Barium stars as tracers of s-process nucleosynthesis in AGB stars

II. Using machine learning techniques on 169 stars

¹ Konkoly Observatory, Research Centre for Astronomy and Earth Sciences (CSFK), ELKH, Konkoly Thege M. út 15–17, 1121 Budapest, Hungary
e-mail: ayaguelopez@lanl.gov
² CSFK, MTA Centre of Excellence, Konkoly Thege Miklós út 15–17, Budapest 1121, Hungary
³ Computer, Computational and Statistical Sciences (CCS) Division, Center for Theoretical Astrophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
⁴ MTA-ELTE Lendület “Momentum” Milky Way Research Group, Hungary
⁵ E. A. Milne Centre for Astrophysics, University of Hull, Hull, 7RX, UK
⁶ ELTE Eötvös Loránd University, Institute of Physics, Pázmány Péter sétány 1/A, Budapest 1117, Hungary
⁷ Observatório Nacional/MCTI, Rua General José Cristino, 77, 20921-400, Rio de Janeiro, Brazil
⁸ Laboratory of Observational Astrophysics, Saint Petersburg State University, Universitetski pr. 28, 198504, Saint Petersburg, Russia
⁹ Laboratório Nacional de Astrofísica/MCTI, Rua dos Estados Unidos 154, Bairro das Nações, 37504-364, Itajubá, Brazil
¹⁰ School of Physics and Astronomy, Monash University, VIC 3800, Australia
¹¹ Joint Institute for Nuclear Astrophysics – Center for the Evolution of the Elements, 640 S Shaw Lane, East Lansing MI 48824, USA

Received: 4 June 2022
Accepted: 28 November 2022

Abstract

Context. Barium (Ba) stars are characterised by an abundance of heavy elements made by the slow neutron capture process (s-process). This peculiar observed signature is due to the mass transfer from a stellar companion, bound in a binary stellar system, to the Ba star observed today. The signature is created when the stellar companion is an asymptotic giant branch (AGB) star.

Aims. We aim to analyse the abundance pattern of 169 Ba stars using machine learning techniques and the AGB final surface abundances predicted by the FRUITY and Monash stellar models.

Methods. We developed machine learning algorithms that use the abundance pattern of Ba stars as input to classify the initial mass and metallicity of each Ba star’s companion star using stellar model predictions. We used two algorithms. The first exploits neural networks to recognise patterns, and the second is a nearest-neighbour algorithm that focuses on finding the AGB model that predicts the final surface abundances closest to the observed Ba star values. In the second algorithm, we included the error bars and observational uncertainties in order to find the best-fit model. The classification process was based on the abundances of Fe, Rb, Sr, Zr, Ru, Nd, Ce, Sm, and Eu. We selected these elements by systematically removing s-process elements from our AGB model abundance distributions and identifying the elements whose removal had the biggest positive effect on the classification. We excluded Nb, Y, Mo, and La. Our final classification combined the output of both algorithms to identify an initial mass and metallicity range for each Ba star companion.

Results. With our analysis tools, we identified the main properties for 166 of the 169 Ba stars in the stellar sample. The classifications based on both stellar sets of AGB final abundances show similar distributions, with an average initial mass of M = 2.23 M_⊙ and 2.34 M_⊙ and an average [Fe/H] = −0.21 and −0.11, respectively. We investigated why the removal of Nb, Y, Mo, and La improves our classification and identified 43 stars for which the exclusion had the biggest effect. We found that these stars have statistically significant and different abundances for these elements compared to the other Ba stars in our sample. We discuss the possible reasons for these differences in the abundance patterns.