Galaxy morphoto-Z with neural Networks (GaZNets)

Abstract

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%.

The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields

“…Bilicki et al 2021), and using machine learning directly on images (see e.g. Li et al 2022). These efforts, however, will not produce photo-z estimates that are included in the formal DR5 ESO release.…”

Euclidpreparation

“…In particular, convolutional neural networks (CNNs; e.g., LeCun et al 1989) are a DL algorithm that has been successfully applied to several astrophysical problems and is expected to play a key role in the future of astronomical data analysis. Among the many different applications, they have been employed to estimate the photometric redshifts of luminous sources (e.g., Pasquet et al 2019;Shuntov et al 2020;Li et al 2022), to perform the morphological classification of galaxies (e.g., Huertas-Company et al 2015;Domínguez Sánchez et al 2018;Zhu et al 2019;Ghosh et al 2020), to constrain the cosmological parameters (e.g., Merten et al 2019;Fluri et al 2019;Pan et al 2020), to identify cluster members (e.g., Angora et al 2020), to find galaxy-scale strong lenses in galaxy clusters (e.g., Angora et al 2023), to quantify galaxy metallicities (e.g., Wu & Boada 2019;Liew-Cain et al 2021), and to estimate the dynamical masses of galaxy clusters (e.g., Ho et al 2019;Gupta & Reichardt 2020).…”