#  Mining the GALAH data I: Study of five Super lithium-rich metal-poor
giants.

Antony Susmitha Indian Institute of Astrophysics
Koramangala II block 560036
Bengaluru, KA, India  Anohita Mallick Indian Institute of Astrophysics
Koramangala II block 560036
Bengaluru, KA, India  Bacham E. Reddy Indian Institute of Astrophysics
Koramangala II block 560036
Bengaluru, KA, India

###### Abstract

The presence of a large amount of Li in giants is still a mystery. Most of the
super Li-rich giants reported in recent studies are in the solar metallicity
regime. Here, we study the five metal-poor super Li-rich giants (SLRs) from
GALAH Data Release 3 with their [Fe/H] ranging from -1.35 to -2.38 with
lithium abundance of A(Li) $\geq$ 3.4 dex. The asteroseismic analysis reveals
that none are on the red giant branch. The average period spacing ($\Delta P$
) values indicate giants are in the core He-burning phase. All of them are
low-mass giants (M $<$ 1.5M⊙). The location in the HR diagram suggests one of
them is in the red clump phase, and interestingly, the other four are much
brighter and coincide with the early AGB phase. The abundance analysis reveals
that C, O, Na, Ba, and Eu are normal for giants of respective metallicities
and evolutionary phases. Further, we didn’t find any strong evidence for the
presence of dust in the form of infrared excess or binarity from the available
radial velocity data. We discussed a few scenarios for the existence of SLRs
at higher luminosity, including past merger events. The findings will help to
understand the production and evolution of Li among giants, in particular,
during and the post-red clump phase.

— Giants— Metal-poor —Super Lithium Rich — RGB

##  1 Introduction

Lithium is expected to deplete as stars evolve off the main sequence and climb
up towards the tip of the red giant branch (RGB). The models predict Li
abundance (A(Li)= $logN(Li)/logN(H)+12$), post first dredge-up, not more than
A(Li) = 1.5 to 1.8 dex depending on mass (Iben, 1967; Gratton et al., 2000;
Lind et al., 2009b). A few among them have A(Li) exceeding the initial
abundance of ISM, A(Li) = 3.2 dex (Knauth et al., 2003) with which stars have
formed. The high A(Li) puzzle in red giants has been there for more than four
decades since its discovery in 1982 (Luck, 1982; Wallerstein & Sneden, 1982).

In recent years, significant advances have been made in understanding the
origin of high A(Li) in giants. This is mainly due to large data sets from
spectroscopic surveys like Large Sky Area Multi-Object Fibre Spectroscopic
Telescope (LAMOST) and Galactic Archaeology with HERMES (GALAH) and time-
resolved photometry from space missions like Kepler. A large number of spectra
helped increase the Li-rich sample by many folds, and the asteroseismic data
from the Kepler mission helped resolve the stars’ evolutionary phase in the
Hertzsprung-Russel Diagram (HRD). These studies suggest that all the Li-rich
giants are in the He-core burning phase of the red clump (RC) region (Deepak &
Reddy, 2019; Singh et al., 2019). The study by Kumar et al. (2020)
demonstrated that giants ascending the RGB only deplete Li and reach A(Li) as
low as $-$0.9 dex towards the RGB tip. Multiple pieces of evidence were put
forward that the high Li origin lies during the He-flash episode, which
terminates the RGB phase of evolution. They argued that the He-flash is only
the main stellar episode between the RGB phase’s end and the red clump’s
beginning. Hence, the He-flash holds the key to the origin of high Li in RCs.
In a novel study, Singh et al. (2021) have shown that the A(Li) in RC giants
and the gravity mode period spacing ($\Pi$p) are correlated. In that, the
giants with high A(Li), relatively younger RCs, have low $\Pi$p values
compared to giants with normal A(Li), relatively older RCs. They concluded
that A(Li) enrichment probably occurred within 2M years since the He-ignition
began at the RGB tip. The study also demonstrated that Li-richness is a
transient phenomenon. Further evidence was put forward by Mallick et al.
(2023) in which they studied Li abundance in samples of Low ($\leq$2M⊙) and
high mass ($>$ 2M⊙) giants. Interestingly, they found no Li-rich giants among
high-mass stars, suggesting He-flash is the most likely cause for Li
enrichment as high-mass giants are not expected to undergo He-flash, but low-
mass giants do. It is reasonable to believe that Li enrichment occurred during
the He-flash. However, it is not very clear how the Li produced in the
interiors was brought to the surface, though a few mechanisms have been
proposed (Cameron & Fowler, 1971; Fekel & Balachandran, 1993; Sackmann &
Boothroyd, 1999; Charbonnel & Balachandran, 2000; Denissenkov & VandenBerg,
2003; Kumar et al., 2011; Lattanzio et al., 2015). Most of the Li-rich giants
in the recent literature are in the metal-rich regime, i.e. [Fe/H] $\geq$
$-$1.0 dex.

Not many Li-rich giants were found among metal-poor giants. Only a handful of
stars were detected in the metal-poor regime (Ruchti et al., 2011; Li et al.,
2018; Casey et al., 2019) whose evolutionary phase was not explicitly
constrained. The question is whether the mechanism that drives the Li
enhancement in metal-rich giants is the same for the metal-poor giants.
Increasing the Li-rich metal-poor giants and resolving their evolutionary
phase may provide further constraints on Li production, dredge-up process and
its evolution.

Here, we report results on five super Li-rich giants found while searching
among a sample of metal-poor giants in the GALAH DR3 for which asteroseismic
data is available.

##  2 Sample selection

Table 1: The basic data of the five SLR giants from GALAH DR3.  Object name | RA | DEC | Vmag | A(Li) | log(L/L⊙) | RVGALAH | RVGaiaDR3
---|---|---|---|---|---|---|---
| (hh:mm:ss) | (dd:mm:ss) |  | (dex) |  | (km/s) | (km/s)
UCAC4 253-045343 | 10 25 28.84 | -39 26 01.09 | 12.6 | 3.74 | 3.01 | 171.3 $\pm$ 0.13 | 170.8 $\pm$ 0.24
UCAC4 099-098976 | 23 07 10.10 | -70 18 55.61 | 12.8 | 4.08 | 1.70 | 183.0 $\pm$ 0.31 | 182.3 $\pm$ 1.51
UCAC4 212-183136 | 20 29 16.13 | -47 41 51.51 | 13.8 | 4.23 | 2.71 | 5.9 $\pm$ 0.23 | 6.2 $\pm$ 1.53
UCAC4 308-077592 | 14 31 09.66 | -28 29 45.42 | 13.3 | 4.80 | 3.23 | -115.7 $\pm$ 0.13 | -115.0 $\pm$ 1.06
TYC 7262-250-1 | 13 09 32.26 | -37 09 17.79 | 11.5 | 4.54 | 3.00 | 30.7 $\pm$ 0.10 | 30.3 $\pm$ 0.34

