Deep Set Auto Encoders for Anomaly Detection in Particle Physics

@article{Ostdiek2022DeepSA,
  title={Deep Set Auto Encoders for Anomaly Detection in Particle Physics},
  author={Bryan Ostdiek},
  journal={SciPost Physics},
  year={2022}
}
  • B. Ostdiek
  • Published 3 September 2021
  • Computer Science
  • SciPost Physics
There is an increased interest in model agnostic search strategies for physics beyond the standard model at the Large Hadron Collider. We introduce a Deep Set Variational Autoencoder and present results on the Dark Machines Anomaly Score Challenge. We find that the method attains the best anomaly detection ability when there is no decoding step for the network, and the anomaly score is based solely on the representation within the encoded latent space. This method was one of the top-performing… 

Figures from this paper

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

TLDR
This work investigates how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider by exploiting normalizing flow layers in the latent space of the variational Autoencoder.

AT THE LHC WITH N ORMALIZING F LOWS

TLDR
This work investigates how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider by exploiting normalizing flow layers in the latent space of the variational Autoencoder.

Creating simple, interpretable anomaly detectors for new physics in jet substructure

TLDR
This work proposes two strategies that use a small number of high-level observables to mimic the decisions made by the autoencoder on background events, one designed to directly learn the output of the authencoder, and the other designed to learn the difference between the aut Koencoder’s outputs on a pair of events.

Challenges for Unsupervised Anomaly Detection in Particle Physics

TLDR
It is found that the hyperparameter choices strongly affect the network performance and that the optimal parameters for one signal are non-optimal for another, which means that the choices that best represent the background are not necessarily best for signal identification.

The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

TLDR
The outcome of a data challenge to detect signals of new physics at the Large Hadron Collider using unsupervised machine learning algorithms, and a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge.

Anomaly detection in high-energy physics using a quantum autoencoder

TLDR
It is shown that a simple quantum autoencoder outperforms classical autoencoders for the same inputs and trains very well, and this performance is reproducible on present quantum devices, shows that quantum aut Koencoders are good candidates for analysing high-energy physics data in future LHC runs.

Quantum Anomaly Detection for Collider Physics

TLDR
An anomaly detection task in the four-lepton state at the Large Hadron Collider that is limited by a small dataset is studied and there is no evidence that QML provides any advantage over classical ML.

Symmetries, safety, and self-supervision

Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of

Simulation-based Anomaly Detection for Multileptons at the LHC

Decays of Higgs boson-like particles into multileptons is a well-motivated process for investigating physics beyond the Standard Model (SM). A unique feature of this final state is the precision with

Symmetries and self-supervision in particle physics

A long-standing problem in the design of machine-learning tools for particle physics applications has been how to incorporate prior knowledge of physical symmetries. In this note we propose

References

SHOWING 1-10 OF 61 REFERENCES

Autoencoders for unsupervised anomaly detection in high energy physics

TLDR
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.

Anomaly Detection with Conditional Variational Autoencoders

TLDR
This work exploits the deep conditional variational autoencoder (CVAE) and defines an original loss function together with a metric that targets hierarchically structured data AD and shows the superior performance of this method for classical machine learning (ML) benchmarks and for the application.

Variational autoencoders for new physics mining at the Large Hadron Collider

TLDR
A one-sided threshold test to isolate previously unseen processes as outlier events is developed, which could inspire new-physics model building and new experimental searches, and be complementary to classic LHC searches.

Finding new physics without learning about it: anomaly detection as a tool for searches at colliders

TLDR
The proposed strategy to search for new physics phenomena at colliders independently of the details of such new events, and the potential of semi-supervised anomaly detection techniques to extensively explore the present and future hadron colliders’ data are demonstrated.

The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

TLDR
The outcome of a data challenge to detect signals of new physics at the Large Hadron Collider using unsupervised machine learning algorithms, and a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge.

Adversarially-trained autoencoders for robust unsupervised new physics searches

TLDR
It is proposed to combine the autoencoder with an adversarial neural network to remove its sensitivity to the smearing of the final-state objects and it is shown that one can achieve a robust anomaly detection in resonance-induced resonance.

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

TLDR
An overview of the LHC Olympics 2020 competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders are reviewed.

Topological obstructions to autoencoding

TLDR
Using a series of illustrative low-dimensional examples, it is shown explicitly how the intrinsic and extrinsic topology of the dataset affects the behavior of an autoencoder and how this topology is manifested in the latent space representation during training.

Combining outlier analysis algorithms to identify new physics at the LHC

TLDR
Using super- symmetric benchmark points, it is found that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.

Anomaly Detection for Resonant New Physics with Machine Learning.

TLDR
A new model-agnostic anomaly detection technique that naturally benefits from modern machine learning algorithms is presented that can turn a modest 2σ excess into a 7σ excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search.
...