A cross-entropy metric is introduced to distinguish transient populations and support novelty detection for LSST observing strategy optimization.
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A convLSTM classifier identifies lensed SNe Ia in simulated LSST-like time series, reaching ~60% true-positive rate at O(10^{-4}) false-positive rate by the seventh epoch even after adding realistic PSF variations and foreground SN contaminants.
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An Information-Theoretic Metric for Transient Classification and Novelty Detection
A cross-entropy metric is introduced to distinguish transient populations and support novelty detection for LSST observing strategy optimization.
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HOLISMOKES XXI: Detecting strongly lensed type Ia supernovae from time series of multi-band LSST-like imaging data -- Part II
A convLSTM classifier identifies lensed SNe Ia in simulated LSST-like time series, reaching ~60% true-positive rate at O(10^{-4}) false-positive rate by the seventh epoch even after adding realistic PSF variations and foreground SN contaminants.