An RNN-plus-attention model that ingests classification histories outperforms standard final-label classifiers on ELAsTiCC synthetic data and is accompanied by new Wasserstein-based metrics for temporal stability and early performance.
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Beyond the Final Label: Exploiting the Untapped Potential of Classification Histories in Astronomical Light Curve Analysis
An RNN-plus-attention model that ingests classification histories outperforms standard final-label classifiers on ELAsTiCC synthetic data and is accompanied by new Wasserstein-based metrics for temporal stability and early performance.