<ul data-eligibleForWebStory="false"><li>Machine learning is crucial for classifying astronomical transients, but existing approaches have limitations when applied to real data and different surveys.</li><li>Transfer learning shows promise in overcoming these challenges by using existing models trained on simulations or data from other surveys.</li><li>A model trained on simulated Zwicky Transient Facility (ZTF) data demonstrates that transfer learning can significantly reduce the labeled data needed for real ZTF transients by 95% while maintaining performance.</li><li>Transfer learning also enables adapting ZTF models for LSST simulations with 94% performance using only 30% of the training data, promising reliable automated classification for LSST early operations.</li></ul>

Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST

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