Effortless reconstructs Roman images 50–60× faster than Imcom; one calibrated single-image output beats ~6 coadded Imcom images on ideal point-source moments.
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5 Pith papers cite this work, alongside 9 external citations. Polarity classification is still indexing.
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Filter-substrate refraction causes dominant lateral shifts yielding 0.3-0.4% PSF size and ellipticity residuals across most Roman bands that exceed weak lensing requirements by an order of magnitude, while longitudinal defocus shifts remain negligible.
A denoising diffusion model trained on transformed JWST observations generates multi-band galaxy images that match key statistical properties of real galaxies for Roman weak lensing simulations.
Proposes foundation models and decision-theoretic policies to manage evolving source representations and optimize follow-up resource allocation in LSST-scale time-domain astronomy.
Machine learning models RuBR_comb, RuBR_loc, and RuBR_DA for real-bogus classification of transients using combined simulated data and domain adaptation for the Roman RAPID pipeline.
citing papers explorer
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Efficient Optimal Image Reconstruction for the Nancy Grace Roman Space Telescope and Beyond: I. First Results with {\sc Effortless}
Effortless reconstructs Roman images 50–60× faster than Imcom; one calibrated single-image output beats ~6 coadded Imcom images on ideal point-source moments.
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Modeling the impact of filter-substrate refraction in the Roman point spread function
Filter-substrate refraction causes dominant lateral shifts yielding 0.3-0.4% PSF size and ellipticity residuals across most Roman bands that exceed weak lensing requirements by an order of magnitude, while longitudinal defocus shifts remain negligible.
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Diffusion-based Galaxy Simulations for the Roman High Latitude Survey
A denoising diffusion model trained on transformed JWST observations generates multi-band galaxy images that match key statistical properties of real galaxies for Roman weak lensing simulations.
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Toward decision-aware AI for LSST-scale time-domain astronomy
Proposes foundation models and decision-theoretic policies to manage evolving source representations and optimize follow-up resource allocation in LSST-scale time-domain astronomy.
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Identifying Gems from Roman RAPIDly
Machine learning models RuBR_comb, RuBR_loc, and RuBR_DA for real-bogus classification of transients using combined simulated data and domain adaptation for the Roman RAPID pipeline.