Precision-Machine Learning for the Matrix Element Method

Abstract

The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the CP-phase of the top Yukawa coupling in associated Higgs and single-top production.

26 pages, 12 figures, v2: update references, v3: include evaluation on Herwig

References in corpus (96)

• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Anomaly Detection with Density Estimation
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• A Precision Measurement of the Mass of the Top Quark
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• A framework for Higgs characterisation
• Event Generation with Normalizing Flows
• Progressive Distillation for Fast Sampling of Diffusion Models
• How to GAN LHC Events
• OmniFold: A Method to Simultaneously Unfold All Observables
• Constraining anomalous Higgs boson couplings to the heavy flavor fermions using matrix element techniques
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• A Guide to Constraining Effective Field Theories with Machine Learning
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
• Machine Learning and LHC Event Generation
• AtlFast3: the next generation of fast simulation in ATLAS
• Automation of the matrix element reweighting method
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• Flow Matching for Generative Modeling
• i-flow: High-dimensional Integration and Sampling with Normalizing Flows
• Score-based Generative Models for Calorimeter Shower Simulation
• Boosting the Direct CP Measurement of the Higgs-Top Coupling
• Search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks using a matrix element method
• Exploring phase space with Neural Importance Sampling
• ABCNet: An attention-based method for particle tagging
• Invertible Networks or Partons to Detector and Back Again
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
• How to GAN away Detector Effects
• Unfolding with Generative Adversarial Networks
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• Understanding Event-Generation Networks via Uncertainties
• Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
• Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
• Evidence for single top-quark production in the $s$-channel in proton-proton collisions at $\sqrt{s}=$8 TeV with the ATLAS detector using the Matrix Element Method
• Generative Networks for Precision Enthusiasts
• GANplifying Event Samples
• Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• The Matrix Element Method and QCD Radiation
• The Matrix Element Method and its Application to Measurements of the Top Quark Mass
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Unravelling $t\bar{t}h$ via the matrix element method
• Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
• Targeting Multi-Loop Integrals with Neural Networks
• Deep-Learning Jets with Uncertainties and More
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• Resonant anomaly detection without background sculpting
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• MadNIS -- Neural Multi-Channel Importance Sampling
• L2LFlows: Generating High-Fidelity 3D Calorimeter Images
• Symmetries, Safety, and Self-Supervision
• FETA: Flow-Enhanced Transportation for Anomaly Detection
• Per-Object Systematics using Deep-Learned Calibration
• Improved Neural Network Monte Carlo Simulation
• Reweighting a parton shower using a neural network: the final-state case
• Polishing a shiny Higgs with matrix elements
• Optimized probes of $CP$-odd effects in the $t\bar{t}h$ process at hadron colliders
• Machine learning the Higgs boson-top quark CP phase
• How to Understand Limitations of Generative Networks
• Top Quark Mass Measurement from Dilepton Events at CDF II with the Matrix-Element Method
• Two Invertible Networks for the Matrix Element Method
• Measuring QCD Splittings with Invertible Networks
• Indirect $\mathcal{C}\mathcal{P}$ probes of the Higgs-top-quark interaction: current LHC constraints and future opportunities
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
• Measurement of spin correlations in t t-bar production using the matrix element method in the muon + jets final state in pp collisions at sqrt(s) = 8 TeV
• Single top-quark production at the Tevatron and the LHC
• Towards a Computer Vision Particle Flow
• Maximum Significance at the LHC and Higgs Decays to Muons
• Modern Machine Learning for LHC Physicists
• Probing the CP structure of the top quark Yukawa coupling: Loop sensitivity vs. on-shell sensitivity
• Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference
• Direct Higgs-top CP-phase measurement with $t\bar{t}h$ at the 14 TeV LHC and 100 TeV FCC
• Measuring the Higgs-bottom coupling in weak boson fusion
• ν-Flows: Conditional Neutrino Regression
• Deep generative models for fast photon shower simulation in ATLAS
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• Learning the language of QCD jets with transformers
• Returning CP-Observables to The Frames They Belong
• Consistency Models
• Jet Diffusion versus JetGPT -- Modern Networks for the LHC
• Improving Generative Model-based Unfolding with Schrödinger Bridges
• CaloFlow for CaloChallenge Dataset 1
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• How to GAN Event Subtraction
• End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
• Building Normalizing Flows with Stochastic Interpolants
• ELSA -- Enhanced latent spaces for improved collider simulations
• Matrix Element Regression with Deep Neural Networks -- breaking the CPU barrier
• Refining Fast Calorimeter Simulations with a Schrödinger Bridge
• EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
• MoMEMta, a modular toolkit for the Matrix Element Method at the LHC
• Reconstructing the invisible with matrix elements

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• Differentiable MadNIS-Lite
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Advancing Tools for Simulation-Based Inference
• Generative Unfolding with Distribution Mapping
• Extrapolating Jet Radiation with Autoregressive Transformers
• BitHEP -- The Limits of Low-Precision ML in HEP
• How to Unfold Top Decays
• A Novel Implementation of the Matrix Element Method at Next-to-Leading Order for the Measurement of the Higgs Self-Coupling ${\lambda }_{3H}$