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We have investigated the feasibilities and accuracies of the identificationsof RR Lyrae stars and quasars from the simulated data of the Multi-channelPhotometric Survey Telescope (Mephisto) W Survey. Based on the variable sourceslight curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 dataand the observation history simulation from the Mephisto-W Survey Scheduler, wehave simulated the $uvgriz$ multi-band light curves of RR Lyrae stars, quasarsand other variable sources for the first year observation of Mephisto-W Survey.We have applied the ensemble machine learning algorithm Random ForestClassifier (RFC) to identify RR Lyrae stars and quasars, respectively. We buildtraining and test samples and extract ~ 150 features from the simulated lightcurves and train two RFCs respectively for the RR Lyrae star and quasarclassification. We find that, our RFCs are able to select the RR Lyrae starsand 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 toclassify RR Lyrae stars and quasars.

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