New method can quickly derive contact binary parameters for large photometric surveys

A contact binary is a strongly interacting binary system with two part stars stuffed with Roche lobes, and there’s a widespread envelope across the part stars.

With the discharge of hundreds of sunshine curves of contact binaries, it sometimes takes a number of hours or days for the present strategies to derive the parameters of contact binaries.

Dr. Ding Xu and Prof. Ji Kaifan from the Yunnan Observatories of the Chinese language Academy of Sciences (CAS), in collaboration with Li Xuzhi from the College of Science and Expertise of China, have proposed a machine learning-based methodology to rapidly receive the parameters and errors of contact binaries.

This research was revealed in The Astronomical Journal on Oct. 18.

The researchers first used a neural network (NN) to ascertain the mapping relationship between the parameters of the contact binary stars and the sunshine curves, and obtained one mannequin with out the affect of the third gentle and one mannequin with the affect of the third gentle, respectively.

The error of the sunshine curves generated by these two fashions is lower than one thousandth of the magnitude, and the parameters and corresponding errors of the contact binaries might be rapidly obtained by combining the Markov chain Monte Carlo algorithm (MCMC). In contrast with the standard strategies, this methodology not solely meets the necessities in accuracy, but additionally improves the pace by 4 orders of magnitude underneath the identical operating situation.

This methodology makes it doable to derive the parameters of numerous contact binaries. Subsequent, the researchers will conduct statistical evaluation of the contact binaries within the TESS survey information of the space telescope and the ZTF survey information of the bottom telescope.