Simulation Assisted Likelihood-free Anomaly Detection

Abstract

Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program. A variety of model-independent searches have been proposed, adding sensitivity to unexpected signals. There are generally two types of such searches: those that rely heavily on simulations and those that are entirely based on (unlabeled) data. This paper introduces a hybrid method that makes the best of both approaches. For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands. This function is then interpolated into the signal region and then the reweighted background-only simulation can be used for supervised learning as well as for background estimation. The background estimation from the reweighted simulation allows for non-trivial correlations between features used for classification and the resonant feature. A dijet search with jet substructure is used to illustrate the new method. Future applications of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) include a variety of final states and potential combinations with other model-independent approaches.

arXiv comment19 pages, 9 figures

References in corpus (57)

• Adam: A Method for Stochastic Optimization
• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• TensorFlow: A system for large-scale machine learning
• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• FastJet user manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Herwig++ Physics and Manual
• Dispelling the N^3 myth for the Kt jet-finder
• Identifying Boosted Objects with N-subjettiness
• Deep Learning and its Application to LHC Physics
• Maximizing Boosted Top Identification by Minimizing N-subjettiness
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Anomaly Detection with Density Estimation
• Searching for New Physics with Deep Autoencoders
• Anomaly Detection for Resonant New Physics with Machine Learning
• Energy Flow Networks: Deep Sets for Particle Jets
• Parameterized Machine Learning for High-Energy Physics
• Learning New Physics from a Machine
• Extending the Bump Hunt with Machine Learning
• OmniFold: A Method to Simultaneously Unfold All Observables
• A generic anti-QCD jet tagger
• Mining gold from implicit models to improve likelihood-free inference
• QCD or What?
• Constraining Effective Field Theories with Machine Learning
• Novelty Detection Meets Collider Physics
• Adversarially-trained autoencoders for robust unsupervised new physics searches
• A Guide to Constraining Effective Field Theories with Machine Learning
• Guiding New Physics Searches with Unsupervised Learning
• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• Uncovering latent jet substructure
• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
• Global Search for New Physics with 2.0/fb at CDF
• Machine Learning Uncertainties with Adversarial Neural Networks
• A robust anomaly finder based on autoencoders
• Model-Independent and Quasi-Model-Independent Search for New Physics at CDF
• Search for New Physics in e mu X Data at D0 Using Sleuth: A Quasi-Model-Independent Search Strategy for New Physics
• A general search for new phenomena in ep scattering at HERA
• A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
• A Quasi-Model-Independent Search for New Physics at Large Transverse Momentum
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Supervised deep learning in high energy phenomenology: a mini review
• A General Search for New Phenomena at HERA
• uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers
• A Quasi-Model-Independent Search for New High p_T Physics at DZero
• Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
• Mass Agnostic Jet Taggers
• QBDT, a new boosting decision tree method with systematic uncertainties into training for High Energy Physics
• Reducing the dependence of the neural network function to systematic uncertainties in the input space
• Nonparametric semisupervised classification for signal detection in high energy physics
• Learning Multivariate New Physics
• Model-Independent Global Search for New High-pT Physics at CDF
• AI Safety for High Energy Physics

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• Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
• Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders
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• Bump Hunting in Latent Space
• ABCDisCo: Automating the ABCD Method with Machine Learning
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Casting a graph net to catch dark showers
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Resonant anomaly detection without background sculpting
• CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
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• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• FETA: Flow-Enhanced Transportation for Anomaly Detection
• DCTRGAN: Improving the Precision of Generative Models with Reweighting
• Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
• Jet Substructure from Dark Sector Showers
• Learning new physics efficiently with nonparametric methods
• Self-supervised Anomaly Detection for New Physics
• The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
• Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
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• Probing triple Higgs coupling with machine learning at the LHC
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• Learning Likelihood Ratios with Neural Network Classifiers
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
• Boosting mono-jet searches with model-agnostic machine learning
• ELSA -- Enhanced latent spaces for improved collider simulations
• A Method to Simultaneously Facilitate All Jet Physics Tasks
• A semi-supervised approach to dark matter searches in direct detection data with machine learning
• Leveraging universality of jet taggers through transfer learning
• Anomalies, Representations, and Self-Supervision
• Data-Directed Search for New Physics based on Symmetries of the SM
• GPU coprocessors as a service for deep learning inference in high energy physics
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• Parameter Estimation using Neural Networks in the Presence of Detector Effects
• Combining Resonant and Tail-based Anomaly Detection
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• Learning Multivariate New Physics
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Reframing Jet Physics with New Computational Methods
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• Simulation-based Anomaly Detection for Multileptons at the LHC
• Unsupervised and lightly supervised learning in particle physics
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• Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Exotic diboson $Z^{\prime }$ decays in the U$\mu \nu$SSM
• Preserving New Physics while Simultaneously Unfolding All Observables
• Finding excesses in model parameter space
• Detecting New Physics as Novelty -- Complementarity Matters
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• Inferring correlated distributions: boosted top jets
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
• Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC
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• Weakly supervised anomaly detection with event-level variables