We utilised the recent data release (DR3) of the GALAH survey (Buder et al.,
2021) to study the lithium (Li) abundance in metal-poor giants. The GALAH
survey employs the High Efficiency and Resolution Multi-Element Spectrograph
(HERMES, Sheinis et al. (2015)) mounted on the 3.9 m Anglo Australian
Telescope (AAT). HERMES provides high-resolution ( R $\sim$ 28000) optical
spectra in four wavelength windows (4713–4903 Å, 5648–5873 Å, 6478–6737 Å, and
7585–7887 Å) covering the spectral features of up to 30 elements, including
Li. We used specific selection criteria provided in the GALAH catalogue as
bit-flags to ensure the quality of the data and accuracy in stellar parameter
estimation. These criteria include flag_sp = 0, flag_fe_h = 0, and flag_Li_fe
= 0. The bit-flag = ’0’ indicates that abundances are reliable and no problems
were detected in determining the stellar parameters, iron and Li abundances.
Further, since we are only focusing on metal-poor giants, we applied the
criteria logg $<$ 3.0 and [Fe/H] $\leq$ -1.0, which resulted in a sample of
1038 metal-poor giant stars from GALAH DR3 survey. Among the selected sample
stars, we found five SLR giants with very high Li abundances, higher than the
present interstellar medium value (A(Li) $\sim$ 3.2). We imposed no other
selection criteria to restrict the samples, such as mass or Signal Noise
Ratio. The spectra of five giants were downloaded from the GALAH survey site
for analysis of the spectra and to search for additional peculiarities that
may be present. The details of the five giants are provided in Table 1. Note
Martell et al. (2021) listed all five giants as Li-rich giants in their
catalogue.

Figure 1: The HR diagram showing the five metal-poor SLRs along with stars in
the background from the GALAH DR3 (yellow points). Note the well-defined RGB,
luminosity bump and the red clump. One of the five SLRs is at the RC and the
remaining four are at or close to the early AGB.

##  3 Stellar parameters and elemental abundances

We found minor differences between the values by comparing the temperatures
derived from photometric colours and those listed in the GALAH catalogue.
Since the Li abundance is derived from the resonant line at 6707 Å which is
very temperature sensitive, any variation in the temperature can lead to
potential uncertainties in Li abundance values. To address this issue and
ensure the reliability of the values adopted from the catalogue data, we re-
derived the stellar parameters and elemental abundances by analysing the
spectra. We used version 12 of the spectral synthesis code TURBOSPECTRUM
developed by Plez (2012) to derive abundances and stellar parameters. We used
the stellar atmospheric models by Mészáros et al. (2012) in which the ATLAS9
and MARCS codes were modified with an updated H2O line list and with a wide
range of C and $\alpha$-enhancements. We have used only carbon normal models
as none of our stars exhibit enhancement in carbon. One-dimensional local
thermodynamic equilibrium (LTE) is assumed for all the species. We have
applied the non-LTE corrections wherever they are available in the literature
(see Table 3).

We adopted the solar abundances from Asplund et al. (2009). The line lists for
atomic lines were assembled from the Vienna Atomic Line Database (VALD) (Kupka
et al., 1999). Hyper-fine structure has been accounted for transitions of
elements Li, Ba, and Eu. For the case of molecular line lists, we used C2 data
from the Kurucz database (Kurucz 2007, 2009, 2013), and CN data from Plez &
Cohen (2005).

###  3.1 Stellar parameters

Table 2: Atmospheric parameters of the five SLR giants taken from the GALAH catalogue and derived in this study. Also, given is the $T_{\rm eff}$ derived from the photometry and listed in the Gaia. The uncertainties associated with the estimation of stellar parameters in this study are $\pm$150 K for Teff, $\pm$0.25 dex for log $g$, and $\pm$0.15 km/s for $\xi_{t}$.  | GALAH DR3 | Teff | Teff | $This~{}study$
---|---|---|---|---
Object name | Teff | log $g$ | [Fe/H] | $\xi_{t}$ | (V-K) | $Gaia$ | Teff | log $g$ | [Fe/H] | $\xi_{t}$ | $vsini$
| (K) | (dex) | (dex) | (km/s) | (K) | (K) | (K) | (dex) | (dex) | (km/s)
UCAC4 253-045343 | 4308$\pm$90 | 1.06$\pm$0.31 | -1.26$\pm$0.07 | 1.6 | 4025 | 4327 | 4100 | 1.00 | -1.43$\pm$0.14 | 1.4 | 6.0
UCAC4 099-098976 | 5263$\pm$101 | 2.49$\pm$0.22 | -1.46$\pm$0.12 | 1.1 | 5205 | 5375 | 5260 | 2.49 | -1.46$\pm$0.13 | 2.1 | 19.1
UCAC4 212-183136 | 4505 $\pm 153$ | 1.49$\pm$0.36 | -1.40$\pm$0.13 | 1.4 | 4091 | 4318 | 4080 | 0.10 | -2.38$\pm$0.15 | 2.7 | 13.8
UCAC4 308-077592 | 4210 $\pm$89 | 0.60$\pm$0.45 | -1.67$\pm$0.07 | 1.6 | 3923 | 4206 | 4100 | 0.60 | -1.86$\pm$0.13 | 1.7 | 6.2
TYC 7262-250-1 | 4218$\pm$73 | 0.82$\pm$0.34 | -1.31$\pm$0.05 | 1.6 | 4193 | 4121 | 4217 | 0.82 | -1.36$\pm$0.12 | 1.7 | 4.3

