Probabilistic photometric redshifts in the era of petascale astronomy

With the growth of large photometric surveys, accurately estimating photometric redshifts, preferably as a probability density function (PDF), and fully understanding the implicit systematic uncertainties in this process has become increasingly important. These surveys are expected to obtain images of billions of distinct galaxies. As a result, storing and analyzing all of these photometric redshift PDFs will be non-trivial, and this challenge becomes even more severe if a survey plans to compute and store multiple different PDFs. In this thesis, we have developed an end-to-end framework that will compute accurate and robust photometric redshift PDFs for massive data sets by using two new, state-of-the-art machine learning techniques that are based on a random forest and a random atlas, respectively. By using data from several photometric surveys, we demonstrate the applicability of these new techniques, and we demonstrate that our new approach is among the best techniques currently available. We also show how different techniques can be combined by using novel Bayesian techniques to improve the photometric redshift precision to unprecedented levels while also presenting new approaches to better identify outliers. In addition, our framework provides supplementary information regarding the data being analyzed, including unbiased estimates of the accuracy of the technique without resorting to a validation data set, identification of poor photometric redshift areas within the parameter space occupied by the spectroscopic training data, and a quantification of the relative importance of the variables used during the estimation process. Furthermore, we present a new approach to represent and store photometric redshift PDFs by using a sparse representation with outstanding compression and reconstruction capabilities. We also demonstrate how this framework can also be directly incorporated into cosmological analyses. The new techniques presented in this thesis are crucial to enable the development of precision cosmology in the era of petascale astronomical surveys.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Astronomy

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Carrasco Kind, Matias

Contributors dc:contributor

• Brunner, Robert J.
• Kemball, Athol J.
• Ricker, Paul M.
• Thaler, Jon J.

• Photometric redshift
• machine learning
• data mining
• galaxy surveys
• petascale astronomy

Statement dc:rights

• Copyright 2014 Matias Carrasco Kind

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/72878

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/72878