Archetype-Based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey

TL;DR

This paper introduces an improved galaxy redshift estimation method for DESI that uses physical archetypes, leading to higher accuracy and fewer failures compared to existing PCA-based templates.

Contribution

The proposed archetype-based approach enhances redshift accuracy and reduces catastrophic failures, offering a more physical and robust alternative to PCA template fitting in large spectroscopic surveys.

Findings

10-30% reduction in catastrophic redshift failures

0.5-0.8% higher redshift success and purity rates for galaxies

5-40% reduction in false positive redshift estimates for sky fibers

Abstract

We present a computationally efficient galaxy archetype-based redshift estimation and spectral classification method for the Dark Energy Survey Instrument (DESI) survey. The DESI survey currently relies on a redshift fitter and spectral classifier using a linear combination of PCA-derived templates, which is very efficient in processing large volumes of DESI spectra within a short time frame. However, this method occasionally yields unphysical model fits for galaxies and fails to adequately absorb calibration errors that may still be occasionally visible in the reduced spectra. Our proposed approach improves upon this existing method by refitting the spectra with carefully generated physical galaxy archetypes combined with additional terms designed to absorb data reduction defects and provide more physical models to the DESI spectra. We test our method on an extensive dataset derived from the survey validation (SV) and Year 1 (Y1) data of DESI. Our findings indicate that the new method delivers marginally better redshift success for SV tiles while reducing catastrophic redshift failure by $10-30\%$. At the same time, results from millions of targets from the main survey show that our model has relatively higher redshift success and purity rates ($0.5-0.8\%$ higher) for galaxy targets while having similar success for QSOs. These improvements also demonstrate that the main DESI redshift pipeline is generally robust. Additionally, it reduces the false positive redshift estimation by $5-40\%$ for sky fibers. We also discuss the generic nature of our method and how it can be extended to other large spectroscopic surveys, along with possible future improvements.