# Online-compatible Unsupervised Non-resonant Anomaly Detection

arXiv: https://arxiv.org/abs/2111.06417 · HTML: https://mlsanity.com/paper/2111.06417/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 114 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://mlsanity.com/paper/2006.10159/) (2020)
- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Anomaly Detection with Density Estimation](https://mlsanity.com/paper/2001.04990/) (2020)
- [Learning to Pivot with Adversarial Networks](https://mlsanity.com/paper/1611.01046/) (2016)
- [Anomaly Detection With Conditional Variational Autoencoders](https://mlsanity.com/paper/2010.05531/) (2020)
- [Fast convolutional neural networks on FPGAs with hls4ml](https://mlsanity.com/paper/2101.05108/) (2021)
- [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)
- [Autoencoders for unsupervised anomaly detection in high energy physics](https://mlsanity.com/paper/2104.09051/) (2021)
- [The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider](https://mlsanity.com/paper/2105.14027/) (2021)
- [Simulation Assisted Likelihood-free Anomaly Detection](https://mlsanity.com/paper/2001.05001/) (2020)
- [Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs](https://mlsanity.com/paper/2012.01563/) (2020)
- [Better Latent Spaces for Better Autoencoders](https://mlsanity.com/paper/2104.08291/) (2021)
- [Partial Distance Correlation with Methods for Dissimilarities](https://mlsanity.com/paper/1310.2926/) (2013)
- [Topological Obstructions to Autoencoding](https://mlsanity.com/paper/2102.08380/) (2021)
- [Bump Hunting in Latent Space](https://mlsanity.com/paper/2103.06595/) (2021)
- [Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark](https://mlsanity.com/paper/2005.01598/) (2020)
- [Challenges for Unsupervised Anomaly Detection in Particle Physics](https://mlsanity.com/paper/2110.06948/) (2021)
- [Simulation-Assisted Decorrelation for Resonant Anomaly Detection](https://mlsanity.com/paper/2009.02205/) (2020)
- [Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection](https://mlsanity.com/paper/2104.02092/) (2021)
- [Decorrelated Jet Substructure Tagging using Adversarial Neural Networks](https://mlsanity.com/paper/1703.03507/) (2017)
- [Unsupervised in-distribution anomaly detection of new physics through conditional density estimation](https://mlsanity.com/paper/2012.11638/) (2020)
- [A Cautionary Tale of Decorrelating Theory Uncertainties](https://mlsanity.com/paper/2109.08159/) (2021)
- [Anomaly Awareness](https://mlsanity.com/paper/2007.14462/) (2020)
- [Adversarial domain adaptation to reduce sample bias of a high energy physics classifier](https://mlsanity.com/paper/2005.00568/) (2020)
- [Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics](https://mlsanity.com/paper/2104.03408/) (2021)

## Built on by (most cited first, 15 of 15)

- [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)
- [Deep Generative Models for Detector Signature Simulation: A Taxonomic Review](https://mlsanity.com/paper/2312.09597/) (2023)
- [Anomalies, Representations, and Self-Supervision](https://mlsanity.com/paper/2301.04660/) (2023)
- [Improving new physics searches with diffusion models for event observables and jet constituents](https://mlsanity.com/paper/2312.10130/) (2023)
- [Neural Embedding: Learning the Embedding of the Manifold of Physics Data](https://mlsanity.com/paper/2208.05484/) (2022)
- [Machine-Learning Compression for Particle Physics Discoveries](https://mlsanity.com/paper/2210.11489/) (2022)
- [Machine-Learned Exclusion Limits without Binning](https://mlsanity.com/paper/2211.04806/) (2022)
- [Feature Selection with Distance Correlation](https://mlsanity.com/paper/2212.00046/) (2022)
- [Physics Community Needs, Tools, and Resources for Machine Learning](https://mlsanity.com/paper/2203.16255/) (2022)
- [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://mlsanity.com/paper/2603.17570/) (2026)
- [Unsupervised Symbolic Anomaly Detection](https://mlsanity.com/paper/2603.17575/) (2026)
- [TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches](https://mlsanity.com/paper/2503.04342/) (2025)
- [Rare anomalies require large datasets: About proving the existence of anomalies](https://mlsanity.com/paper/2508.09894/) (2025)
- [RangeAD: Fast On-Model Anomaly Detection](https://mlsanity.com/paper/2603.17795/) (2026)

## Abstract (arXiv)

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events - there must also be a strategy to provide context to the selected events. We propose the first complete strategy for unsupervised detection of non-resonant anomalies that includes both signal sensitivity and a data-driven method for background estimation. Our technique is built out of two simultaneously-trained autoencoders that are forced to be decorrelated from each other. This method can be deployed offline for non-resonant anomaly detection and is also the first complete online-compatible anomaly detection strategy. We show that our method achieves excellent performance on a variety of signals prepared for the ADC2021 data challenge.