The verification of periodicity with the use of recurrent neural networks

TL;DR

This paper introduces a machine learning method using recurrent neural networks to automatically verify the periodicity in astronomical time-series data, overcoming limitations of traditional analytic approaches.

Contribution

It presents a novel RNN-based technique that directly analyzes phase-folded light curves for false alarm probability, independent of light curve shape.

Findings

Method is insensitive to light curve shape

Establishes minimum data points for reliable verification

Effective in large astronomical survey data

Abstract

The ability to automatically and robustly self-verify periodicity present in time-series astronomical data is becoming more important as data sets rapidly increase in size. The age of large astronomical surveys has rendered manual inspection of time-series data less practical. Previous efforts in generating a false alarm probability to verify the periodicity of stars have been aimed towards the analysis of a constructed periodogram. However, these methods feature correlations with features that do not pertain to periodicity, such as light curve shape, slow trends and stochastic variability. The common assumption that photometric errors are Gaussian and well determined is also a limitation of analytic methods. We present a novel machine learning based technique which directly analyses the phase folded light curve for its false alarm probability. We show that the results of this method are largely insensitive to the shape of the light curve, and we establish minimum values for the number of data points and the amplitude to noise ratio.