skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: The EB factory project. II. Validation with the Kepler field in preparation for K2 and TESS

Abstract

Large repositories of high precision light curve data, such as the Kepler data set, provide the opportunity to identify astrophysically important eclipsing binary (EB) systems in large quantities. However, the rate of classical “by eye” human analysis restricts complete and efficient mining of EBs from these data using classical techniques. To prepare for mining EBs from the upcoming K2 mission as well as other current missions, we developed an automated end-to-end computational pipeline—the Eclipsing Binary Factory (EBF)—that automatically identifies EBs and classifies them into morphological types. The EBF has been previously tested on ground-based light curves. To assess the performance of the EBF in the context of space-based data, we apply the EBF to the full set of light curves in the Kepler “Q3” Data Release. We compare the EBs identified from this automated approach against the human generated Kepler EB Catalog of ∼2600 EBs. When we require EB classification with ⩾90% confidence, we find that the EBF correctly identifies and classifies eclipsing contact (EC), eclipsing semi-detached (ESD), and eclipsing detached (ED) systems with a false positive rate of only 4%, 4%, and 8%, while complete to 64%, 46%, and 32%, respectively. When classification confidence is relaxed, the EBF identifiesmore » and classifies ECs, ESDs, and EDs with a slightly higher false positive rate of 6%, 16%, and 8%, while much more complete to 86%, 74%, and 62%, respectively. Through our processing of the entire Kepler “Q3” data set, we also identify 68 new candidate EBs that may have been missed by the human generated Kepler EB Catalog. We discuss the EBF's potential application to light curve classification for periodic variable stars more generally for current and upcoming surveys like K2 and the Transiting Exoplanet Survey Satellite.« less

Authors:
;
Publication Date:
OSTI Identifier:
22342200
Resource Type:
Journal Article
Journal Name:
Astronomical Journal (New York, N.Y. Online)
Additional Journal Information:
Journal Volume: 148; Journal Issue: 6; Other Information: Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 1538-3881
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ACCURACY; ASTROPHYSICS; CATALOGS; CLASSIFICATION; COMPARATIVE EVALUATIONS; DATA ANALYSIS; ECLIPSE; PERFORMANCE; PERIODICITY; PIPELINES; SATELLITES; SPACE; VALIDATION; VARIABLE STARS; VISIBLE RADIATION

Citation Formats

Parvizi, Mahmoud, Paegert, Martin, and Stassun, Keivan G., E-mail: mahmoud.parvizi@vanderbilt.edu. The EB factory project. II. Validation with the Kepler field in preparation for K2 and TESS. United States: N. p., 2014. Web. doi:10.1088/0004-6256/148/6/125.
Parvizi, Mahmoud, Paegert, Martin, & Stassun, Keivan G., E-mail: mahmoud.parvizi@vanderbilt.edu. The EB factory project. II. Validation with the Kepler field in preparation for K2 and TESS. United States. https://doi.org/10.1088/0004-6256/148/6/125
Parvizi, Mahmoud, Paegert, Martin, and Stassun, Keivan G., E-mail: mahmoud.parvizi@vanderbilt.edu. 2014. "The EB factory project. II. Validation with the Kepler field in preparation for K2 and TESS". United States. https://doi.org/10.1088/0004-6256/148/6/125.
@article{osti_22342200,
title = {The EB factory project. II. Validation with the Kepler field in preparation for K2 and TESS},
author = {Parvizi, Mahmoud and Paegert, Martin and Stassun, Keivan G., E-mail: mahmoud.parvizi@vanderbilt.edu},
abstractNote = {Large repositories of high precision light curve data, such as the Kepler data set, provide the opportunity to identify astrophysically important eclipsing binary (EB) systems in large quantities. However, the rate of classical “by eye” human analysis restricts complete and efficient mining of EBs from these data using classical techniques. To prepare for mining EBs from the upcoming K2 mission as well as other current missions, we developed an automated end-to-end computational pipeline—the Eclipsing Binary Factory (EBF)—that automatically identifies EBs and classifies them into morphological types. The EBF has been previously tested on ground-based light curves. To assess the performance of the EBF in the context of space-based data, we apply the EBF to the full set of light curves in the Kepler “Q3” Data Release. We compare the EBs identified from this automated approach against the human generated Kepler EB Catalog of ∼2600 EBs. When we require EB classification with ⩾90% confidence, we find that the EBF correctly identifies and classifies eclipsing contact (EC), eclipsing semi-detached (ESD), and eclipsing detached (ED) systems with a false positive rate of only 4%, 4%, and 8%, while complete to 64%, 46%, and 32%, respectively. When classification confidence is relaxed, the EBF identifies and classifies ECs, ESDs, and EDs with a slightly higher false positive rate of 6%, 16%, and 8%, while much more complete to 86%, 74%, and 62%, respectively. Through our processing of the entire Kepler “Q3” data set, we also identify 68 new candidate EBs that may have been missed by the human generated Kepler EB Catalog. We discuss the EBF's potential application to light curve classification for periodic variable stars more generally for current and upcoming surveys like K2 and the Transiting Exoplanet Survey Satellite.},
doi = {10.1088/0004-6256/148/6/125},
url = {https://www.osti.gov/biblio/22342200}, journal = {Astronomical Journal (New York, N.Y. Online)},
issn = {1538-3881},
number = 6,
volume = 148,
place = {United States},
year = {Mon Dec 01 00:00:00 EST 2014},
month = {Mon Dec 01 00:00:00 EST 2014}
}