Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case

Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case

The importance of the current role of data-driven science is constantly increasing within Astrophysics, due to the huge amount of multi-wavelength data collected every day, characterized by complex and high-volume information requiring efficient and, as much as possible, automated exploration tools....

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Bibliographic Details
Main Authors: Massimo Brescia, Stefano Cavuoti, Oleksandra Razim, Valeria Amaro, Giuseppe Riccio, Giuseppe Longo
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Astronomy and Space Sciences
Subjects:
photometric redshifts
machine learning
astroinformatics
galaxies
data analysis
Online Access:https://www.frontiersin.org/articles/10.3389/fspas.2021.658229/full

Internet

https://www.frontiersin.org/articles/10.3389/fspas.2021.658229/full

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