Photometric redshift estimation based on data mining with PhotoRApToR
Stefano Cavuoti, Massimo Brescia, Virgilio De Stefano, Giuseppe Longo
TL;DR
This paper introduces PhotoRApToR, a desktop application utilizing neural networks and data processing tools to improve photometric redshift estimation in large digital surveys.
Contribution
It presents a new software tool that combines machine learning and data processing for accurate photometric redshift estimation.
Findings
Successfully tested on multiple scientific cases
Provides a user-friendly interface for non-linear regression and classification
Available for free download from the DAME Program website
Abstract
Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multilayer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and postprocessing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.
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