Identifications of RR Lyrae stars and Quasars from the simulated data of Mephisto-W Survey

TL;DR

This study demonstrates that machine learning algorithms can accurately identify RR Lyrae stars and quasars from simulated multi-band light curves of the Mephisto-W Survey, achieving high precision and completeness.

Contribution

The paper presents a novel application of Random Forest classifiers to simulated Mephisto-W Survey data for the identification of variable sources, with detailed feature analysis.

Findings

High classification accuracy with purity over 91% and completeness over 90%.

Effective feature importance analysis for variable source classification.

First simulation-based assessment of RR Lyrae and quasar identification for Mephisto-W Survey.

Abstract

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the $uvgriz$ multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first year observation of Mephisto-W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~ 150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with $purity$ = 95.4 per cent and $completeness$ = 96.9 per cent for the RR Lyrae RFC and $purity$ = 91.4 per cent and $completeness$ = 90.2 per cent for the quasar RFC. We have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.