Deep learning based photometric redshifts for the Kilo-Degree Survey Bright Galaxy Sample

Abstract

In cosmological analyses, precise redshift determination remains pivotal for understanding cosmic evolution. However, with only a fraction of galaxies having spectroscopic redshifts (spec-$z$s), the challenge lies in estimating redshifts for a larger number. To address this, photometry-based redshift (photo-$z$) estimation, employing machine learning algorithms, is a viable solution. Identifying the limitations of previous methods, this study focuses on implementing deep learning (DL) techniques within the Kilo-Degree Survey (KiDS) Bright Galaxy Sample for more accurate photo-$z$ estimations. Comparing our new DL-based model against prior `shallow' neural networks, we showcase improvements in redshift accuracy. Our model gives mean photo-$z$ bias $⟨\Delta z⟩=10^{-3}$ and scatter $\mathrm{SMAD}(\Delta z)=0.016$, where $\Delta z=(z_{\mathrm{phot}}-z_{\mathrm{spec}})/(1+z_{\mathrm{spec}})$. This research highlights the promising role of DL in revolutionizing photo-$z$ estimation.

Proceedings of the XLI Polish Astronomical Society Meeting, 11-15 September 2023, Toruń, Poland