Simulation-based Anomaly Detection for Multileptons at the LHC
arXiv:2203.09601 · doi:10.1007/JHEP01(2023)061
Abstract
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 which the SM is known. As a result, simulations are used directly to estimate the background. Current searches consider specific models and typically focus on those with a single free parameter to simplify the analysis and interpretation. In this paper, we explore recent proposals for signal model agnostic searches using machine learning in the multilepton final state. These tools can be used to simultaneously search for many models, some of which have no dedicated search at the Large Hadron Collider. We find that the machine learning methods offer broad coverage across parameter space beyond where current searches are sensitive, with a necessary loss of performance compared to dedicated searches by only about one order of magnitude.
arXiv comment16 pages, 6 figures
References in corpus (53)
- 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 Introduction to PYTHIA 8.2
- A Brief Introduction to PYTHIA 8.1
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Anomaly Detection with Density Estimation
- Searching for New Physics with Deep Autoencoders
- Anomaly Detection for Resonant New Physics with Machine Learning
- Learning New Physics from a Machine
- Extending the Bump Hunt with Machine Learning
- 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
- LHC Benchmark Scenarios for the Real Higgs Singlet Extension of the Standard Model
- Guiding New Physics Searches with Unsupervised Learning
- Variational Autoencoders for New Physics Mining at the Large Hadron Collider
- The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
- Nuisance hardened data compression for fast likelihood-free inference
- Automatic physical inference with information maximising neural networks
- Uncovering latent jet substructure
- Classifying Anomalies THrough Outer Density Estimation (CATHODE)
- Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
- Autoencoders for unsupervised anomaly detection in high energy physics
- Anomaly detection with Convolutional Graph Neural Networks
- Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders
- Better Latent Spaces for Better Autoencoders
- Variational Autoencoders for Anomalous Jet Tagging
- Simulation-Assisted Decorrelation for Resonant Anomaly Detection
- Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
- Bump Hunting in Latent Space
- Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC
- Uncertainty Aware Learning for High Energy Physics
- Challenges for Unsupervised Anomaly Detection in Particle Physics
- Learning the latent structure of collider events
- Unsupervised clustering for collider physics
- INFERNO: Inference-Aware Neural Optimisation
- Mass Unspecific Supervised Tagging (MUST) for boosted jets
- Deep Set Auto Encoders for Anomaly Detection in Particle Physics
- A Quasi-Model-Independent Search for New High p_T Physics at DZero
- Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders
- Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning
- Transferability of Deep Learning Models in Searches for New Physics at Colliders
- Unsupervised Outlier Detection in Heavy-Ion Collisions
- E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
- Anomalous Jet Identification via Sequence Modeling
- Topological Obstructions to Autoencoding
- QBDT, a new boosting decision tree method with systematic uncertainties into training for High Energy Physics
- Anomaly detection from mass unspecific jet tagging
- Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters
- A Data-Directed Paradigm for BSM searches: the bump-hunting example