even fully explain the
observed weak Ba–Na trend.

The obtained average barium-to-iron abundance ratio in 261 RGB stars in 47
Tuc, $\langle\left[\mathrm{Ba}/\mathrm{Fe}\right]_{\rm 1D\leavevmode\nobreak\
NLTE}\rangle=-0.01\pm 0.06$, agrees well with the abundances obtained at this
metallicity in other GGCs, as well as with those determined in the Galactic
disc and halo stars (Fig. 8). This, as well as the relatively narrow spread in
Ba abundances of $\pm 0.06$ dex (which can be fully accounted for by the Ba
abundance determination error, $\sigma_{{\rm A(Ba)}}\approx 0.12$ dex),
suggest that Ba abundance in 47 Tuc may reflect that of the primordial proto-
cluster gas that has not been altered significantly (if at all) in the
subsequent chemical enrichment during the evolution of the GGC.

In a recent analysis of Zr abundance in 237 RGB stars in 47 Tuc, Kolomiecas et
al. (2021) detected a weak but statistically significant Zr–Na correlation and
2P–1P Zr abundance difference of 0.06 dex. Assuming that the 2P were indeed
enriched in Zr but not in Ba would suggest that only some s-process elements
have been produced by the polluters that have enriched the 2P stars in Na and
modified abundances of other light elements such as Li, N, O, Mg, Al, as
determined in the earlier studies (see e.g. Bastian & Lardo, 2018).
Theoretical yields from low- and intermediate-mass AGB stars predict that Zr
and Ba can be produced in substantial amounts but they should be synthesised
simultaneously (Cristallo et al., 2015). Similarly, substantial amounts of Zr
and Ba could be produced by explosive nucleosynthesis in massive rotating
stars but, again, both elements should be produced simultaneously (Limongi &
Chieffi, 2018). Besides, in the latter scenario one would also expect some
enrichment in the Fe-group and r-process elements which has not been observed
in the Type I GGCs until now (e.g. Bastian & Lardo, 2018; Gratton et al.,
2019). It would be therefore very desirable to verify whether correlations
between the other s-process and light chemical elements may exist in 47 Tuc as
well as in other Type I GGCs.

##  5 Conclusions

We present homogeneous abundances of Fe, Na, and Ba obtained in the sample of
261 RGB stars that belong to Galactic globular cluster (GGC) 47 Tuc. Contrary
to the earlier finding of Gratton et al. (2013), our results show no
statistically significant variation of Ba abundance with that of Na thus
suggesting that the primordial (Na-poor, 1P) and polluted (Na-rich, 2P)
populations in this GGC are characterized by the same average Ba abundance,
$\left[\mathrm{Ba}/\mathrm{Fe}\right]_{\rm 1D\leavevmode\nobreak\
NLTE}=0.02\pm 0.06$ (here the error is standard deviation due to star-to-star
abundance variation). This is also supported by the analysis of D’Orazi et al.
(2010) who detected no Ba–Na correlation in a sample of 110 RGB stars in this
GGC. Taken together with the recent finding of Kolomiecas et al. (2021) who
reported a detection of weak but statistically significant Zr–Na correlation
in 237 RG stars in 47 Tuc, these results would indicate that in this GGC the
2P stars may have been enriched in certain s-process elements (i.e. Zr) but
not in others (i.e. Ba). It would be therefore very desirable to verify
whether such correlations between the abundances of s-process and light
chemical elements may exist in other GGCs via the analysis of more s-process
elements in large samples of GGC stars, preferably by using spectra of higher
resolution and better signal-to-noise ratio.

###### Acknowledgements.

We thank the anonymous referee for useful comments that significantly helped
to improve the paper. This study has benefited from the activities of the
”ChETEC” COST Action (CA16117), supported by COST (European Cooperation in
Science and Technology) and from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 101008324 (ChETEC-INFRA). JK
acknowledges support from European Social Fund (project No
09.3.3-LMT-K-712-19-0172) under grant agreement with the Research Council of
Lithuania (LMTLT). This work has made use of the VALD database, operated at
Uppsala University, the Institute of Astronomy RAS in Moscow, and the
University of Vienna.

