Novelty Detection Meets Collider Physics
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Abstract (arXiv)
Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of density-based novelty evaluators, which are sensitive to the clustering of unknown-pattern testing data or new-physics signal events, for the design of detection algorithms. We also explore the influence of the known-pattern data fluctuations, arising from non-signal regions, on detection sensitivity. Strategies to address it are proposed. The algorithms are applied to detecting fermionic di-top partner and resonant di-top productions at LHC, and exotic Higgs decays of two specific modes at a e^+e^- future collider. With parton-level analysis, we conclude that potentially the new-physics benchmarks can be recognized with high efficiency.
Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jul 2018.
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Citing papers per year: 2021: 6; 2022: 6; 2023: 6; 2024: 1; 2025: 1.
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- 2021Machine Learning in the Search for New Fundamental Physicscited by 50
- 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
- 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
- 2021Topological Obstructions to Autoencodingcited by 20
- 2021Better Latent Spaces for Better Autoencoderscited by 20
- 2022Learning new physics efficiently with nonparametric methodscited by 20
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
- 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
- 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detectioncited by 13
- 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
- 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
- 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimationcited by 10
8 more
- 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
- 2023Unravelling physics beyond the standard model with classical and quantum anomaly detectioncited by 9
- 2022IRC-safe Graph Autoencoder for unsupervised anomaly detectioncited by 8
- 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Datacited by 7
- 2022Invariant Representation Driven Neural Classifier for Anti-QCD Jet Taggingcited by 4
- 2025Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPCcited by 1
- 2022Data-driven detector signal characterization with constrained bottleneck autoencoderscited by 0
- 2024Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practicecited by 0
- 2025Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC30 Dec 2025
- 2024Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice1 Mar 2024
- 2023Machine Learning for Anomaly Detection in Particle Physics20 Dec 2023
- 2023Improving new physics searches with diffusion models for event observables and jet constituents15 Dec 2023
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Review15 Dec 2023
- 2023High-dimensional and Permutation Invariant Anomaly Detection6 Jun 2023
- 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation8 May 2023
- 2023Unravelling physics beyond the standard model with classical and quantum anomaly detection25 Jan 2023
- 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning15 Sep 2022
- 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Data10 Aug 2022
- 2022IRC-safe Graph Autoencoder for unsupervised anomaly detection26 Apr 2022
- 2022Learning new physics efficiently with nonparametric methods5 Apr 2022
8 more
- 2022Data-driven detector signal characterization with constrained bottleneck autoencoders9 Mar 2022
- 2022Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging18 Jan 2022
- 2021Machine Learning in the Search for New Fundamental Physics7 Dec 2021
- 2021Challenges for Unsupervised Anomaly Detection in Particle Physics13 Oct 2021
- 2021Autoencoders for unsupervised anomaly detection in high energy physics19 Apr 2021
- 2021Better Latent Spaces for Better Autoencoders16 Apr 2021
- 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection5 Apr 2021
- 2021Topological Obstructions to Autoencoding16 Feb 2021
- 2021Machine Learning in the Search for New Fundamental Physicscited by 50
- 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
- 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
- 2021Topological Obstructions to Autoencodingcited by 20
- 2021Better Latent Spaces for Better Autoencoderscited by 20
- 2022Learning new physics efficiently with nonparametric methodscited by 20
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
- 2021Challenges for Unsupervised Anomaly Detection in Particle Physicscited by 16
- 2021Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detectioncited by 13
- 2023High-dimensional and Permutation Invariant Anomaly Detectioncited by 12
- 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
- 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimationcited by 10
12 most cited of 19 All 19 (JSON)
- 2025Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC30 Dec 2025
- 2024Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice1 Mar 2024
- 2023Machine Learning for Anomaly Detection in Particle Physics20 Dec 2023
- 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Review15 Dec 2023
- 2023High-dimensional and Permutation Invariant Anomaly Detection6 Jun 2023
- 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation8 May 2023
- 2023Unravelling physics beyond the standard model with classical and quantum anomaly detection25 Jan 2023
- 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning15 Sep 2022
- 2022Neural Embedding: Learning the Embedding of the Manifold of Physics Data10 Aug 2022
- 2022IRC-safe Graph Autoencoder for unsupervised anomaly detection26 Apr 2022
- 2022Learning new physics efficiently with nonparametric methods5 Apr 2022
- 2022Data-driven detector signal characterization with constrained bottleneck autoencoders9 Mar 2022
12 newest of 19 All 19 (JSON)