arXiv subjects

Pradosh Barun Das

Publications and source records attributed to Pradosh Barun Das.

3 recordsLinked to original sources

arXiv2026

The GALAH Survey: Neutron-Capture Elemental Abundances for 350,000 Gaia-RVS spectra and the Chemodynamics of Accreted Structures

We present a comprehensive data-driven spectroscopic analysis of 357,415 red giant stars using Gaia DR3 Radial Velocity Spectrometer (RVS) spectra (8460-8700 A; $R\approx11,500$), aimed at deriving homogenous stellar parameters and elemental abundances (collectively referred to as stellar labels). We employ The Cannon, a generative model based on 2747 giants in common with GALactic Archaeology with HERMES (GALAH) DR4, adopting GALAH labels ($R\approx28,000$) for training. The resulting model predicts 11 stellar labels for RVS giants: effective temperature ($T_{\rm eff}$), surface gravity ($\log g$), projected rotational velocity ($v\sin i$), and abundances of [Fe/H], [Ca/Fe], [Si/Fe], [Ni/Fe], [Ti/Fe], as well as the neutron-capture elements [Zr/Fe], [Ce/Fe], and [Nd/Fe]. Building on these results, we develop a probabilistic framework to chemically identify debris from the Gaia-Sausage-Enceladus (GSE) accretion event. A logistic regression classifier, optimized via Markov chain Monte Carlo sampling and trained on a small reference sample of GSE members and comparison stars, identifies stars with high GSE membership probabilities based solely on their chemical abundances, with the resulting candidates exhibiting distinctive abundance-ratio patterns, including [Ca/Ti], [Ti/Ce], and [Nd/Zr]. Applying independent kinematic constraints yields a robust sample of GSE candidates, demonstrating that the characteristic chemical signatures remain consistent after applying these constraints. This work demonstrates the potential of data-driven analysis techniques to extract detailed chemical information from medium-resolution spectra and establishes a framework for tracing Galactic accretion events using chemical abundances.

astro-ph.GA↗

arXiv2025

Optical spectroscopic signatures of the red giant evolutionary state

Modern spectroscopic surveys output large data volumes. Theoretical models provide a means to transform the information encoded in these data to measurements of physical stellar properties. However, in detail the models are incomplete and simplified, and prohibit interpretation of the fine details in spectra. Instead, the available data provide an opportunity to use data-driven, differential analysis techniques, as a means towards understanding spectral signatures. We deploy such an analysis to examine core helium-fusing red clump (RC) and shell hydrogen-fusing red giant branch (RGB) stars, to uncover signatures of evolutionary state imprinted in optical stellar spectra. We exploit 786 pairs of RC and RGB stars from the GALAH survey, chosen to minimise spectral differences, with evolutionary state classifications from TESS and K2 asteroseismology. We report sub-percent residual, systematic spectral differences between the two classes of stars, and show that these residuals are significant compared to a reference sample of RC$-$RC and RGB$-$RGB pairs selected using the same criteria. First, we report systematic differences in the Swan ($\rm{C}_2$) band and CN bands caused by stellar evolution and a difference in mass, where RGB stars at similar stellar parameters have higher masses than RC stars. Secondly, we observe systematic differences in the line-width of the H$_α$ and H$_β$ lines caused by a difference in microturbulence, as measured by GALAH, where we measure higher microturbulence in RC stars than RGB stars. This work demonstrates the ability of large surveys to uncover the subtle spectroscopic signatures of stellar evolution using model-free, data-driven methods.

astro-ph.SR↗

arXiv2025

The GALAH Survey: Stellar parameters and abundances for 800,000 Gaia RVS spectra using GALAH DR4 and The Cannon

Analysing stellar parameters and abundances from nearly one million Gaia Data Release 3 (DR3) Radial Velocity Spectrometer (RVS) spectra poses challenges due to the limited spectral coverage (restricted to the infrared Ca II triplet) and variable signal-to-noise ratios of the data. To address this, we use The Cannon, a data-driven method, to transfer stellar parameters and abundances from the GALAH (GALactic Archaeology with HERMES) DR4 (R ~ 28,000) catalogue to the lower resolution Gaia DR3 RVS spectra (R ~ 11,500). Our model, trained on 14,484 common targets, predicts parameters such as $T_{\text{eff}}$, $\log g$, and $[\text{Fe/H}]$, along with several other elements across approximately 800,000 Gaia RVS spectra. We utilise stars from open and globular clusters present in the Gaia RVS catalogue to validate our predicted mean $[\text{Fe/H}]$ with high precision (~0.02-0.10 dex). Additionally, we recover the bimodal distribution of $[\text{Ti/Fe}]$ versus $[\text{Fe/H}]$, reflecting the high and low $α$-components of Milky Way disc stars, demonstrating The Cannon's capability for accurate stellar abundance determination from medium-resolution Gaia RVS spectra. The methodologies and resultant catalogue presented in this work highlight the remarkable potential of the RVS dataset, which by the end of the Gaia mission will comprise spectra of over 200 million stars.

astro-ph.GA↗