Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decays
Reconstructing the mass of a heavy resonance from its decay products with missing energy is one of the central tasks that directly determine the sensitivity in new physics searches at collider experiments. Supervised learning approaches to this problem often struggle to generalize well due to the presence of various systematic uncertainties and distribution shifts. Exhausting all possible variations in the labeled data can be very compute-intensive, while a failure of the model to generalize can corrupt the reconstructed resonance widths that are critical in peak-hunting analyses. In this work, following the foundation model paradigm, we use a self-supervised approach to pre-train a trans...
| Authors | Ho Fung Tsoi, Alex Yang, Luis Felipe Gutierrez Zagazeta et al. |
|---|---|
| Primary category | hep-ex |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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