# A cross-correlation-based estimate of the galaxy luminosity function

## TL;DR

This paper introduces a novel clustering-based method to accurately estimate galaxy redshift distributions and luminosity functions without relying on photometric redshifts, using only clustering data and apparent magnitudes.

## Contribution

It presents a new approach that simultaneously derives unbiased redshift distributions and redshift-dependent luminosity functions from clustering information, avoiding degeneracy with galaxy bias.

## Key findings

- Successfully applied to mock survey data
- Achieves unbiased redshift distributions
- Models luminosity bias shape effectively

## Abstract

We extend existing methods for using cross-correlations to derive redshift distributions for photometric galaxies, without using photometric redshifts. The model presented in this paper simultaneously yields highly accurate and unbiased redshift distributions and, for the first time, redshift-dependent luminosity functions, using only clustering information and the apparent magnitudes of the galaxies as input. In contrast to many existing techniques for recovering unbiased redshift distributions, the output of our method is not degenerate with the galaxy bias b(z), which is achieved by modelling the shape of the luminosity bias. We successfully apply our method to a mock galaxy survey and discuss improvements to be made before applying our model to real data.