We have measured the equivalent width (EW) of unblended neutral and singly
ionised Fe lines to derive the stellar parameters. EWs were measured by
fitting Gaussian profiles to the features using splot task in PyRAF 111PYRAF
is a product of the Space Telescope Science Institute, operated by AURA for
NASA.. We have considered only those lines whose EW is less than 100 mÅ since
they are on the linear part of the curve-of-growth and are relatively
insensitive to the choice of microturbulence (Mucciarelli, 2011). The
uncertainties in the measurements were determined using the revised Cayrel
formula (Cayrel de Strobel & Spite, 1988; Battaglia et al., 2008). We have
interpolated the models from the grid of atmospheric models provided by
Mészáros et al. (2012) to obtain models of specific stellar parameters. The
$T_{eff}$ was derived by forcing the abundances of Fe I lines giving the same
abundance irrespective of the line’s lower excitation potential (LEP). We
estimated an error of 150 K on the spectroscopic temperature by ensuring that
any alteration in the slope of iron abundances derived from neutral lines with
excitation potential remained within one standard deviation of the Fe I line
abundances. The usual technique to derive the surface gravity (log g) assumes
ionisation equilibrium between Fe I and Fe II lines. Since we have only one
clean Fe II line detected in the GALAH spectra, we adopted the log g value
when the Fe II abundance is within the 1$\sigma$ of the average Fe I
abundances. We assumed an error of 0.25 dex for the estimation of log g.
Microturbulent velocity ($\xi_{t}$) is calculated by establishing the
abundance of Fe I to be independent of reduced equivalent width
(log(EW/$\lambda$)). The error in the estimation of $\xi_{t}$ is obtained when
the Fe I abundance did not change by more than 1$\sigma$ and the error is 0.15
km -1. The method was iterated until we found a set of $T_{\rm eff}$, log $g$,
and $\xi_{t}$ values for which we found no significant slope between
abundances of Fe I lines and their LEP values, measured EWs, and the
abundances of neutral and ionised lines are equal. The final value of Fe
abundance obtained for the converged atmospheric parameters is taken as the
metallicity of a star. The derived stellar parameters, the estimated errors in
this study, and the values given in the GALAH catalogue are listed in Table 2
along with values of $T_{\rm eff}$ derived from the colour (V-K) and values
given in the Gaia catalogue. Values of $T_{\rm eff}$ derived here agree very
well with the values from Gaia and (V-K) but one could notice a significant
difference of about 500K with GALAH value for a giant UCAC4 212-183136. For
the same star, we also found a large difference in $logg$. However, the $logg$
derived from the Gaia parallax agrees well with our value (see Table 5). The
differences between the values derived here and the values given in the GALAH
DR3 catalogue may be attributed to the choice of model atmospheres and, to
some extent method of analysis; pipeline versus manual analysis of star by
star. Since the $T_{\rm eff}$ and $logg$ derived here are in good agreement
with the values derived from Gaia parallax we adopt stellar parameters
obtained in this study for abundance analysis.

###  3.2 Abundances

Table 3: The elemental abundances of the SLR stars in this study. Object name | A(Li)LTE | A(Li)NLTE | [C/Fe] | [O/Fe] | [Na/Fe] | [Ba/Fe] | [Eu/Fe]
---|---|---|---|---|---|---|---
|  $\pm$0.20 |  $\pm$0.20 |  $\pm$0.20 |  $\pm$0.20 |  $\pm$0.20 |  $\pm$0.20 |  $\pm$0.30
UCAC4 253-045343 | 3.65 | 3.60 | -0.14 | 1.03 | -0.27 | 0.56 | 0.75
UCAC4 099-098976 | 4.90 | 4.21 |  $<$ -0.15 | 0.96 | -0.24 | -0.02 |  $<$ 0.20
UCAC4 212-183136 | 3.50 | 3.45 | 0.08 | $-$ | 0.58 | -0.38 | 0.86
UCAC4 308-077592 | 4.45 | 4.42 | -0.03 | 1.16 | -0.24 | -0.13 | 0.45
TYC 7262-250-1 | 4.38 | 4.31 | -0.19 | 0.55 | -0.05 | -0.09 | 0.59

Due to discrepancies between the stellar parameters from the GALAH data and
our values, we reevaluated the abundance of lithium and the elements of
interest for this study, which include C, O, Na, Ba, and Eu. We used the
resonance line at 6707.8 Å to derive the Li abundance and assumed that all the
Li present was from the 7Li isotope. The other Li I line, 6103Å, is not in the
spectral range covered by the GALAH survey. We have obtained the NLTE
corrections for the derived Li abundances from Lind et al. (2009a) , and the
corrected values are given in Table 3. We also used the 3D NLTE correction
code BREIDABLIK (Wang et al., 2021) to obtain the 3D NLTE Li abundance.
However, the code issued a warning saying the stellar parameters and LTE Li
abundances of all stars in this study are outside the grid utilized in
BREIDABLIK and the corrections may not be reliable. Nonetheless, according to
the grid from Lind et al. (2009a), only the Li abundance in UCAC4 099-098976
falls outside the grid where we performed a linear extrapolation of the grid
to obtain the NLTE correction for the star. One could see that the NLTE
corrections from Lind et al. (2009a) are very small ($\sim$ -0.05) for the
cool stars in our sample whereas, for UCAC4 099-098976, which is relatively
hotter, the NLTE correction is $\sim$ -0.7 (1D NLTE(A(Li) - 1D LTE A(Li))).
Such larger correction were also reported previously by Li et al. (2018) and
Sanna et al. (2020). May be the higher correction is needed as the star is
relatively hotter and metal poor in which large over ionisation takes place.
The plots for the best fit with their final abundance values are shown in Fig.
2.

|  |
---|---|---
|  |
Figure 2: The Li line profiles at 6707 Å of the five SLR stars compared with
the best fit synthetic spectra (dashed line).

The GALAH spectra also cover the spectral features O I triplet near 7774 Å, Ba
II at 5853.7 Å and 6496.9 Å and Eu II at 6645.1 Å. Using the spectral
synthesis method, we derived the abundances of these elements, and the values
are given in Table 3. To determine the abundance of C, we examined the C2
molecular band-head feature at 4737 Å which is quite weak or absent.
Constraining the 12C/13C isotopic ratio was particularly challenging, as all
our stars are deficient in carbon. Also, the 13C2 feature at 4744 Å is too
faint to derive the abundance. Additionally, the usual 13CN features (7990 -
8040 Å) used for deriving the 13C abundance are not covered by the GALAH
spectrograph.

All the stars in our sample display depleted C abundance and elevated O
abundance. The O abundance was estimated using triplets, but it should be
noted that these values have contributions from NLTE effects, and corrections
for such effects are not available for cool stars similar to those studied
here. Given this limitation, we can only confidently state that oxygen is
enhanced. However, providing a quantitative measure of the enhancement after
applying the NLTE correction is currently beyond the scope of this study.

####  3.2.1 Abundance errors

The abundances of all the elements except Fe are derived using the spectral
synthesis method. So, the uncertainties in the abundance values were estimated
through the goodness of the least squares fit, and the values are given in
Table 3. For deriving the Fe abundance, we used the method described in the
section 3.1 and the error due to contributions from uncertainties in EWs and
atomic parameters are listed as the 1$\sigma$ line-to-line scatter,
$\sigma_{log\epsilon}$ in Table 2.

