Issue |
A&A
Volume 666, October 2022
|
|
---|---|---|
Article Number | A85 | |
Number of page(s) | 12 | |
Section | Extragalactic astronomy | |
DOI | https://doi.org/10.1051/0004-6361/202244081 | |
Published online | 11 October 2022 |
Galaxy morphoto-Z with neural Networks (GaZNets)
I. Optimized accuracy and outlier fraction from imaging and photometry
1
School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, PR China
e-mail: liruiww@gmail.com
2
National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012, PR China
3
School of Physics and Astronomy, Sun Yat-sen University, Zhuhai Campus, 2 Daxue Road, Xiangzhou District, Zhuhai, PR China
e-mail: napolitano@mail.sysu.edu.cn
4
CSST Science Center for Guangdong-Hong Kong-Macau Great Bay Area, Zhuhai, 519082, PR China
5
Yunnan Observatories, Chinese Academy of Sciences, Kunming, 650011 Yunnan, PR China
6
INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, 80131 Napoli, Italy
7
Center for Theoretical Physics, Polish Academy of Sciences, Al. Lotników 32/46, 02-668 Warsaw, Poland
8
INFN – Sezione di Napoli, via Cinthia 9, 80126 Napoli, Italy
9
INAF – Osservatorio Astronomico di Padova, via dell’Osservatorio 5, 35122 Padova, Italy
Received:
22
May
2022
Accepted:
16
July
2022
Aims. 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.
Methods. As a first application of this tool, we estimate photo-z for a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on ∼140 000 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 < 21) and low-redshift (z < 0.8) systems; however, we could use ∼6500 galaxies in the range 0.8 < z < 3 to effectively extend the training to higher redshift. The inputs are the r-band galaxy images plus the nine-band magnitudes and colors from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey.
Results. By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD = 0.014 for lower redshift and NMAD = 0.041 for higher redshift galaxies) and a low fraction of outliers (0.4% for lower and 1.27% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a ∼10%−35% improvement in precision at different redshifts and a ∼45% reduction in the fraction of outliers. We finally discuss the finding that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-z outlier fraction of galaxies can be cut down to 0.3%.
Key words: surveys / galaxies: general / techniques: photometric / galaxies: photometry
© R. Li et al. 2022
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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