A Bayesian CNN maps 2D slitless spectral images to redshift estimates with NMAD precision 0.0104 for SNR_GI >=1 and better for brighter sources, while remaining robust to wavelength calibration errors via spatial augmentations.
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4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Low-luminosity FRII radio galaxies show higher core prevalence, comparable hotspots, and ~32% restarting/remnant behavior compared to bright FRIIs, revealing a highly diverse population where FRII dynamics occur at low powers.
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.
Random Forest regression on combined optical plus mid-infrared colors yields NMAD of 0.0188, R-squared of 0.9561, and 0.294 percent outliers for photometric redshifts in 23,797 Seyfert II galaxies selected from SDSS and WISE.
citing papers explorer
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Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey
A Bayesian CNN maps 2D slitless spectral images to redshift estimates with NMAD precision 0.0104 for SNR_GI >=1 and better for brighter sources, while remaining robust to wavelength calibration errors via spatial augmentations.
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The diverse morphologies and evolution of low-luminosity edge-brightened radio galaxies
Low-luminosity FRII radio galaxies show higher core prevalence, comparable hotspots, and ~32% restarting/remnant behavior compared to bright FRIIs, revealing a highly diverse population where FRII dynamics occur at low powers.
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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.
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Predicting Redshift in Seyfert Galaxies Using Machine Learning
Random Forest regression on combined optical plus mid-infrared colors yields NMAD of 0.0188, R-squared of 0.9561, and 0.294 percent outliers for photometric redshifts in 23,797 Seyfert II galaxies selected from SDSS and WISE.