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Title: A NOVEL, FULLY AUTOMATED PIPELINE FOR PERIOD ESTIMATION IN THE EROS 2 DATA SET

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.
Authors:
 [1] ; ;  [2] ;  [3] ;  [4] ;  [5]
  1. Institute for Applied Computational Science, Harvard University, Cambridge, MA 02138 (United States)
  2. Millennium Institute of Astrophysics (Chile)
  3. Universidad de los Andes, Facultad de Ingeniería y Ciencias Aplicadas, Monseñor Álvaro del Portillo 12455, Las Condes, Santiago (Chile)
  4. Computational Neuroengineering Laboratory, University of Florida, Gainesville, FL 32611 (United States)
  5. UPMC-CNRS, UMR7095, Institut d'Astrophysique de Paris, F-75014 Paris (France)
Publication Date:
OSTI Identifier:
22340094
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal, Supplement Series; Journal Volume: 216; Journal Issue: 2; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ACCURACY; AMPLITUDES; AUTOMATION; CATALOGS; DATA ANALYSIS; DIAGRAMS; EFFICIENCY; INFORMATION THEORY; MULTIVARIATE ANALYSIS; PERIODICITY; RELIABILITY; SENSITIVITY ANALYSIS; SIGNAL-TO-NOISE RATIO; VISIBLE RADIATION