Science and Technology Production
Article
Authorship
V. Contreras Rojas
;
M. Jaque Arancibia
;
C.E. Ferreira Lopes
;
N. Monsalves
;
R. Angeloni
;
G. J. M. Luna
;
V. Marels
;
D. Concha
;
N. E. Nuñez
;
C. Saffe
;
FLORES TRIVIGNO, MATIAS GASTON
Date
2026
Publishing House and Editing Place
EDP SCIENCES S A
Magazine
ASTRONOMY AND ASTROPHYSICS
EDP SCIENCES S A
Summary
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Context. Symbiotic stars are interacting binary systems composed of a red giant transferring material to a hot compact star, typically a white dwarf. These systems are crucial for studying stellar evolution, accretion processes, mass transfer, and a variety of complex astrophysical phenomena. However, there is a significant discrepancy between the number of confirmed symbiotic stars (∼ 300) and the estimated population in the Milky Way (1.2 × 103 − 1.5 × 104), suggesting t...
Context. Symbiotic stars are interacting binary systems composed of a red giant transferring material to a hot compact star, typically a white dwarf. These systems are crucial for studying stellar evolution, accretion processes, mass transfer, and a variety of complex astrophysical phenomena. However, there is a significant discrepancy between the number of confirmed symbiotic stars (∼ 300) and the estimated population in the Milky Way (1.2 × 103 − 1.5 × 104), suggesting that a large fraction remains undetected. Aims. To address this issue, we propose the identification of new symbiotic stars through the application of machine learning techniques. Our approach combines multi-band photometric data from Gaia DR3, 2MASS, and WISE, together with parallax measurements and the pseudo-equivalent width of Hα, to effectively distinguish symbiotic candidates from other stellar populations. Methods. We trained a Random Forest model using a sample of 166 confirmed S-type symbiotic stars and a control sample of 1600 non-symbiotic stars. To mitigate class imbalance and improve classification performance, we applied the Synthetic Minority Oversampling Technique (SMOTE). The model achieved an F1-score of 89% for the symbiotic class. Results. We applied our model to a catalog of approximately 2.5 million stars selected based on photometric colors consistent with those of S-type symbiotic stars. From this sample, 990 candidates were identified with a classification probability of at least 70%. To refine the selection, we applied statistically and physically motivated cuts based on effective temperature, surface gravity, metallicity and complemented by SkyMapper photometry. This process yielded 12 high-confidence candidates, characterized by cool temperatures, low surface gravities, solar-like metallicity, Hα emission, luminosities ranging from moderate to high, and UV excesses consistent with the properties of S-type symbiotic systems. Conclusions. To evaluate the model’s performance, we applied it to a validation set of symbiotic stars recently confirmed in the literature, recovering 92.3% of them. This result supports the effectiveness and generalizability of our classification approach.
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Key Words
MODELPHOTOMETRYSMOTESYMBIOTIC