PITA, a new semi-supervised deep learning algorithm, outperforms prior photo-z methods by using a triple-task loss on images, colors, and available redshifts to produce a smooth latent space.
and others , title =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Neural network classification with CRPS optimization produces calibrated photometric redshift PDFs for DESI Legacy and Pan-STARRS data, achieving σ_NMAD of 0.0153 on LSDR10 and outperforming regression methods.
The first circumgalactic dust reddening measurement from Rubin DP1 data finds A_V proportional to r_perp to the -1.8 power within 120 kpc, consistent with prior SDSS/KiDS/DES results despite 1000x smaller area and fainter foreground sample.
Hyrax is a GPU-enabled open-source framework for the full ML lifecycle in astronomy, with demonstrations of unsupervised discovery and classification on real survey data from Rubin, ZTF, and other projects.
citing papers explorer
-
Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS
PITA, a new semi-supervised deep learning algorithm, outperforms prior photo-z methods by using a triple-task loss on images, colors, and available redshifts to produce a smooth latent space.
-
Photometric Redshift PDFs via Neural Network Classification for DESI Legacy Imaging Surveys and Pan-STARRS
Neural network classification with CRPS optimization produces calibrated photometric redshift PDFs for DESI Legacy and Pan-STARRS data, achieving σ_NMAD of 0.0153 on LSDR10 and outperforming regression methods.
-
A First Measurement of Circumgalactic Dust Reddening from Only 4.6 deg$^2$ of the Rubin Observatory's DP1
The first circumgalactic dust reddening measurement from Rubin DP1 data finds A_V proportional to r_perp to the -1.8 power within 120 kpc, consistent with prior SDSS/KiDS/DES results despite 1000x smaller area and fainter foreground sample.
-
Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid
Hyrax is a GPU-enabled open-source framework for the full ML lifecycle in astronomy, with demonstrations of unsupervised discovery and classification on real survey data from Rubin, ZTF, and other projects.