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Learn from every mistake! Hierarchical information combination in astronomy

Published online by Cambridge University Press:  30 May 2017

Maria Süveges ,
Sotiria Fotopoulou ,
Jean Coupon ,
Stéphane Paltani ,
Laurent Eyer  and
Lorenzo Rimoldini
Maria Süveges
Affiliation:
Dept. of Astronomy, University of Geneva, Ch. d'Ecogia 16, 1290 Versoix, Switzerland
Sotiria Fotopoulou
Affiliation:
Dept. of Astronomy, University of Geneva, Ch. d'Ecogia 16, 1290 Versoix, Switzerland
Jean Coupon
Affiliation:
Dept. of Astronomy, University of Geneva, Ch. d'Ecogia 16, 1290 Versoix, Switzerland
Stéphane Paltani
Affiliation:
Dept. of Astronomy, University of Geneva, Ch. d'Ecogia 16, 1290 Versoix, Switzerland
Laurent Eyer
Affiliation:
Dept. of Astronomy, University of Geneva, Ch. des Maillettes 51, 1290 Versoix, Switzerland
Lorenzo Rimoldini
Affiliation:
Dept. of Astronomy, University of Geneva, Ch. d'Ecogia 16, 1290 Versoix, Switzerland
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Abstract

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Throughout the processing and analysis of survey data, a ubiquitous issue nowadays is that we are spoilt for choice when we need to select a methodology for some of its steps. The alternative methods usually fail and excel in different data regions, and have various advantages and drawbacks, so a combination that unites the strengths of all while suppressing the weaknesses is desirable. We propose to use a two-level hierarchy of learners. Its first level consists of training and applying the possible base methods on the first part of a known set. At the second level, we feed the output probability distributions from all base methods to a second learner trained on the remaining known objects. Using classification of variable stars and photometric redshift estimation as examples, we show that the hierarchical combination is capable of achieving general improvement over averaging-type combination methods, correcting systematics present in all base methods, is easy to train and apply, and thus, it is a promising tool in the astronomical “Big Data” era.

Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 39 - 45
Copyright
Copyright © International Astronomical Union 2017 

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