# Cooperative photometric redshift estimation

## TL;DR

This paper presents a hybrid approach combining SED fitting and machine learning to improve the accuracy of photometric redshift estimation in galaxy surveys, demonstrating enhanced results over individual methods.

## Contribution

It introduces a collaborative framework that integrates SED fitting with machine learning to reduce errors and outliers in photometric redshift predictions.

## Key findings

- Hybrid approach improves prediction accuracy
- SED fitting helps classify galaxy spectral types
- Reduced catastrophic outliers in redshift estimates

## Abstract

In the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of about 25,000 galaxies from the second data release of the Kilo Degree Survey (KiDS) we obtain photometric redshifts with five different methods: (i) Random forest, (ii) Multi Layer Perceptron with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric Redshift model (or BPZ) and (v) a classical SED template fitting procedure (Le Phare). We show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. We use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.