Proposes foundation models and decision-theoretic policies to manage evolving source representations and optimize follow-up resource allocation in LSST-scale time-domain astronomy.
author Ishida , E
2 Pith papers cite this work. Polarity classification is still indexing.
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fields
astro-ph.IM 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
LStein is presented as a novel visualization approach for sparse 2.5-dimensional data, implemented in Python and demonstrated on astronomical lightcurves.
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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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LStein: A new approach to visualizing sparse 2.5-dimensional data
LStein is presented as a novel visualization approach for sparse 2.5-dimensional data, implemented in Python and demonstrated on astronomical lightcurves.