Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new approach. The usage of machine learning methods, however is still far from trivial and many problems still need to be solved. Using the evaluation of photometric redshifts as a case study, we outline the main problems and some ongoing efforts to solve them. © Springer International Publishing AG, part of Springer Nature 2018.

Data deluge in astrophysics: Photometric redshifts as a template use case

;Cavuoti, S.;Amaro, V.;Longo, G.
2018

Abstract

Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new approach. The usage of machine learning methods, however is still far from trivial and many problems still need to be solved. Using the evaluation of photometric redshifts as a case study, we outline the main problems and some ongoing efforts to solve them. © Springer International Publishing AG, part of Springer Nature 2018.
978-331996552-9
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/741638
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