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Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Time domain surveys generate many transient candidates, making Real Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while community labels can be noisy and survey dependent. We aim to develop a Real Bogus classification framework that can be trained without human labeled data using injected transients and bogus dominated survey data, remains robust under strong cl...