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##  Appendix A Differences between Ba abundances determined in different
observing samples

Table 6: Target stars common to different observing programs. 072.D-0777(A) | 073.D-0211(A) | 088.D-0026(A)
---|---|---
B-1256 | – | R287
F-1389 | 1389 | –
B-3449 | – | R563
F-3476 | – | R317
F-4373 | 4373 | –
– | 5172 | R259
B-5362 | – | R253
F-7711 | 7711 | –
– | 7904 | R443
B-7993 | – | R277
B-9163 | – | R800
– | 9518 | R237
– | 9717 | R682
B-9997 | – | R248
F-10198 | – | R756
F-10527 | – | R782
– | 10994 | R795
F-12408 | – | R784
F-13668 | 13668 | –
B-13795 | 13795 | R752
B-13853 | – | R246
B-14583 | 14583 | –
F-15451 | 15451 | –
– | 15552 | R381
– | 16597 | R778
B-16667 | – | R790
B-17819 | – | R245
– | 21369 | R249
F-23236 | 23236 | –
F-24463 | 24463 | –
B-29146 | – | R256
– | 29490 | R231
F-30104 | 30104 | –
B-30463 | 30463 | –
B-30949 | – | R766
B-32730 | 32730 | –
B-35878 | 35878 | –
B-38976 | – | R762
B-41429 | – | R392
B-42866 | 42866 | R656
B-42887 | – | R760
F-43632 | 43632 | R512
B-43852 | 43852 | R704
B-43889 | 43889 | R450

As was indicated in Sect. 3, a number of target RGB stars have been observed
in several observing programs (Table 6). A comparison of Fe, Na, and Ba
abundances obtained using spectra that were acquired in the different programs
is provided in Fig. 9–11. In most cases, differences between the average
abundances obtained in various samples was very small and did not exceed 0.03
dex (Figs 9-10).

The final abundances of the target RGB stars were further corrected for these
small systematic shifts. Since the program 072.D-0777(A) had the largest
number of stars, we chose to apply abundance shifts relative to this sample.
For stars in 073.D-0211(A) sample we applied abundance shifts of +0.03, +0.01,
+0.03 dex for the abundances of Fe, Na, and Ba, respectively. In case of the
sample 088.D-0026(A), abundances of Fe, Na, and Ba were corrected by +0.02,
$-0.01$, and $-0.03$ dex, respectively. The final abundance ratios (Table 8)
were computed using Solar reference values derived in Sect. 3.1.

Figure 9: Comparison of the abundances (top three pannels) and microturbulence
velocity values between the common stars in the programs 072.D-0777(A) and
073.D-0211(A). Grey dashed line shows average value of the difference.

Figure 10: Comparison of the abundances (top three pannels) and
microturbulence velocity values between the common stars in the programs
072.D-0777(A) and 088.D-0026(A). Grey dashed line shows average value of the
difference.

Figure 11: Comparison of the abundances (top three pannels) and
microturbulence velocity values between the common stars in the programs
073.D-0211(A) and 088.D-0026(A). Grey dashed line shows average value of the
difference.

##  Appendix B The Fe i and Fe ii lines used in the abundance analysis

The list of Fe i and Fe ii lines and their atomic parameters is provided in
the Table 7. We note that abundances from Fe ii lines were only determined
with the purpose of checking the agreement between the surface gravities
obtained from photometry and those derived using Fe ii lines; however, they
were not used in further analysis. Thus, Fe abundances used throughout this
paper are those that were obtained using Fe i lines.

Table 7: The list of iron spectral lines used in the abundance determination.  $\lambda$, nm |  $\chi$, eV | log gf | Ion. stage
---|---|---|---
612.79070 | 4.140 | -1.398 | Fe i
615.16180 | 2.180 | -3.300 | Fe i
616.53600 | 4.140 | -1.460 | Fe i
617.33360 | 2.220 | -2.810 | Fe i
618.02040 | 2.730 | -2.650 | Fe i
618.79900 | 3.940 | -1.580 | Fe i
620.03130 | 2.610 | -2.310 | Fe i
621.34300 | 2.220 | -2.550 | Fe i
621.92810 | 2.200 | -2.410 | Fe i
622.92283 | 2.845 | -2.805 | Fe i
623.26410 | 3.650 | -1.130 | Fe i
624.63188 | 3.602 | -0.733 | Fe i
625.25554 | 2.404 | -1.687 | Fe i
626.51340 | 2.180 | -2.510 | Fe i
627.02250 | 2.860 | -2.570 | Fe i
627.12788 | 3.332 | -2.703 | Fe i
630.15012 | 3.654 | -0.718 | Fe i
631.58115 | 4.076 | -1.710 | Fe i
632.26860 | 2.590 | -2.280 | Fe i
633.53308 | 2.198 | -2.177 | Fe i
633.68243 | 3.686 | -0.856 | Fe i
634.41490 | 2.430 | -2.890 | Fe i
638.07430 | 4.190 | -1.270 | Fe i
660.91100 | 2.560 | -2.610 | Fe i
670.35670 | 2.760 | -3.010 | Fe i
672.66660 | 4.610 | -1.010 | Fe i
675.01530 | 2.420 | -2.580 | Fe i
680.68450 | 2.730 | -3.090 | Fe i
681.02630 | 4.610 | -0.940 | Fe i
684.36560 | 4.550 | -0.780 | Fe i
685.51620 | 4.560 | -0.570 | Fe i
685.81500 | 4.610 | -0.910 | Fe i
691.66820 | 4.150 | -1.260 | Fe i
614.92580 | 3.890 | -2.720 | Fe ii
623.83920 | 3.870 | -2.520 | Fe ii
624.75570 | 3.890 | -2.320 | Fe ii
636.94620 | 2.891 | -4.160 | Fe ii

