Year: 2026
Type: preprint
Abstract: 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 renormalization group structure of high-energy jet formation as a physically grounded source of SSL views. We define teacher-student representations across energy scales using different levels of hierarchical clustering, aligned via DINO-style self-distillation. To mirror the background-dominated nature of real collider data, RINO pretrains exclusively on jets from quantum chromodynamics (QCD) processes and is evaluated on out-of-distribution (OOD) transfer to rare signal classes. In our controlled data-efficiency study, RINO matches fully supervised training on JetNet after finetuning on only 1% of the labeled data. With the full labeled dataset, RINO improves OOD classification accuracy on top-quark jets by 5.9% and, without retraining, improves AUC on the unseen Higgs boson decay channel $H \to 4q$ by 4.1% over the strongest SSL baseline. Scale-structure diagnostics reveal that DINO-style self-distillation permits our backbone to retain scale-dependent structure. Ablation studies show that this accounts for an 8.6% OOD accuracy gap with respect to alternative SSL paradigms such as SimCLR. Code is available at https://github.com/zichunhao/RINO.
Source: arXiv (Cornell University)
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, Alfred P. Sloan Foundation, Research Corporation for Science Advancement, California Institute of Technology, Office of Science, University of California, San Diego, Office of the President, University of California, Advanced Scientific Computing Research, High Energy Physics, Laboratory Directed Research and Development, Fermilab
Awards: G1387744893, G1585335955, CI-New: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI), Collaborative Proposal: Geodynamic Tomography: A New Approach to Studies of Mantle Upwelling and its Application to the Lau Basin and The East Pacific Rise, G2011917337, G2091642509, G2376186588, G2926102538, G3270322552, Towards Deploying a Science-Driven Regional Interoperable Science DMZ, G4923312906, CC* NPEO: Toward the National Research Platform, G5218904322, CCRI: ABR: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI), Category II: A Prototype National Research Platform, G6314282115, G6471802762, HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery, G6632354235, CC*DNI DIBBs: The Pacific Research Platform, G8890396195, CCRI: ENS: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI), G8989177975
OpenAlex ID: W7221831693