Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case

TL;DR

This paper discusses the challenges and ongoing efforts in applying machine learning to estimate photometric redshifts in astronomy, highlighting the broader context of big data in astrophysics.

Contribution

It provides a case study on photometric redshifts to illustrate key problems and potential solutions in machine learning applications for big data in astrophysics.

Findings

Identifies main challenges in machine learning for photometric redshifts

Highlights ongoing efforts to address these challenges

Emphasizes the importance of this approach in the era of big data

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.