mlsanityData to 13 Oct 2026

RINO: Renormalization Group Invariance with No Labels

arXiv 2509.07486 ↗First posted 9 Sep 2025hep-ex cs.LGcited by 6

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Abstract (arXiv)

A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying collision or detector response. To help mitigate this problem of domain shift, we propose RINO (Renormalization Group Invariance with No Labels), a self-supervised learning approach that can instead pretrain models directly on collision data, learning embeddings invariant to renormalization group flow scales. In this work, we pretrain a transformer-based model on jets originating from quantum chromodynamic (QCD) interactions from the JetClass dataset, emulating real QCD-dominated experimental data, and then finetune on the JetNet dataset -- emulating simulations -- for the task of identifying jets originating from top quark decays. RINO demonstrates improved generalization from the JetNet training data to JetClass data compared to supervised training on JetNet from scratch, demonstrating the potential for RINO pretraining on real collision data followed by fine-tuning on small, high-quality MC datasets, to improve the robustness of ML models in HEP.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 31 of its 53 references that are in the map. First posted on arXiv 9 Sep 2025.

Builds on

31 of 53 references.

References · 31

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19 more
  1. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
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  3. 2022Particle Transformer for Jet Taggingcited by 97
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Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Enhancing next token prediction based pre-training for jet foundation modelscited by 3
  2. 2025Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physicscited by 2
  3. 2026jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillationcited by 1
  4. 2026Panda Diplomacy: Foundation Model Pre-training across Particle Imaging Detectors for High Energy and Nuclear Physicscited by 1
  5. 2026Self-Supervised Learning for Robust Resonance Mass Regression in Cascade Decayscited by 0
  6. 2026Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHCcited by 0