##  4 Other observed properties

###  4.1  The Hα profile and mass loss

The profile of Hα spectral line is an indicator of possible mass-loss/stellar
activity present in the star and can be detected through asymmetries in the Hα
line profile (Mészáros et al., 2009). All five giants show distorted Hα
profile, and three of them also show emission in the Hα wings as shown in Fig.
3. The emissions are also asymmetric. We also examined Ca-triplet features not
covered in the GALAH spectra, using the Gaia DR3 data (Gaia Collaboration et
al., 2023), and the feature seems normal. We looked for possible mass loss
using $2MASS$ and WISE all-sky survey of infrared photometry.

The W4 band magnitudes in the WISE catalogue have quality flags other than
’A’. Two of the samples have ’U’ as the quality flag, which is expected for
such fainter sources as the W4 band is proved to have very low sensitivity for
reliable detection of fainter sources. So we did not calculate the W1-W4 value
for those stars, but for the other three stars (UCAC4 253-045343, UCAC4
308-077592, TYC 7262-250-1), we have obtained the values for $W1-W4$ as 0.49,
0.64 and 0.25, respectively. The values are very low, suggesting no active
mass loss. None of the giants shows any excess in the infrared colours (J, H,
K and W1, W2, and W3 or W4), suggesting, at least, no hot dust component. We
don’t have far-IR colours for the sample to check if the stars have cold dust
in case the mass loss occurred some time ago.

As per the asymmetric Hα profiles, it is shown that the emission wings of the
Hα line found in metal-poor stars can arise naturally from an extended, static
chromosphere(Dupree et al., 1984; Dupree, 1986), and may not be solely the
signature of mass-loss. So, the line asymmetries exhibited by these stars can
be attributed to some disturbances in the stellar atmosphere, which would
result in local mass flows rather than a steady-state mass loss.

Figure 3: The Hα profile of five SLRs. Note, that all of them show asymmetry
in the profiles.

###  4.2 Rotational velocity

Charbonnel & Lagarde (2010) suggests that the excess Li in some low-mass
giants may be due to rotation-induced extra mixing. We derived the giants’
projected rotational velocity to see if a correlation exists between Li
abundance and the projected rotation velocity, $vsini$. The GALAH DR3 provided
the overall line broadening parameter in the form of $vbroad$, which includes
$vsini$, and thermal broadening macro-turbulence ($vmacro$). We disentangled
the $vbroad$ into $vsini$ and $vmacro$ by adopting the relations between
$vmacro$ and $T_{\rm eff}$ for different luminosity classes as per the recipe
given in Hekker & Meléndez (2007). We have adopted the method described in
Ruchti et al. (2011) to distinguish the luminosity class of each star. Since
all the stars except UCAC4 099-098976 lie close to the RGB tip, we classified
them as luminosity class II. For UCAC4 099-098976, we used the designation
luminosity class III as its location in the HR diagram is closer to the RGB
bump. The derived $vsini$ is given in the Table 3. According to Carney et al.
(2008), the luminous giants with Mv $<$-1.5 exhibit net rotation, and the
projected rotation velocity can be larger than 3 km s-1, a typical value for
the upper RGB giants.

All the giants in this study show larger $vsini$ values than expected,
particularly the two giants, UCAC4 099-098976, and UCAC4 212-183136. Fekel &
Balachandran (1993) argued that during the convective mixing, redistribution
of angular momentum from the dredged-up material could induce increased
rotation in stars, creating a dynamo resulting in chromospheric activity.
Also, it could be the case where giants show higher rotation due to a sudden
decrease in the size, i.e., radius due to He-flash and the subsequent
transition to the red clump phase.

###  4.3 Radial velocity

The sample stars’ multi-epoch radial velocity values are important to
understanding any radial velocity variation observed. This can be used to
distinguish whether any of our giants are a member of a binary system. We
examined the radial velocity data from GALAH DR3 and Gaia DR3 (Katz et al.,
2023) (see Table 1). The radial velocity from Gaia DR3 is obtained by median
combining the individual epoch (transit) radial velocities. The individual
epoch radial velocities are not available from Gaia DR3. The RV data from the
two catalogues match very well, within 1 km s-1, though they are in two
different epochs, indicating no radial velocity variations. We have also
looked at the re-normalised unit weight error. (RUWE) of the samples in the
Gaia database. RUWE corresponds to the reduced chi-squared of the best-fitting
5-parameter single-body astrometric solution. This error coefficient can be
used to identify possible non-single stars whose RUWE $>$ 1.4 (Lindegren et
al., 2018, 2021). All the five giants have the RUWE $<$ 1.4, indicating a lack
of binarity. However, for binary systems whose orbital period is larger than
about $\sim$ 1000 days, RUWE loses most of its efficiency in detecting binary
systems, and RUWE parameter may not provide correct information about their
binarity (Jorissen, 2019). Hence, even though the RUWE for the samples is $<$
1.4, the radial velocity variation from a long-period binary companion cannot
be ruled out. The kinematic (U, V, W) data provided in the GALAH DR3 catalogue
suggest that the four giants, including the RC star, belong to the Galactic
halo component, whereas TYC 7262-250-1 belongs to the Galactic thick disk.

Table 4: The U,V,W space velocities of the SLR stars are given here. The last column corresponds to the total space velocity.  Object name | U | V | W | Vtotal
---|---|---|---|---
| (km/s) | (km/s) | (km/s) | (km/s)
UCAC4 253-045343 | -129.2 | -173.5 | 54.2 | 239.8
UCAC4 099-098976 | -186.4 | -328.6 | -158.1 | 429.6
UCAC4 212-183136 | -46.8 | -259.0 | -24.4 | 282.0
UCAC4 308-077592 | -74.0 | -69.7 | -189.2 | 230.4
TYC 7262-250-1 | -45.1 | -14.2 | 104.0 | 115.7

##  5 The evolutionary phase

It is important to determine the evolutionary phase of Li-rich giants to
constrain the origin of Li enhancement. The evolutionary phase could be
determined either using the star’s location in the HR diagram combined with
the evolutionary tracks or by using asteroseismic data. The latter turned out
to be more reliable and is considered the gold standard for separating RC
giants of He-core burning from those of RGB giants with He-inert core. Recent
studies concluded that super Li-rich giants are most likely to be RC stars
rather than RGB stars (Deepak & Reddy, 2019; Singh et al., 2019; Kumar et al.,
2020).

