Skip to main content

Research Repository

Advanced Search

Of Genes and Machines: Application of a Combination of Machine Learning Tools to Astronomy Data Sets

Heinis, S.; Kumar, S.; Gezari, S.; Burgett, W.S.; Chambers, K.C.; Draper, P.W.; Flewelling, H.; Kaiser, N.; Magnier, E.A.; Metcalfe, N.; Waters, C.

Of Genes and Machines: Application of a Combination of Machine Learning Tools to Astronomy Data Sets Thumbnail


Authors

S. Heinis

S. Kumar

S. Gezari

W.S. Burgett

K.C. Chambers

P.W. Draper

H. Flewelling

N. Kaiser

E.A. Magnier

C. Waters



Abstract

We apply a combination of genetic algorithm (GA) and support vector machine (SVM) machine learning algorithms to solve two important problems faced by the astronomical community: star–galaxy separation and photometric redshift estimation of galaxies in survey catalogs. We use the GA to select the relevant features in the first step, followed by optimization of SVM parameters in the second step to obtain an optimal set of parameters to classify or regress, in the process of which we avoid overfitting. We apply our method to star–galaxy separation in Pan-STARRS1 data. We show that our method correctly classifies 98% of objects down to iP1 = 24.5, with a completeness (or true positive rate) of 99% for galaxies and 88% for stars. By combining colors with morphology, our star–galaxy separation method yields better results than the new SExtractor classifier spread_model, in particular at the faint end (iP1 > 22). We also use our method to derive photometric redshifts for galaxies in the COSMOS bright multiwavelength data set down to an error in (1 + z) of s = 0.013, which compares well with estimates from spectral energy distribution fitting on the same data (s = 0.007) while making a significantly smaller number of assumptions.

Citation

Heinis, S., Kumar, S., Gezari, S., Burgett, W., Chambers, K., Draper, P., …Waters, C. (2016). Of Genes and Machines: Application of a Combination of Machine Learning Tools to Astronomy Data Sets. Astrophysical Journal, 821(2), Article 86. https://doi.org/10.3847/0004-637x/821/2/86

Journal Article Type Article
Acceptance Date Mar 2, 2016
Online Publication Date Apr 13, 2016
Publication Date Apr 20, 2016
Deposit Date May 24, 2016
Publicly Available Date Jun 21, 2016
Journal Astrophysical Journal
Print ISSN 0004-637X
Electronic ISSN 1538-4357
Publisher American Astronomical Society
Peer Reviewed Peer Reviewed
Volume 821
Issue 2
Article Number 86
DOI https://doi.org/10.3847/0004-637x/821/2/86

Files

Published Journal Article (4.9 Mb)
PDF

Copyright Statement
© 2016. The American Astronomical Society. All rights reserved.







You might also like



Downloadable Citations