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Uncertain Photometric Redshifts with Deep Learning Methods

Published online by Cambridge University Press:  30 May 2017

A. D’Isanto
A. D’Isanto*
Affiliation:
Heidelberg Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg - GERMANY email: antonio.disanto@h-its.org
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Abstract

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The need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multi-modal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.

Keywords

techniques: galaxies: distances and redshiftsphotometricmethods: data analysissurveys(galaxies:) quasars: general etc.
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 209 - 212
Copyright
Copyright © International Astronomical Union 2017 

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