###  5.1 Evolutionary tracks

GALAH DR3 provides a value-added catalogue (VAC) with luminosities calculated
using the Bayesian Stellar Parameter Estimation code (BSTEP) from Sharma et
al. (2018). BSTEP provides a Bayesian estimate of intrinsic stellar parameters
from observed parameters by using the stellar isochrones. Although the
luminosities of these stars are readily available from the VAC, due to the
change in effective temperature from this study and from the GALAH DR3
catalogue (see Table 2), we have calculated the luminosity and log$g$ of these
stars using parallaxes obtained from the Gaia DR3 and V band magnitude using
the relation

$-2.5log\frac{L}{L_{\odot}}=M_{V}+BC_{V}(T_{eff})-Mbol_{\odot}$ (1)

and

$log{\it
g}=logg_{\odot}+log\frac{M}{M_{\odot}}+4log\frac{T_{eff}}{T_{eff_{\odot}}}-log\frac{L}{L_{\odot}}$
(2)

where the absolute magnitude in V band is calculated by

$M_{V}=V+5-5log_{10}r-A_{V}$ (3)

and the temperature-dependent bolometric correction is calculated by

$BC_{V}(T_{eff})=a+blogT_{eff}+c(logT_{eff})^{2}+d(logT_{eff})^{3}...$ (4)

Here, a, b, c, d  are the polynomial coefficients of the model function.
Values of these coefficients are taken from Torres (2010). The values of Mbol⊙
= 4.74, logg⊙ = 4.44 and T${}_{eff_{\odot}}$ = 5772 K are adapted for the Sun.
r is the distance to the star (in parsec), and AV is theinterstellar
extinction in the V band. We have obtained the $A_{V}$ values from Schlafly &
Finkbeiner (2011). We have used the $T_{eff}$ derived in this study as the
input $T_{eff}$ in the above equation, whereas the mass of the star is
obtained from the asteroseismic analysis (refer section 5.2.) The final values
are given in the Table 5. Within the estimated uncertainties, the log $g$
values derived from Gaia parallaxes agree well with those from GALAH DR3,
except for the giant UCAC4 212-183136. For this star, the $logg$ from GALAH
DR3 is more than 1 dex compared to the value derived in this study from the
spectra and the Gaia parallax (see section 3.1).

Table 5: The luminosity and log$g$ derived using Gaia parallaxes and V band magnitudes (see section 5.1)of the SLR stars in this study.  Object name | Av | $\pi$ | $\log\left(\frac{L}{L_{\odot}}\right)$ | logg
---|---|---|---|---
| (mag) | (mas) |  | (dex)
UCAC4 253-045343 | 0.28 | 0.13 | 3.17$\pm$0.16 | 0.75$\pm$0.25
UCAC4 099-098976 | 0.08 | 0.37 | 1.73$\pm$0.12 | 2.56$\pm$0.16
UCAC4 212-183136 | 0.09 | 0.05 | 3.50$\pm$0.22 | 0.10$\pm$0.24
UCAC4 308-077592 | 0.24 | 0.05 | 3.66$\pm$0.18 | 0.33$\pm$0.21
TYC 7262-250-1 | 0.16 | 0.18 | 3.18$\pm$0.20 | 0.57$\pm$0.23

The luminosity values derived here agree well with the values given in GALAH
DR3 VAC within the estimated uncertainties except for one star, UCAC4
099-098976. The HR diagram of the data set from GALAH DR3 (See Fig. 1) shows a
well-defined main sequence, sub-giant and red giant branch. Also, one could
notice two groups of stars on a red giant branch; one is identified as the
luminosity bump and the other as a red clump or horizontal branch. Of the five
giants in our sample, one overlaps with the red clump region, and the four
seem to be much brighter and closer to the early AGB.

###  5.2 Asteroseismic analysis

Asteroseismology has emerged as a valuable tool in distinguishing core helium-
burning stars from hydrogen-shell-burning RGBs. The turbulent outer layers of
RGBs display stochastically excited oscillations, which could be detected from
high-precision time series data of long duration, typically facilitated by
space missions such as Kepler (Borucki et al., 2010), CoROT (Michel et al.,
2008) and TESS (Ricker et al., 2015). Two distinct types of oscillations are
seen in stars: pressure ($p$-mode) or acoustic mode, typically observed in the
outer envelope of the star, and the gravity ($g$-mode) mode, predominantly
found in the stellar core. Using the analysis of the oscillations associated
with the $p$\- and $g$-modes in red giants, one could derive two
characteristic parameters to distinguish giants with He-inert core, RGB
giants, from those of He-core burning red clump giants. The two parameters are
large frequency separation ($\Delta\nu$ of $P$-modes and the average period
spacing ($\Delta$P) of mixed modes arising from coupling between the interior
$g$-mode and the envelope $p$-mode oscillations.

For our analysis, we made use of data from NASA’s Transiting Exoplanet Survey
Satellite (TESS) from the Mikulski Archive for Space Telescopes (MAST) archive
(https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html). We
systematically removed points from the light curves with a quality flag
greater than 0 or NaN flux values to ensure good data quality. Although this
resulted in removing some data points, which can impact seismic analysis — the
number of such excluded points remained minimal compared to the overall data
size. To rectify these sudden jumps in measured flux, we applied correction
using the TESS Asteroseismic Science Operations Center (TASOC) pipeline
(Handberg et al., 2021). This pipeline employed a piece-wise cubic Hermite
polynomial to interpolate the gaps, ensuring a more continuous and consistent
light curve. Finally, all corrected light curves for a single object were
stitched together using the lightkurve package (Lightkurve Collaboration et
al., 2018). The resultant light curves were converted to periodograms for
seismic analysis using the Lomb-Scargle periodogram technique. The power
density spectrum (PSD) is subjected to background removal using a log-median
filter. The PSD is divided by the estimated background noise to create a
”Signal-to-noise spectrum”.

