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

Abstract : We present a new method to discriminate periodic from nonperiodic irregularly sampled light curves. 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 data set containing 100,000 synthetic periodic and nonperiodic light curves with various periods, amplitudes, and shapes generated using a multivariate generative model. We correctly identified periodic and nonperiodic light curves with a completeness of ~90% and a precision of ~95%, for light curves with a signal-to-noise ratio (S/N) larger than 0.5. We characterize the efficiency and reliability of the model using these synthetic light curves and apply the method on the EROS-2 data set. A crucial consideration is the speed at which the method can be executed. Using a hierarchical search and some simplification on the parameter search, we were able to analyze 32.8 million light curves in ~18 hr on a cluster of GPGPUs. Using the sensitivity analysis on the synthetic data set, we infer that 0.42% of the sources in the LMC and 0.61% of the sources in the SMC show periodic behavior. The training set, catalogs, and source code are all available at http://timemachine.iic.harvard.edu.
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Submitted on : Tuesday, April 19, 2022 - 2:03:21 PM
Last modification on : Wednesday, April 20, 2022 - 3:40:09 AM

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Pavlos Protopapas, Pablo Huijse, Pablo A. Estévez, Pablo Zegers, José C. Príncipe, et al.. A Novel, Fully Automated Pipeline for Period Estimation in the EROS 2 Data Set. The Astrophysical Journal Supplement, 2015, 216, ⟨10.1088/0067-0049/216/2/25⟩. ⟨insu-03644769⟩



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