Physical parameters of 12201 ASAS-SN contact binaries determined by the Neural Network
Kai Li, Li-Heng Wang

TL;DR
This paper introduces an advanced neural network combined with MCMC to efficiently determine physical parameters of over 12,000 contact binaries from large astronomical survey data, revealing statistical properties and subtype distinctions.
Contribution
The study presents a novel neural network model with MCMC and spot parameters for large-scale analysis of contact binaries, improving parameter estimation accuracy and enabling comprehensive statistical analysis.
Findings
Identified two peaks in mass ratio distribution.
Found about 50% of systems likely have spots.
Observed that light curve asymmetry increases with decreasing period and primary temperature.
Abstract
In the era of astronomical big data, more than one million contact binaries have been discovered. Traditional approaches of light curve analysis are inadequate for investigating such an extensive number of systems. This paper builds on prior research to present an advanced Neural Network model combined with the Markov Chain Monte Carlo algorithm and including spot parameters. This model was applied to 12785 contact binaries selected from All-Sky Automated Survey for Supernovae. By removing those with goodness of fit less than 0.8, we obtained the physical parameters of 12201 contact binaries. Among these binaries, 4332 are A-subtype systems, while 7869 are W-type systems, and 1594 systems have mass ratios larger than 0.72 (H-subtype system). A statistical study of the physical parameters was carried out, and we found that there are two peaks in the mass ratio distribution and that the…
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Taxonomy
TopicsGamma-ray bursts and supernovae · Geological and Geophysical Studies · Astronomy and Astrophysical Research
