Investigation of the Orbital Period and Mass Relations for W UMa-type Contact Systems

TL;DR

This study derives new empirical relationships between orbital period, mass, temperature, and other parameters of W UMa-type contact binary systems using a large sample and advanced statistical methods.

Contribution

It introduces novel orbital period-mass and period-temperature relations for W UMa systems based on an expanded dataset and machine learning techniques.

Findings

New P-M relations for each component of W UMa systems.

A P-T1 relation incorporating temperature effects.

Models linking orbital period with primary mass and mass ratio.

Abstract

New relationships between the orbital period and some parameters of W Ursae Majoris (W UMa) type systems are presented in this study. To investigate the relationships, we calculated the absolute parameters of a sample of 118 systems. For this purpose, we used the parallax values obtained from the Gaia Early Data Release 3 (Gaia EDR3) star catalog for more precise calculations. The other required parameters, including the light curve solutions and the orbital period were derived from previous research. For some relationships, we added 86 systems from another study with an orbital period of less than 0.6 days to our sample, allowing us to increase the number of systems to 204. Therefore, the mass (M) values of each component along with all the other absolute parameters were recalculated for these contact systems. We used the Markov Chain Monte Carlo (MCMC) approach in order to gain the new orbital period-mass relations (P-M) per component, and added the temperature (T) to the process to acquire the new orbital period-temperature (P-T1) relation. We presented the orbital period behavior in terms of log(g) by new relations for each component. We have also obtained a model between the orbital period, the mass of the primary component and temperature (P-M1-T1) using the Artificial Neural Networks (ANN) method. Additionally, we present a model for the relationship between the orbital period and the mass ratio (P-q) by fitting a Multi-Layer Perceptron (MLP) regression model to a sample of the data collected from the literature.