Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry

In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photome...

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Bibliographic Details
Published in:arXiv.org 2022-07
Main Authors: Li, Rui, Napolitano, Nicola R, Feng, Haicheng, Li, Ran, Amaro, Valeria, Xie, Linghua, Tortora, Crescenzo, Bilicki, Maciej, Brescia, Massimo, Cavuoti, Stefano, Radovich, Mario
Format: Article
Language:English
Subjects:
Active galactic nuclei
Cosmology
Galactic evolution
Galaxies
Infrared astronomy
Infrared photometry
Machine learning
Neural networks
Outliers (statistics)
Photometry
Red shift
Sky surveys (astronomy)
Stars & galaxies
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Summary:In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only. As a first application of this tool, we estimate photo-z of a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on \(\sim140 000\) galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG\(\_\)AUTO\(
ISSN:2331-8422
DOI:10.48550/arxiv.2205.10720