Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z

TL;DR

SYTH-Z introduces a new method for photometric redshift estimation using physically motivated synthetic spectra, reducing biases and improving accuracy over traditional observational data-based models.

Contribution

The paper presents a novel framework that employs synthetic spectral energy distributions and a mixture density network for more accurate redshift estimation.

Findings

Superior accuracy across a wide redshift range compared to baseline models.

Effective reduction of systematic differences through zero-point re-calibration.

Mitigates reliance on costly spectroscopic follow-up.

Abstract

Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potential deficiencies, such as sample bias and incompleteness. Motivated by these considerations, we propose using physically motivated synthetic spectral energy distributions in redshift estimation. In addition, the synthetic data would have to span a domain in colour-redshift space concordant with that of the targeted observational surveys. With a matched distribution and realistically modelled synthetic data in hand, a suitable regression algorithm can be appropriately trained; we use a mixture density network for this purpose. We also perform a zero-point re-calibration to reduce the systematic differences between noise-free synthetic data and the (unavoidably) noisy observational data sets. This new redshift estimation framework, SYTH-Z, demonstrates superior accuracy over a wide range of redshifts compared to baseline models trained on observational data alone. Approaches using realistic synthetic data sets can therefore greatly mitigate the reliance on expensive spectroscopic follow-up for the next generation of photometric surveys.