Anomaly Awareness
arXiv:2007.14462 · doi:10.21468/SciPostPhys.15.2.053
Abstract
We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.
arXiv comment12 pages, 17 figures
References in corpus (37)
- PYTHIA 6.4 Physics and Manual
- PYTHIA 6.2 Physics and Manual
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- An Alternative to Compactification
- Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
- A Brief Introduction to PYTHIA 8.1
- Asymptotic formulae for likelihood-based tests of new physics
- MadGraph 5 : Going Beyond
- Anomaly Detection with Density Estimation
- Classification without labels: Learning from mixed samples in high energy physics
- ALPs Effective Field Theory and Collider Signatures
- Deep-learning Top Taggers or The End of QCD?
- Searching for New Physics with Deep Autoencoders
- Updated Global SMEFT Fit to Higgs, Diboson and Electroweak Data
- Anomaly Detection for Resonant New Physics with Machine Learning
- Learning New Physics from a Machine
- A generic anti-QCD jet tagger
- QCD or What?
- Simulation Assisted Likelihood-free Anomaly Detection
- Novelty Detection Meets Collider Physics
- Adversarially-trained autoencoders for robust unsupervised new physics searches
- Pulling Out All the Tops with Computer Vision and Deep Learning
- Guiding New Physics Searches with Unsupervised Learning
- Variational Autoencoders for New Physics Mining at the Large Hadron Collider
- Scale-invariant resonance tagging in multijet events and new physics in Higgs pair production
- Uncovering latent jet substructure
- Gravity-mediated (or Composite) Dark Matter
- Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
- Non-Resonant Searches for Axion-Like Particles at the LHC
- Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders
- Variational Autoencoders for Anomalous Jet Tagging
- Uncovering Natural Supersymmetry via the interplay between the LHC and Direct Dark Matter Detection
- A robust anomaly finder based on autoencoders
- Electroweak Higgs boson production in the standard model effective field theory beyond leading order in QCD
- Exploring SMEFT in VH with Machine Learning
- Convolutional Neural Networks for Direct Detection of Dark Matter
- Jet tagging made easy