Minute-cadence observations of the LAMOST Fields with the TMTS – V. Machine learning classification of TMTS catalogues of periodic variable stars.

Periodic variables are always of great scientific interest in astrophysics. Thanks to the rapid advancement of modern large-scale time-domain surveys, the number of reported variable stars has experienced substantial growth for several decades, which significantly deepened our comprehension of stellar structure and binary evolution. The Tsinghua University-Ma Huateng Telescopes for Survey (TMTS) has started to monitor the LAMOST sky areas since 2020, with a cadence of 1 min. During the period from 2020 to 2022, this survey has resulted in densely sampled light curves for ~30 000 variables of the maximum powers in the Lomb–Scargle periodogram above the 5σ threshold. In this paper, we classified 11 638 variable stars into six main types using xgboost and Random Forest classifiers with accuracies of 98.83 per cent and 98.73 per cent, respectively. Among them, 5301 (45.55 per cent) variables are newly discovered, primarily consisting of δ Scuti stars, demonstrating the capability of TMTS in searching for short-period variables. We cross-matched the catalogue with Gaia 's second Data Release and LAMOST's seventh Data Release to obtain important physical parameters of the variables. We identified 5504 δ Scuti stars (including 4876 typical δ Scuti stars and 628 high-amplitude δ Scuti stars), 5899 eclipsing binaries (including EA-, EB-, and EW-type), and 226 candidates of RS Canum Venaticorum. Leveraging the metal abundance data provided by LAMOST and the Galactic latitude, we discovered eight candidates of SX Phe stars within the class of 'δ Scuti stars'. Moreover, with the help of Gaia colour–magnitude diagram, we identified nine ZZ Ceti stars. [ABSTRACT FROM AUTHOR]