##  Appendix C Abundances of Ba in the target RGB stars with
$4600<T_{\mathrm{eff}}<4700$ K

As noted in Sect. 4, our data hint to a weak Ba–Na anti-correlation. Further
analysis has revealed that this may be an artefact caused by several spurious
outliers with $4600<T_{\mathrm{eff}}<4700$ K that were observed in one of the
programs, 073.D-0211(A) (Fig. 12). No such anti-correlation is seen in any
other $\Delta T_{\mathrm{eff}}=100$ K wide effective temperature bins. Indeed,
no statistically significant anti-correlation is left in the
$4600<T_{\mathrm{eff}}<4700$ K either when stars 19992, 38289, and 21369 are
excluded. We therefore conclude that there is no statistically significant
Ba–Na correlation in the analysed sample of 261 RGB stars in 47 Tuc.

Figure 12: Barium and sodium abundances in the effective temperature range
$4600<T_{\mathrm{eff}}<4700$ K. Labels next to the outlying data points show
identifications of the stars.

##  Appendix D Abundances of Fe, Na, and Ba in 261 target RGB stars in 47 Tuc

Abundances of Fe (LTE), Na (NLTE), and Ba (NLTE) obtained in 261 RGB stars in
47 Tuc are provided in Table 8.

Table 8: Abundances of Fe, Na, and Ba determined in the sample of 261 stars in
47 Tuc. In cases when abundances were determined using data from several
observing programs, microturbulence velocities and abundances obtained using
spectra from each individual program are provided. The IDs of individual stars
and IDs of the corresponding observing programs are given in the last two
columns, respectively (072.D-0777(A), PI: P. François; 073.D-0211(A), PI: E.
Carretta; 088.D-0026(A), PI: I. McDonald). The complete table is available in
electronic form.

GAIA Source ID | $T_{\mathrm{eff}}$ | $\log g$ | $\xi_{\rm micro}$ | $A{\rm(Fe)}$ | [Fe/H] | $A{\rm(Na)}$ | [Na/Fe] | $A{\rm(Ba)}$ | [Ba/Fe] | ID | Obs. program
---|---|---|---|---|---|---|---|---|---|---|---
| K |  |  km s-1 |  |  |  |  |  |  |  |
4689575185928019840 | 4275 | 1.45 | 1.47 | 6.77 | -0.78 | 5.58 | 0.19 | 1.54 | 0.15 | R761 | 088.D-0026(A)
4689575289007212416 | 4231 | 1.34 | 1.56 | 6.77 | -0.78 | 5.60 | 0.21 | 1.54 | 0.15 | R772 | 088.D-0026(A)
4689581263316313856 | 4553 | 2.07 | 1.53 | 6.77 | -0.78 | 5.63 | 0.24 | 1.39 | 0.00 | R786 | 088.D-0026(A)
4689613840634674048 | 4271 | 1.48 | 1.48 | 6.82 | -0.73 | 5.66 | 0.22 | 1.38 | -0.06 | 5277 | 073.D-0211(A)
4689614837066802944 | 4630 | 2.09 | 1.40 | 6.79 | -0.76 | 5.84 | 0.43 | 1.28 | -0.13 | 6808 | 073.D-0211(A)
4689614970198135296 | 4245 | 1.34 | 1.57 | 6.78 | -0.77 | 5.56 | 0.16 | 1.36 | -0.04 | B-13396 | 072.D-0777(A)
4689618238680828800 | 4303 | 1.50 | 1.58 | 6.75 | -0.80 | 5.63 | 0.26 | 1.33 | -0.04 | B-9254 | 072.D-0777(A)
4689618891515852160 | 4704 | 2.19 | 1.44 | 6.81 | -0.74 | 6.10 | 0.67 | 1.42 | -0.01 | R198 | 088.D-0026(A)
4689619166393741824 | 4512 | 1.96 | 1.46 | 6.82 | -0.73 | 5.73 | 0.29 | 1.36 | -0.08 | R199 | 088.D-0026(A)
4689621709015302400 | 4560 | 2.04 | 1.29 | 6.81 | -0.74 | 5.88 | 0.45 | 1.42 | -0.01 | R677 | 088.D-0026(A)
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