Automated period detection from variable stars' time series database

TL;DR

This paper presents an automated method for accurately determining the periods of variable stars from their luminosity time series data, reducing human intervention and improving classification accuracy.

Contribution

The study introduces an enhanced, automated period detection technique based on SigSpec, tailored for variable star data, with improved noise handling and spurious peak avoidance.

Findings

243 out of 386 stars had exact period recovery

88 stars' half periods were correctly identified

SigSpec demonstrated potential for fully automated analysis

Abstract

The exact period determination of a multi-periodic variable star based on its luminosity time series data is believed a task requiring skill and experience. Thus the majority of available time series analysis techniques require human intervention to some extent. The present work is dedicated to establish an automated method of period (or frequency) determination from the time series database of variable stars. Relying on the SigSpec method (Reegen 2007), the technique established here employs a statistically unbiased treatment of frequency-domain noise and avoids spurious (i. e. noise induced) and alias peaks to the highest possible extent. Several add-on's were incorporated to tailor SigSpec to our requirements. We present tests on 386 stars taken from ASAS2 project database. From the output file produced by SigSpec, the frequency with maximum spectral significance is chosen as the genuine frequency. Out of 386 variable stars available in the ASAS2 database, our results contain 243 periods recovered exactly and also 88 half periods, 42 different periods etc. SigSpec has the potential to be effectively used for fully automated period detection from variable stars' time series database. The exact detection of periods helps us to identify the type of variability and classify the variable stars, which provides a crucial information on the physical processes effective in stellar atmospheres.