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

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.

References in corpus (2)

• Limits on the Macho Content of the Galactic Halo from the EROS-2 Survey of the Magellanic Clouds
• The beat Cepheids in the Magellanic Clouds: an analysis from the EROS-2 database

Cited by in corpus (9)

• Gamma-ray and Optical Oscillations of 0716+714, MRK 421, and BL Lac
• Machine Learning Techniques for Stellar Light Curve Classification
• Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves
• Robust period estimation using mutual information for multi-band light curves in the synoptic survey era
• An Updated Catalog of 4680 Northern Eclipsing Binaries with Algol-Type light curve morphology in the Catalina Sky Surveys
• Meta Classification for Variable Stars
• Finding radio transients with anomaly detection and active learning based on volunteer classifications
• GRAPE: Genetic Routine for Astronomical Period Estimation
• Classification of blazars based on data-driven approaches