We selected a small window in the background corrected PSD showing clear power
excess, and the Auto-Correlation Function (ACF) is computed and integrated
over this frequency range. The resulting collapsed ACF exhibits a distinct
peak. The frequency value corresponding to the highest value of the collapsed
ACF gives $\nu_{max}$. ACF convolves data with a lagged version of itself, and
when oscillation modes overlap each other, spikes are seen in the ACF.
Empirical relation given by Stello et al. (2009) gives a rough estimate of
$\Delta\nu$

$\Delta\nu=(0.263\pm 0.009)\nu_{\text{max}}^{(0.772\pm 0.005)}\,\mu\text{Hz}$
(5)

Lightkurve identifies the peak of the ACF nearest to this estimate and reports
it as $\Delta\nu$. Owing to the shorter baseline time-series data provided by
TESS, the signal-to-noise ratio (S/N) is comparatively low. Hence for the
detection of significant oscillation peaks, we have used only frequencies
corresponding to the standard criterion of S/N $>$ 3-4 (Breger et al., 1993;
Li et al., 2019). The same background-corrected PSD is used to identify
several consecutive dipole modes and estimate the median period spacing
$\Delta P$. Measurement of $\Delta$P of each star is demonstrated in Fig. 4

|
---|---
|
|
Figure 4: Period spacing measurements for five SLR giants in this study.
Figure 5:  Asteroseismic plot of $\Delta$P - $\Delta\nu$. The dots with
respective colours in the background represent stars classified according to
evolutionary phases following the classification scheme given in (Bedding et
al., 2011). RGB stars are marked as olive circles, and RC stars are
represented by orange circles. The five SLRs from this study (marked as red
star symbol) show $\Delta$P and $\Delta\nu$ similar to RC stars.

Fig. 5 shows the $\Delta$P-$\Delta\nu$ diagram of our sample. The RGB stars
have a much denser core than core He-burning RC stars. Hence RC stars exhibit
a more pronounced coupling in the mixed modes which leads to larger $\Delta$P
values. Thus period spacing is a distinctive characteristic that provides a
means to distinguish between these distinct phases of stellar evolution
(Bedding et al., 2011). We consider $\Delta$P $>$ 100s as the RC threshold
(Stello et al., 2013). The five giants are shown in $\Delta$P-$\Delta\nu$
asteroseismic diagram. All five giants fall in the region occupied by giants
of the post-He-flash phase with He-burning at the center, and none in the RGB
region. Note, Martell et al. (2021) list four of these in their catalog as Li-
rich RGB giants using stellar isochrones.

Stellar parameters: mass, radius, log $g$ and Luminosity : Another significant
application of asteroseismology is the determination of fundamental stellar
parameters such as mass, radius, and log $g$. They can be estimated using two
seismic scaling relations(Kjeldsen & Bedding, 1995):

$\frac{M}{M_{\odot}}\approx\left(\frac{\nu_{\text{max}}}{\nu_{\text{max},\odot}}\right)^{3}\left(\frac{\Delta\nu}{\Delta\nu_{\odot}}\right)^{-4}\left(\frac{T_{\text{eff}}}{T_{\text{eff},\odot}}\right)^{3/2}$
(6)
$\frac{R}{R_{\odot}}\approx\left(\frac{\nu_{\text{max}}}{\nu_{\text{max},\odot}}\right)\left(\frac{\Delta\nu}{\Delta\nu_{\odot}}\right)^{-2}\left(\frac{T_{\text{eff}}}{T_{\text{eff},\odot}}\right)^{1/2}$
(7)
$\frac{g}{g_{\odot}}\approx\left(\frac{\nu_{\text{max}}}{\nu_{\text{max},\odot}}\right)\left(\frac{T_{\text{eff}}}{T_{\text{eff},\odot}}\right)^{1/2}$
(8)

From Stefan-Boltzmann law, luminosity can be approximated as

$\frac{L}{L_{\odot}}=\left(\frac{R}{R_{\odot}}\right)^{2}\left(\frac{T_{\text{eff}}}{T_{\odot}}\right)^{4}$
(9)

.

Table 6: Asteroseismic parameters of sample giants Object name | $\nu_{max}$ | $\Delta\nu$ |  $\Delta$P | Mass | Radius | log $g$ | $\log\left(\frac{L}{L_{\odot}}\right)$
---|---|---|---|---|---|---|---
| ($\mu$Hz) | ($\mu$Hz) | (s) | (M⊙) | (R⊙) | (dex) |
UCAC4 253-045343 | 36.57$\pm$ 1.09 | 4.11$\pm$0.94 | 194.99$\pm$5.90 | 1.17$\pm$0.53 | 10.84$\pm$3.03 | 2.44$\pm$0.01 | 1.47$\pm$0.28
UCAC4 099-098976 | 40.62$\pm$2.17 | 5.08$\pm$0.75 | 203.34$\pm$8.02 | 1.04$\pm$0.24 | 9.11$\pm$ 1.38 | 2.54$\pm$0.01 | 1.76$\pm$0.17
UCAC4 212-183136 | 31.55$\pm$1.12 | 4.38$\pm$0.61 | 182.46$\pm$15.74 | 0.57$\pm$0.12 | 8.14 $\pm$ 1.11 | 2.37$\pm$0.01 | 1.22$\pm$ 0.17
UCAC4 308-077592 | 45.62$\pm$3.39 | 4.58$\pm$0.25 | 173.98$\pm$8.16 | 1.40$\pm$0.04 | 10.62 $\pm$ 0.34 | 2.53$\pm$0.01 | 1.46$\pm$ 0.09
TYC 7262-250-1 | 37.99$\pm$1.69 | 4.84$\pm$0.88 | 145.42$\pm$6.52 | 0.72$\pm$0.18 | 8.31$\pm$ 1.33 | 2.46$\pm$0.01 | 1.29$\pm$ 0.19

The solar reference values are $\nu_{\text{max},\odot}$ = 3090 $\mu$Hz,
$\Delta\nu_{\odot}$ = 135.1 $\mu$Hz (Huber et al., 2010) and
$T_{\text{eff},\odot}$ = 5777.2K (Prša et al., 2016) Although scaling
relations provide efficient estimation of these parameters, greater accuracy
can be achieved through asteroseismic grid modelling. This becomes more
pertinent when examining metal-poor stars ([Fe/H] $<$ -1), as scaling
relations tend to yield mass estimates that are inflated by roughly 16%
(Epstein et al., 2014). In this work, we have used grid modelling to measure
mass, radius and log $g$. Grid modelling was performed using version 0.0.6 of
the ASFGRID code (Sharma et al., 2016; Stello & Sharma, 2022). Derived values
for all stars are provided in Table 6. The log $g$ values from the
asteroseismic analysis are significantly higher for giants compared to values
derived from both the Gaia parallax and in this study except for one star
which is at RC, where all the methods yield the same log$g$ or the luminosity.

