Uncertain Photometric Redshifts with Deep Learning Methods. Issue Volume 12:Issue S325(2016) (30th May 2017)

• Record Type: Journal Article
• Authors: D'Isanto, A.
• Editors: Brescia, M.; Djorgovski, S.G.; Feigelson, E.; Longo, G.; Cavuoti, S.
• Is Part Of: Proceedings of the International Astronomical Union. Volume 12:Issue S325(2016)
• Page Start: 209
• Page End: 212
• Publication Date: 2017-05-30
• Subjects: techniques: galaxies: distances and redshifts, -- photometric, -- methods: data analysis, -- surveys, -- (galaxies:) quasars: general etc.; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921316013090
• Languages: English
• ISSNs: 1743-9213

Abstract

Abstract: 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.