A search for new symbiotic stars in the Milky Way

Machine-learning techniques applied to photometric databases

¹ Departamento de Astronomía, Universidad de La Serena, Raul Bitran, 1720256 La Serena, Chile
² Instituto de Astronomía y Ciencias Planetarias, Universidad de Atacama, Copayapu 485 Copiapó, Chile
³ Millennium Institute of Astrophysics (MAS), Nuncio Monseñor Sotero Sanz 100, Of. 104 Providencia, Santiago, Chile
⁴ Department of Physics, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy
⁵ Gemini Observatory/NSF’s NOIRLab, Casilla 603 La Serena, Chile
⁶ Universidad Nacional de Hurlingham (UNAHUR), Secretaría de Investigación, Av. Gdor. Vergara 2222, Villa Tesei, Buenos Aires, Argentina
⁷ Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
⁸ Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de San Juan, Av. Ignacio de la Roza 590 (O), Complejo Universitario “Islas Malvinas”, Rivadavia J5402DCS, San Juan, Argentina
⁹ Instituto de Ciencias Astronómicas, de la Tierra y del Espacio (ICATE-CONICET), C.C 467, 5400 San Juan, Argentina

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 15 July 2025
Accepted: 9 February 2026

Abstract

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 multiband 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 nonsymbiotic stars. To mitigate class imbalance and improve the 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. We identified 990 candidates in this sample 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, and metallicity and complemented the cuts 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 ultraviolet excesses consistent with the properties of S-type symbiotic systems.

Conclusions. To evaluate the model performance, we applied it to a validation set of symbiotic stars recently confirmed in the literature. We recovered 92.3% of them. This result supports the effectiveness and generalizability of our classification approach.