RINO: Renormalization Group Distillation with No Labels
Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics. This leads to significant inaccuracies and biases in the transfer of trained models to real data, limiting the power of deep learning methods. Self-supervised learning (SSL) is a promising alternative for pretraining directly on real, unlabeled data. However, existing techniques largely operate with inductive biases inspired by domains such as images and natural language. We propose RINO (Renormalization Group DIstillation with NO Labels), which, for the first time, exploits the fundamentally multi-scale renor...
| Authors | Zichun Hao, Raghav Kansal, Abhijith Gandrakota et al. |
|---|---|
| Primary category | hep-ex |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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