##  6 Discussion

The new metal-poor SLRs, along with the known SLRs, are shown in a plot of log
$g$ and $T_{\rm eff}$ (Fig. 6: left panel). We have also shown a few metal-
rich SLRs (Singh et al., 2021), which have been classified as red clump giants
using asteroseismic analysis. Two metal-poor SLRs (one from our study and the
other from Li et al. (2018)) fall in the RC region, and the rest are much
brighter, overlapping with the early AGB space in the HR diagram. We lack
asteroseismically determined evolutionary phases for the already known metal-
poor SLRs from the literature (see Fig 5). Since all five in this study are in
the core-He-burning phase, similar to many SLRs in the metal-rich regime, it
is reasonable to assume all the known metal-poor SLRs are also in the post-He-
flash phase, and none are in the RGB phase. The same sample is shown in the
right panel of Fig 6, i.e., in a plot of log $g$ and A(Li), along with
standard model predictions for Li abundances computed using MESA stellar
evolutionary code (Modules for Experiments in Stellar Astrophysics) for 1M⊙
with two representative values of [Fe/H]. As shown in the figure, Li only gets
depleted as giants ascend the RGB, and the value reaches as low as A(Li) =
-2.0 dex for metal-poor giants on reaching the red clump phase. This implies
that the existence of SLRs at evolved phases, either at RC or at the early
AGB, does not comply with the existing theoretical models.

The sample SLRs in Fig.6 fall into two distinctive groups: one at RC near log
$g$ $\sim$ 2.5 and the other group at higher luminosity, log $g$ $\sim$ 1.0.
As per metal-rich Li-rich giants at RC, there are multiple evidences that Li
enrichment occurs during the He-flash as the He-flash is the only major
stellar event before stars arrive on RC (Singh et al., 2019; Kumar et al.,
2020; Singh et al., 2021; Mallick et al., 2023). It is also found that the Li-
richness among giants is a transient phenomenon (Singh et al., 2021) as Li
starts depleting while they are on RC, which explains why the SLRs are rare.
The 2nd group of SLRs at higher luminosity, beyond the RC phase, is
interesting. The origin of high Li in these stars is not understood, but their
existence suggests multiple mechanisms may be responsible for high Li in
giants at different evolutionary phases. Here, we discuss three possibilities
for the 2nd group in the HR diagram:

1\. One hypothesis could be that these giants are transitioning to RC post-He-
ignition at the RGB tip. The location of stars in the HRD and their very high
Li abundances do not rule out this possibility. The very high Li and the
asteroseismic parameter, the average period spacing ($\Delta P$), imply that
these giants have undergone He-flash very recently, which has been well
demonstrated in the case of metal-rich Li-rich giants (see Singh et al.,
2021). If the He-flash is the universal origin of high Li in giants, these
giants must be in the transition to RC.

Figure 6:  The five metal-poor SLRs in this study (star symbols) are shown in
the left panel along with the known metal-poor SLRs ( crosses) from (Sitnova
et al., 2023; Kowkabany et al., 2022; Sanna et al., 2020; Li et al., 2018; Yan
et al., 2018; Kirby et al., 2012; Ruchti et al., 2011; Kraft et al., 1999),
metal-rich SLRs ( circles) from (Singh et al., 2021), and two representative
evolutionary tracks for [Fe/H]=-2.5 (dot-dashed line) and [Fe/H]=0.0
(continuous line). The colour code of the tracks indicates the evolutionary
phases; core H-burning (cyan), shell H-burning ( orange), core He-burning,
(magenta), and RGB luminosity bump (black). In the right panel, the same
sample is shown along with the predicted MESA Li evolutionary models for two
representative [Fe/H] values. The colour code on model tracks is the same as
in the left panel.

Other than these, we do not have any other observable signature to distinguish
a giant, whether it is descending to the RC or evolving off from the RC phase
as the evolutionary paths for a star descending towards RC and ascending
towards the early AGB overlap in the HR diagram.

However, given the time scales involved, the likelihood of finding stars
during the transition may be very small. The giants are expected to take about
2 Million years to fully convert the degenerate He-core into a convective He-
core burning phase of RC. It is unclear whether the main He-flash causes the
giants’ sudden drop in luminosity to RC level. If so, we expect the transition
period to be about a few hundred days. The subsequent sub-flashes continue in
stars at RC, converting the degenerate core into a fully convective core He-
burning. Here, we can invoke the analogy of a typical Supernovae Ia (SN Ia)
from low-mass stars whose light curves show a drop in brightness by a factor
of 10-20 from their peak brightness within about 100 days (Pastorello et al.,
2007; Pignata et al., 2008). The only difference is that the star gets
disintegrated in the case of an SN explosion, and in the case of He-flash, the
star is intact except for a sudden drop in the star’s luminosity to RC level
due to a shrink in the star’s size. We ran the MESA model of 1M⊙ with [Fe/H] =
-2.5 and examined its evolution from the tip of RGB to the RC. We found that
the transient period is about 500 days. Taking an SLR phase of about 2 Myrs
(see (Singh et al., 2021)), we find that the probability of finding SLRs
during the transition to RC is about one in 2 Myrs. In the case of giants,
Deepak & Reddy (2019) found 20 SLRs among the 51,982 RC giants, which is about
one in 2500 or 0.04$\%$ probability. However, none were found during the short
transition phase from the RGB tip to RC. On the other hand, we found in this
study four SLRs out of a total sample of 1038 metal-poor giants, i.e. one in
250 giants or 0.4$\%$, which is much larger than the expected probability of
detecting SLRs during the transition period or even a factor 10 larger than
SLRs among RC giants. The much larger percentage of SLRs among metal-poor
giants implies that these four giants may not be in the transition to RC. The
higher percentage of SLR among metal-poor giants also indicates SLR phase may
last for longer among metal-poor giants compared to their counterparts in the
metal-rich regime. Or Li-rich origin among metal-poor giants may differ from
metal-rich giants.

2\. The second possibility is that the giants evolved from the RC phase to
early AGB, with a carbon-oxygen core surrounded by He- and H-burning shells.
Probably they have evolved from RC, and their high Li abundance may have a
different origin other than the He-flash. Unfortunately, we do not have
asteroseismic signatures for differentiating giants at RC from those that
evolved off to the AGB phase. The normal C and $s$-process elements (see Table
3), signatures of 3rd-dredge-up, indicate the giants are still at the early
AGB phase and yet to undergo 3rd-dredge-up. It is not clear how these giants
became enriched with Li. Is the high Li inherited from the RC or produced
$insitu$ at the early AGB? Indeed, the high Li cannot be from the RC phase,
where most Li produced during the preceding He-flash event might have been
destroyed. Singh et al. (2021) demonstrated that Li gets depleted rapidly and
set a conservative upper limit of 40 Myrs for the SLR phase to last since the
He-ignition began at the RGB tip. They argued that since only about 0.3 to 0.5
$\%$ of SLRs among RC giants, Li must be depleted rapidly. Thus, the high Li
in the 2nd group may have a different origin other than the He-flash. Many
studies argued that the high Li among low-mass giants could be due to cool
bottom processing (CBP) (Sackmann & Boothroyd, 1999; Ruchti et al., 2011) in
which additional mixing could occur between radiative layers beneath the deep
convective envelope reaching by-products from H-burning shell. Though the
physical mechanism of such CBP is poorly understood, it was invoked for high
Li seen in low-mass early AGB stars (Abia & Isern, 2000).

