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, self-, and semi-supervised deep learning photo- z algorithms out to z ∼ 3. 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, the Photo- z Inference with a Triple-task Algorithm ( PITA ), 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.
Khederlarian et al. (Mon,) studied this question.