A Novel, Fully Automated Pipeline for Period Estimation in the EROS 2 Data Set

TL;DR

This paper introduces a fully automated pipeline that accurately discriminates periodic from non-periodic lightcurves in large datasets, using a novel kernel and similarity measure, achieving high precision and efficiency on synthetic and real data.

Contribution

The paper presents a new method with a periodic kernel and information-theoretic similarity measure for period estimation, optimized for large-scale lightcurve analysis.

Findings

Achieved 90% completeness and 95% precision on synthetic data.

Analyzed 32.8 million lightcurves in 18 hours using GPU clusters.

Estimated periodic source fractions in LMC and SMC.

Abstract

We present a new method to discriminate periodic from non-periodic irregularly sampled lightcurves. We introduce a periodic kernel and maximize a similarity measure derived from information theory to estimate the periods and a discriminator factor. We tested the method on a dataset containing 100,000 synthetic periodic and non-periodic lightcurves with various periods, amplitudes and shapes generated using a multivariate generative model. We correctly identified periodic and non-periodic lightcurves with a completeness of 90% and a precision of 95%, for lightcurves with a signal-to-noise ratio (SNR) larger than 0.5. We characterize the efficiency and reliability of the model using these synthetic lightcurves and applied the method on the EROS-2 dataset. A crucial consideration is the speed at which the method can be executed. Using hierarchical search and some simplification on the parameter search we were able to analyze 32.8 million lightcurves in 18 hours on a cluster of GPGPUs. Using the sensitivity analysis on the synthetic dataset, we infer that 0.42% in the LMC and 0.61% in the SMC of the sources show periodic behavior. The training set, the catalogs and source code are all available in http://timemachine.iic.harvard.edu.