Learning Spectral Templates for Photometric Redshift Estimation from Broadband Photometry

TL;DR

This paper introduces a machine learning approach to derive spectral templates from a large galaxy dataset, significantly improving photometric redshift accuracy by reducing outliers, bias, and scatter compared to traditional templates.

Contribution

We develop a novel training algorithm that learns spectral energy distributions directly from galaxy data, surpassing limitations of existing spectrophotometric and spectral synthesis methods.

Findings

Reduced outlier fraction in redshift estimates by up to 28%

Bias in redshift estimation decreased by up to 91%

Scatter in redshift estimates reduced by up to 25%

Abstract

Estimating redshifts from broadband photometry is often limited by how accurately we can map the colors of galaxies to an underlying spectral template. Current techniques utilize spectrophotometric samples of galaxies or spectra derived from spectral synthesis models. Both of these approaches have their limitations, either the sample sizes are small and often not representative of the diversity of galaxy colors or the model colors can be biased (often as a function of wavelength) which introduces systematics in the derived redshifts. In this paper we learn the underlying spectral energy distributions from an ensemble of $\sim$100K galaxies with measured redshifts and colors. We show that we are able to reconstruct emission and absorption lines at a significantly higher resolution than the broadband filters used to measure the photometry for a sample of 20 spectral templates. We find that our training algorithm reduces the fraction of outliers in the derived photometric redshifts by up to 28%, bias up to 91%, and scatter up to 25%, when compared to estimates using a standard set of spectral templates. We discuss the current limitations of this approach and its applicability for recovering the underlying properties of galaxies. Our derived templates and the code used to produce these results are publicly available in a dedicated Github repository: https://github.com/dirac-institute/photoz_template_learning.