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Modeling lightcurves for improved classification of astronomical objects

Faraway, Julian and Mahabal, Ashish and Sun, Jiayang and Wang, Xiao-Feng and Wang, Yi G. and Zhang, Lingsong (2016) Modeling lightcurves for improved classification of astronomical objects. Statistical Analysis and Data Mining, 9 (1). pp. 1-11. ISSN 1932-1864. http://resolver.caltech.edu/CaltechAUTHORS:20160303-125620384

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Abstract

Many synoptic surveys are observing large parts of the sky multiple times. The resulting time series of light measurements, called lightcurves, provide a wonderful window to the dynamic nature of the Universe. However, there are many significant challenges in analyzing these lightcurves. We describe a modeling-based approach using Gaussian process regression for generating critical measures for the classification of such lightcurves. This method has key advantages over other popular nonparametric regression methods in its ability to deal with censoring, a mixture of sparsely and densely sampled curves, the presence of annual gaps caused by objects not being visible throughout the year from a given position on Earth and known but variable measurement errors. We demonstrate that our approach performs better by showing it has a higher correct classification rate than past methods popular in astronomy. Finally, we provide future directions for use in sky-surveys that are getting even bigger by the day.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1002/sam.11305DOIArticle
http://onlinelibrary.wiley.com/doi/10.1002/sam.11305/abstractPublisherArticle
Additional Information:© 2016 Wiley Periodicals, Inc. Received 8 September 2014; revised 5 November 2015; accepted 8 January 2016; Published online 1 February 2016. This work is one of results from the Imaging Working Group at the SAMSI’s 2012-13 Program on Statistical and Computational Methodology for Massive Datasets. We thank SAMSI for bringing us together and for their financial support. The CSS survey is funded by the National Aeronautics and Space Administration under Grant No. NNG05GF22G issued through the Science Mission Directorate Near-Earth Objects Observations Program. The CRTS survey is supported by the U.S. National Science Foundation under grants AST-0909182 and AST-1313422. We are also thankful to the Keck Institute of Space Studies, the Indo-US Science and Technology Forum (IUSSTF), and part of the work was supported through the Classification grant, IIS-1118041. We thank SG Djorgovski for useful comments and AJ Drake and MJ Graham for help in assembling the 100K dataset.
Group:Keck Institute for Space Studies
Funders:
Funding AgencyGrant Number
NASANNG05GF22G
NSFAST-0909182
NSFAST-1313422
Keck Institute of Space Studies (KISS)UNSPECIFIED
Indo-US Science and Technology Forum (IUSSTF)UNSPECIFIED
NSFIIS-1118041
Subject Keywords:classification, feature selection, Gaussian process regression, irregular sampling, missing data
Record Number:CaltechAUTHORS:20160303-125620384
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20160303-125620384
Official Citation:Faraway, J., Mahabal, A., Sun, J., Wang, X.-F., Wang, Y. G. and Zhang, L. (2016), Modeling lightcurves for improved classification of astronomical objects. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9: 1–11. doi:10.1002/sam.11305
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65027
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Mar 2016 21:08
Last Modified:04 Mar 2016 21:08

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