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.