VARIABLE STARS IN ARCHIVAL SCHMIDT CAMERA OBSERVATIONS:  CROSS-MATCHING AND ML CLASSIFICATION

I. Izmailova; A. Umirbayeva; S. Shomshekova; L. Aktay

doi:10.31489/2026N2/156-164

Authors

DOI:

https://doi.org/10.31489/2026N2/156-164

Keywords:

archival astronomical data, variable stars, catalog cross-matching, machine learning, photometric observations

Abstract

Digitized archival astronomical observations provide a valuable basis for the study of variable stars and their long-term behavior. This work presents an analysis of variable stars based on archival photometric data obtained with a Schmidt camera at the Fesenkov Astrophysical Institute during the period from 1960 to 1989. The detected objects were cross-matched with existing catalogs of variable stars and modern astrometric and photometric databases, allowing the compilation of an extended set of stellar parameters that had not previously been combined within a single dataset. To complement incomplete catalog information, machine learning methods were applied to classify stellar variability types and spectral classes using available astrometric and photometric features. Several classification models were tested, and the most stable and accurate approach was selected for further analysis. The resulting catalog integrates archival observations, catalog cross-identification results, and predicted stellar characteristics, providing an extended resource for studies of stellar variability and long-term photometric evolution.

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VARIABLE STARS IN ARCHIVAL SCHMIDT CAMERA OBSERVATIONS: CROSS-MATCHING AND ML CLASSIFICATION