3\. The third possibility is the external origin, like the merger scenario
explored by Izzard et al. (2007); Zhang & Jeffery (2013) to explain high Li
among low mass R- or J-type stars. Zhang & Jeffery (2013) suggests that the
merger with He white dwarf (HeWD) could produce single stars with high Li and
similar luminosity to those in the 2nd group. The merger models do not expect
enhancement of $s$-process elements and also no enhancement in carbon if the
merger involved a red giant and low mass HeWD. Piersanti et al. (2010)
conducted a three-dimensional smoothed particle hydrodynamics (3D SPH)
simulation of a merger between a low-mass HeWD and an RGB star, and they
identified inefficient helium burning, resulting in no carbon enhancement.
Zhang & Jeffery (2013) also obtained comparable outcomes from their one-
dimensional post-merger calculations using low-mass HeWD models. The study by
Zhang et al. (2020) for a wide range of progenitors, mass binaries revealed
that the post-merger abundances are a function of the mass of HeWDs. They
postulated that Li-rich giants could form from mergers of low-mass HeWDs,
(0.35M⊙ $\leq$ MWD $\leq$0.40 M⊙) with a low-mass RGB star. Evidence for
stellar merger could be the presence of IR excess and dust. Unfortunately,
only NIR data for these stars in the $2MASS$ JHKs bands is available, and
their spectral energy distributions (SEDs) exhibit no IR excess. Far-infrared
data is essential to confirm IR excess.

Interestingly, we found that luminosity values derived from asteroseismic
analysis for the four SLRs are significantly lower than those found from Gaia
parallaxes. However, for the RC giant UCAC4 099-098976, the luminosity values
derived from both methods are the same (see Table 5 and Table 6). We also
checked the luminosity values derived from both methods for normal giants with
little or no Li at the early-AGB and found no difference in the luminosity
values. It is unclear at this point whether the difference could be due to the
merger history of these SLRs. A merger could manifest the giant with higher
luminosity due to increased mass and size. The increased mass may not affect
the dense degenerate core at the centre; hence, there is little or no change
in oscillations from the pre-merger giant. This implies that the four SLRs at
higher luminosity probably had a merger history and not the one at RC.
Unfortunately, the absence of TESS/Kepler data hindered our ability to confirm
the evolutionary origin of two SLRs studied by Ruchti et al. (2011) — whether
they result from the evolution of single stars or involved a merger event.

##  7 Conclusion

In this study, we analysed photometric, spectroscopic and asteroseismic data
of five Super Li-rich giants. Among the five, one star is in the red clump
phase, whereas the other four lie at higher luminosity near the early AGB. By
comparing the existing metal-poor giants, we found two distinct groups of
metal-poor SLRs: one at RC luminosity and the other at high luminosity near
the early AGB. The SLRs at RC most likely originated from the He-flash,
similar to metal-rich SLRs. The origin of SLRs at early-AGB may be either
$insitu$ through cool bottom processing or external merger-induced
nucleosynthesis and dredge-up. The significant difference between the
luminosity values derived from Gaia parallaxes and the asteroseismology
indicates some kind of merger events for the SLRs at higher luminosity values.
We also discussed whether these giants are in the transition to the RC, post
He-flash, and found it is unlikely as the observed SLR percentage among the
metal-poor giants is much higher compared to the expected SLRs during the
transition or among RC giants.

Being the poorly explored population, large statistically uniform samples are
required to better understand the evolutionary phase and Li production. The
metal-poor Li-rich giants are crucial for understanding how the metallicity
affects the Li production in giants. Also, it is important to measure other
key chemical abundance ratios such as ${}^{12}C/^{13}C$ and C/N to understand
the dredge-up process and evolutionary phase.

##  8 acknowledgments

This work made use of the Third Data Release of the GALAH Survey (Buder et
al., 2021). The GALAH Survey is based on data acquired through the Australian
Astronomical Observatory, under programs: A/2013B/13 (The GALAH pilot survey);
A/2014A/25, A/2015A/19, A2017A/18 (The GALAH survey phase 1); A2018A/18 (Open
clusters with HERMES); A2019A/1 (Hierarchical star formation in Ori OB1);
A2019A/15 (The GALAH survey phase 2); A/2015B/19, A/2016A/22, A/2016B/10,
A/2017B/16, A/2018B/15 (The HERMES-TESS program); and A/2015A/3, A/2015B/1,
A/2015B/19, A/2016A/22, A/2016B/12, A/2017A/14 (The HERMES K2-follow-up
program). We acknowledge the traditional owners of the land on which the AAT
stands, the Gamilaraay people, and pay our respects to elders past and
present. This paper includes data that has been provided by AAO Data Central
(https://datacentral.org.au). All lightcurves for seismic analysis were
obtained from the public data archive at MAST
(https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html). This research
made use of the open-source python packages Lightkurve
222https://github.com/lightkurve/lightkurve (Lightkurve Collaboration et al.,
2018) and pySYD 333https://github.com/ashleychontos/pySYD (Chontos et al.,
2022). This work has also made use of data from the European Space Agency
(ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia
Data Processing and Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has
been provided by national institutions, in particular, the institutions
participating in the Gaia Multilateral Agreement. This research has made use
of the SIMBAD database, operated at CDS, Strasbourg, France. (Wenger et al.,
2000). This research also has made use of the NLTE data obtained from the
INSPECT database, version 1.0 (www.inspect-stars.net) 444http://www.inspect-
stars.com/

## Lightkurve results for sample giants

|
---|---
Figure 7: UCAC4 253-045343

Results of Lightkurve analysis of the star UCAC4 253-045343. The left subplot
has 3 panels - The top panel displays the signal-to-noise ratio (SNR)
periodogram. The vertical red line indicates $\nu_{max}$. Middle Panel: In the
middle panel, we apply a two-dimensional ACF to different segments of the
periodogram. Lower Panel: In the bottom panel, we present the mean collapsed
ACF as a function of the central frequency of each segment. A Gaussian curve
(shown in blue) is the smoothened collapsed ACF. The right subplot has two
panels - The upper panel displays the frequency region over which ACF is
evaluated. In the lower panel, we present the ACF itself. This is the outcome
of computing the correlation between the data and itself while it is
progressively shifted over itself.

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