A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF

TL;DR

This study develops a statistical model to identify variable sources in LAMOST data by leveraging multi-catalog light curves, resulting in a credible catalog of over 600,000 candidates validated against existing catalogs.

Contribution

It introduces a novel statistical modeling approach for variable source identification in LAMOST data, improving the completeness and reliability of variable source catalogs.

Findings

Created a catalog of 631,769 variable source candidates with >95% probability.

Achieved a correct identification rate of up to 69% when cross-matched with existing catalogs.

Validated the catalog's credibility through cross-comparison with GAIA and other sources.

Abstract

The identification and analysis of different variable sources is a hot issue in astrophysical research. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) spectroscopic survey has accumulated massive spectral data but contains no information about variable sources. Although a few related studies present variable source catalogs for the LAMOST, the studies still have a few deficiencies regarding the type and number of variable sources identified. In this study, we presented a statistical modeling approach to identify variable source candidates. We first crossed the Kepler, Sloan Digital Sky Survey (SDSS), and Zwicky Transient Facility (ZTF) catalogs to obtain light curves data of variable and non-variable sources. The data are then modeled statistically using commonly used variability parameters, respectively. And then, an optimal variable source identification model is determined using the Receiver Operating Characteristic (ROC) curve and four credible evaluation indices such as precision, accuracy, recall, and F1score. Based on this identification model, a catalog of LAMOST variable sources (including 631,769 variable source candidates with a probability greater than 95% and so on) is obtained. To validate the correctness of the catalog, we performed a two-by-two cross-comparison with the GAIA catalog and other published variable source catalogs. We achieved the correct rate ranging from 50% to 100%. Among the 123,756 sources cross-matched, our variable source catalog identifies 85,669 with a correct rate of 69%, which indicates that the variable source catalog presented in this study is credible.