# Machine Learning for Anomaly Detection in Particle Physics

arXiv: https://arxiv.org/abs/2312.14190 · HTML: https://mlsanity.com/paper/2312.14190/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 182 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 30 of 57)

- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Noise-Induced Barren Plateaus in Variational Quantum Algorithms](https://mlsanity.com/paper/2007.14384/) (2020)
- [Quantum advantage in learning from experiments](https://mlsanity.com/paper/2112.00778/) (2021)
- [Connecting ansatz expressibility to gradient magnitudes and barren plateaus](https://mlsanity.com/paper/2101.02138/) (2021)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Quantum machine learning beyond kernel methods](https://mlsanity.com/paper/2110.13162/) (2021)
- [The Inductive Bias of Quantum Kernels](https://mlsanity.com/paper/2106.03747/) (2021)
- [Exponential concentration in quantum kernel methods](https://mlsanity.com/paper/2208.11060/) (2022)
- [Anomaly Detection with Density Estimation](https://mlsanity.com/paper/2001.04990/) (2020)
- [Quantum Graph Neural Networks](https://mlsanity.com/paper/1909.12264/) (2019)
- [Quantum Convolutional Neural Networks for High Energy Physics Data Analysis](https://mlsanity.com/paper/2012.12177/) (2020)
- [FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks](https://mlsanity.com/paper/1809.04570/) (2018)
- [Machine Learning in High Energy Physics Community White Paper](https://mlsanity.com/paper/1807.02876/) (2018)
- [Classification without labels: Learning from mixed samples in high energy physics](https://mlsanity.com/paper/1708.02949/) (2017)
- [Dirichlet Variational Autoencoder](https://mlsanity.com/paper/1901.02739/) (2019)
- [Variational Autoencoders for New Physics Mining at the Large Hadron Collider](https://mlsanity.com/paper/1811.10276/) (2018)
- [Quantum support vector machine for big data classification](https://mlsanity.com/paper/1307.0471/) (2013)
- [Variational Autoencoders for Anomalous Jet Tagging](https://mlsanity.com/paper/2007.01850/) (2020)

## Built on by (most cited first, 30 of 30)

- [Multiple testing for signal-agnostic searches of new physics with machine learning](https://mlsanity.com/paper/2408.12296/) (2024)
- [Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations](https://mlsanity.com/paper/2501.05588/) (2025)
- [Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC](https://mlsanity.com/paper/2506.00102/) (2025)
- [Guided Quantum Compression for High Dimensional Data Classification](https://mlsanity.com/paper/2402.09524/) (2024)
- [Universal New Physics Latent Space](https://mlsanity.com/paper/2407.20315/) (2024)
- [Enhancing anomaly detection with topology-aware autoencoders](https://mlsanity.com/paper/2502.10163/) (2025)
- [Agents of Discovery](https://mlsanity.com/paper/2509.08535/) (2025)
- [Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice](https://mlsanity.com/paper/2605.31103/) (2026)
- [Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction](https://mlsanity.com/paper/2509.22059/) (2025)
- [AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing](https://mlsanity.com/paper/2510.21935/) (2025)
- [Learning Reduced Representations for Quantum Classifiers](https://mlsanity.com/paper/2512.01509/) (2025)
- [Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders](https://mlsanity.com/paper/2603.26604/) (2026)
- [Generative Models and Statistical Validation](https://mlsanity.com/paper/2605.30453/) (2026)
- [Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey](https://mlsanity.com/paper/2403.11934/) (2024)
- [Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC](https://mlsanity.com/paper/2410.13799/) (2024)
- [A Step Toward Interpretability: Smearing the Likelihood](https://mlsanity.com/paper/2501.07643/) (2025)
- [Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies](https://mlsanity.com/paper/2511.03095/) (2025)
- [A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation](https://mlsanity.com/paper/2607.27501/) (2026)
- [Can AI Follow In Einstein's Footsteps?](https://mlsanity.com/paper/2607.27794/) (2026)
- [Anomaly Detection from a Tensor Train Perspective](https://mlsanity.com/paper/2409.15030/) (2024)
- [AI-based particle track identification in scintillating fibres read out with imaging sensors](https://mlsanity.com/paper/2410.10519/) (2024)
- [The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations](https://mlsanity.com/paper/2501.03383/) (2025)
- [Generator Based Inference (GBI)](https://mlsanity.com/paper/2506.00119/) (2025)
- [Shedding Light on Dark Matter at the LHC with Machine Learning](https://mlsanity.com/paper/2509.15121/) (2025)
- [Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach](https://mlsanity.com/paper/2604.06541/) (2026)
- [Learning Standard Model structure from LHC data with Riemannian flow matching](https://mlsanity.com/paper/2607.16144/) (2026)
- [Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments](https://mlsanity.com/paper/2607.20302/) (2026)
- [Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders](https://mlsanity.com/paper/2607.24921/) (2026)
- [Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments](https://mlsanity.com/paper/2608.13652/) (2026)
- [Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC](https://mlsanity.com/paper/2610.13437/) (2026)

## Abstract (arXiv)

The detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected events that may be indicative of new phenomena or physics beyond the Standard Model. Recent advances in Machine Learning for anomaly detection have encouraged the utilization of such techniques on particle physics problems. This review article provides an overview of the state-of-the-art techniques for anomaly detection in particle physics using machine learning. We discuss the challenges associated with anomaly detection in large and complex data sets, such as those produced by high-energy particle colliders, and highlight some of the successful applications of anomaly detection in particle physics experiments.