分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods is the most important step to distinguish different types of variable stars. Here, we propose a new method of determining the variability periods. By combining the statistical parameters of the light curves, the colors of the variables, the window function and the GLS algorithm, the aperiodic variables are excluded and the periodic variables are divided into eclipsing binaries and NEB variables (other types of periodic variable stars other than eclipsing binaries), the periods of the two main types of variables are derived. We construct a random forest classifier based on 241,154 periodic variables from the ASAS-SN and OGLE datasets of variables. The random forest classifier is trained on 17 features, among which 11 are extracted from the light curves and 6 are from the Gaia Early DR3, ALLWISE and 2MASS catalogs. The variables are classified into 7 superclasses and 17 subclasses. In comparison with the ASAS-SN and OGLE catalogs, the classification accuracy is generally above approximately 82% and the period accuracy is 70%-99%. To further test the reliability of the new method and classifier, we compare our results with the results of Chen et al. (2020) for ZTF DR2. The classification accuracy is generally above 70%. The period accuracy of the EW and SR variables is 50% and 53%, respectively. And the period accuracy of other types of variables is 65%-98%.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： The first B-, V-, Rc-, and Ic-band light curves of CSS J022914.4+044340 are presented and analyzed. It is found that CSS J022914.4+044340 is a low mass ratio (0.198 +- 0.005) deep (63.7 +- 7.9%) contact binary, indicating that it has been already at the end evolutionary stage of tidally-locked evolution via magnetized wind. Because of the totally eclipsing character, the photometric solutions are reliable. The temperature and the metallicity are determined from the spectroscopic data as T = 5855 +- 15 K, and [Fe/H] = -0.842 +- 0.031, respectively. Based on the parallax of Gaia EDR3, the physical parameters of CSS J022914.4+044340 are estimated as M1 = 1.44 (+0.25,-0.22) solar mass, M2 = 0.29 (+0.05,-0.05) solar mass, R1 = 1.26 (+0.08,-0.06) solar radius, R2 = 0.65 (+0.03,-0.04) solar radius, L1 = 1.718 (+0.186,-0.191) solar luminosity, L2 = 0.416 (+0.039,-0.050) solar luminosity. Combined the fraction in light of the third body via the photometric solution (54%), the luminosity of the third body is estimated as 2.705 solar luminosity. The third body may be inferred as a subgiant. Thus, it is explained that why the primary component of CSS J022914.4+044340 has higher mass among the similar systems, and why its metallicity is so poor.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods is the most important step to distinguish different types of variable stars. Here, we propose a new method of determining the variability periods. By combining the statistical parameters of the light curves, the colors of the variables, the window function and the GLS algorithm, the aperiodic variables are excluded and the periodic variables are divided into eclipsing binaries and NEB variables (other types of periodic variable stars other than eclipsing binaries), the periods of the two main types of variables are derived. We construct a random forest classifier based on 241,154 periodic variables from the ASAS-SN and OGLE datasets of variables. The random forest classifier is trained on 17 features, among which 11 are extracted from the light curves and 6 are from the Gaia Early DR3, ALLWISE and 2MASS catalogs. The variables are classified into 7 superclasses and 17 subclasses. In comparison with the ASAS-SN and OGLE catalogs, the classification accuracy is generally above approximately 82% and the period accuracy is 70%-99%. To further test the reliability of the new method and classifier, we compare our results with the results of Chen et al. (2020) for ZTF DR2. The classification accuracy is generally above 70%. The period accuracy of the EW and SR variables is 50% and 53%, respectively. And the period accuracy of other types of variables is 65%-98%.
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