# Autoencoders for unsupervised anomaly detection in high energy physics

arXiv: https://arxiv.org/abs/2104.09051 · HTML: https://mlsanity.com/paper/2104.09051/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 68 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)
- [Do Deep Generative Models Know What They Don't Know?](https://mlsanity.com/paper/1810.09136/) (2018)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Why Normalizing Flows Fail to Detect Out-of-Distribution Data](https://mlsanity.com/paper/2006.08545/) (2020)
- [Input complexity and out-of-distribution detection with likelihood-based generative models](https://mlsanity.com/paper/1909.11480/) (2019)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features](https://mlsanity.com/paper/2006.10848/) (2020)
- [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)
- [Variational Autoencoders for New Physics Mining at the Large Hadron Collider](https://mlsanity.com/paper/1811.10276/) (2018)
- [Variational Autoencoders for Anomalous Jet Tagging](https://mlsanity.com/paper/2007.01850/) (2020)
- [Weakly Supervised Classification in High Energy Physics](https://mlsanity.com/paper/1702.00414/) (2017)
- [Better Latent Spaces for Better Autoencoders](https://mlsanity.com/paper/2104.08291/) (2021)
- [Novelty Detection Meets Collider Physics](https://mlsanity.com/paper/1807.10261/) (2018)
- [Topological Obstructions to Autoencoding](https://mlsanity.com/paper/2102.08380/) (2021)
- [Bump Hunting in Latent Space](https://mlsanity.com/paper/2103.06595/) (2021)
- [Jet Constituents for Deep Neural Network Based Top Quark Tagging](https://mlsanity.com/paper/1704.02124/) (2017)
- [Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection](https://mlsanity.com/paper/2104.02092/) (2021)
- [Learning to Classify from Impure Samples with High-Dimensional Data](https://mlsanity.com/paper/1801.10158/) (2018)
- [(Machine) Learning to Do More with Less](https://mlsanity.com/paper/1706.09451/) (2017)
- [Machine Learning on data with sPlot background subtraction](https://mlsanity.com/paper/1905.11719/) (2019)

## Built on by (most cited first, 30 of 32)

- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [Machine Learning for Anomaly Detection in Particle Physics](https://mlsanity.com/paper/2312.14190/) (2023)
- [Learning new physics efficiently with nonparametric methods](https://mlsanity.com/paper/2204.02317/) (2022)
- [Deep Generative Models for Detector Signature Simulation: A Taxonomic Review](https://mlsanity.com/paper/2312.09597/) (2023)
- [Challenges for Unsupervised Anomaly Detection in Particle Physics](https://mlsanity.com/paper/2110.06948/) (2021)
- [Online-compatible Unsupervised Non-resonant Anomaly Detection](https://mlsanity.com/paper/2111.06417/) (2021)
- [Lorentz group equivariant autoencoders](https://mlsanity.com/paper/2212.07347/) (2022)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Improving new physics searches with diffusion models for event observables and jet constituents](https://mlsanity.com/paper/2312.10130/) (2023)
- [Anomaly Detection Based on Isolation Mechanisms: A Survey](https://mlsanity.com/paper/2403.10802/) (2024)
- [Autoencoders for Semivisible Jet Detection](https://mlsanity.com/paper/2112.02864/) (2021)
- [IRC-safe Graph Autoencoder for unsupervised anomaly detection](https://mlsanity.com/paper/2204.12231/) (2022)
- [Neural Embedding: Learning the Embedding of the Manifold of Physics Data](https://mlsanity.com/paper/2208.05484/) (2022)
- [Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder](https://mlsanity.com/paper/2311.17162/) (2023)
- [Generating Physical Dynamics under Priors](https://mlsanity.com/paper/2409.00730/) (2024)
- [Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging](https://mlsanity.com/paper/2201.07199/) (2022)
- [Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments](https://mlsanity.com/paper/2312.13896/) (2023)
- [Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model](https://mlsanity.com/paper/2402.10204/) (2024)
- [Generative Invertible Quantum Neural Networks](https://mlsanity.com/paper/2302.12906/) (2023)
- [AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection](https://mlsanity.com/paper/2305.16497/) (2023)
- [Versatile Energy-Based Probabilistic Models for High Energy Physics](https://mlsanity.com/paper/2302.00695/) (2023)
- [Targeted collapse regularized autoencoder for anomaly detection: black hole at the center](https://mlsanity.com/paper/2306.12627/) (2023)
- [AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing](https://mlsanity.com/paper/2510.21935/) (2025)
- [Triggering Dark Showers with Conditional Dual Auto-Encoders](https://mlsanity.com/paper/2306.12955/) (2023)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC](https://mlsanity.com/paper/2512.24290/) (2025)
- [Radial Autoencoders for Enhanced Anomaly Detection](https://mlsanity.com/paper/2203.15884/) (2022)
- [AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework](https://mlsanity.com/paper/2404.07968/) (2024)
- [A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System](https://mlsanity.com/paper/2405.02574/) (2024)
- [TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches](https://mlsanity.com/paper/2503.04342/) (2025)

## Abstract (arXiv)

Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches. We scrutinize the usage of autoencoders for unsupervised anomaly detection based on reconstruction loss to show their capabilities, but also their limitations. As a particle physics benchmark scenario, we study the tagging of top jet images in a background of QCD jet images. Although we reproduce the positive results from the literature, we show that the standard autoencoder setup cannot be considered as a model-independent anomaly tagger by inverting the task: due to the sparsity and the specific structure of the jet images, the autoencoder fails to tag QCD jets if it is trained on top jets even in a semi-supervised setup. Since the same autoencoder architecture can be a good tagger for a specific example of an anomaly and a bad tagger for a different example, we suggest improved performance measures for the task of model-independent anomaly detection. We also improve the capability of the autoencoder to learn non-trivial features of the jet images, such that it is able to achieve both top jet tagging and the inverse task of QCD jet tagging with the same setup. However, we want to stress that a truly model-independent and powerful autoencoder-based unsupervised jet tagger still needs to be developed.