Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS

TL;DR

This paper evaluates deep learning methods for photometric redshift estimation using HST/CANDELS data, introducing a semi-supervised model that outperforms others, especially with limited labeled data, for the Roman Space Telescope.

Contribution

The paper presents a novel semi-supervised deep learning model, PITA, that improves photometric redshift accuracy by effectively leveraging unlabeled data and outperforms existing methods.

Findings

Semi-supervised PITA outperforms fully-supervised and classical methods.

PITA's latent space varies smoothly with redshift, magnitude, and color.

Semi-supervised approach remains effective with reduced labeled data.

Abstract

Photometric redshifts (photo-$z$'s) will be crucial for studies of galaxy evolution, large-scale structure, and transients with the Nancy Grace Roman Space Telescope. Deep learning methods leverage pixel-level information from ground-based images to achieve the best photo-$z$'s for low-redshift galaxies, but their efficacy at higher redshifts with deep, space-based imaging remains largely untested. We used Hubble Space Telescope CANDELS optical and near-infrared imaging to evaluate fully-supervised, self-supervised, and semi-supervised deep learning photo-$z$ algorithms out to $z\sim3$. Compared to template-based and classical machine learning photometry methods, the fully-supervised and semi-supervised models achieved better performance. Our new semi-supervised model, PITA (Photo-$z$ Inference with a Triple-task Algorithm), outperformed all others by learning from unlabeled and labeled data through a three-part loss function that incorporates images and colors for all objects as well as redshifts when available. PITA produces a latent space that varies smoothly in magnitude, color, and redshift, resulting in the best photo-$z$ performance even when the redshift training set was significantly reduced. In contrast, the self-supervised approach produced a latent space with significant color and redshift fluctuations that hindered photo-$z$ inference. Looking forward to Roman, we recommend using semi supervised deep learning to take full advantage of the information contained in the hundreds of millions of high-resolution images and color measurements, together with the limited redshift measurements available, to achieve the most accurate photo-$z$ estimates for both faint and bright sources.