mlsanityData to 7 Oct 2026

Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging

arXiv 2201.07199 ↗First posted 18 Jan 2022hep-ph cs.LG hep-excited by 4

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Abstract (arXiv)

We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that, with a well-calibrated and powerful enough feature extractor, a well-trained mass-decorrelated supervised Standard Model neural jet classifier can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing data-augmented mass-invariance (and thus decoupling the dominant factor) not only facilitates background estimation, but also induces more substructure-aware representation learning. We are able to reach excellent tagging efficiencies for all the test signals considered. In the best case, we reach a background rejection rate of 51 and a significance improvement factor of 3.6 at 50 % signal acceptance, with the jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 33 of its 64 references that are in the map. First posted on arXiv 18 Jan 2022.

Builds on

33 of 64 references.

References · 33

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21 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2023Anomalies, Representations, and Self-Supervisioncited by 10
  2. 2023Versatile Energy-Based Probabilistic Models for High Energy Physicscited by 2
  3. 2024Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learningcited by 1
  4. 2022Bridging Machine Learning and Sciences: Opportunities and Challengescited by 0