# Representation learning for automated spectroscopic redshift estimation

## TL;DR

This paper introduces novel machine learning methods, including dictionary learning and autoencoders, for more accurate and reliable spectroscopic redshift estimation in galaxy surveys, outperforming traditional template matching techniques.

## Contribution

It presents two innovative representation learning techniques for galaxy spectra that enhance redshift estimation accuracy over classical methods.

## Key findings

- Both methods outperform eigentemplates in accuracy.
- Improved reliability across different signal-to-noise ratios.
- Effective on realistic simulated survey data.

## Abstract

Determining the radial positions of galaxies up to a high accuracy depends on the correct identification of salient features in their spectra. Classical techniques for spectroscopic redshift estimation make use of template matching with cross-correlation. These templates are usually constructed from empirical spectra or simulations based on the modeling of local galaxies. We propose two new spectroscopic redshift estimation schemes based on new learning techniques for galaxy spectra representation, using either a dictionary learning technique for sparse representation or denoising autoencoders. We investigate how these representations impact redshift estimation. These methods have been tested on realistic simulated galaxy spectra, with photometry modelled after the Large Synoptic Survey Telescope (LSST) and spectroscopy reproducing properties of the Sloan Digital Sky Survey (SDSS). They were compared to Darth Fader, a robust technique extracting line features and estimating redshift through eigentemplates cross-correlations. We show that both dictionary learning and denoising autoencoders provide improved accuracy and reliability across all signal-to-noise regimes and galaxy types. The representation learning framework for spectroscopic redshift analysis introduced in this work offers high performance in feature extraction and redshift estimation, improving on a classical eigentemplates approach. This is a necessity for next-generation galaxy surveys, and we demonstrate a successful application in realistic simulated survey data.