Parameter Estimation using Neural Networks in the Presence of Detector Effects

Abstract

Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators. Parametrized neural network reweighting can be used to extend this fitting procedure to many dimensions and does not require binning. If the fit is to be performed using reconstructed data, then expensive detector simulations must be used for training the neural networks. We introduce a new two-level fitting approach that only requires one dataset with detector simulation and then a set of additional generation-level datasets without detector effects included. This Simulation-level fit based on Reweighting Generator-level events with Neural networks (SRGN) is demonstrated using simulated datasets for a variety of examples including a simple Gaussian random variable, parton shower tuning, and the top quark mass extraction.

arXiv comment16 pages, 13 figures, 4 tables; v3: Updated to journal version

References in corpus (48)

• 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
• An Introduction to PYTHIA 8.2
• FastJet user manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Dispelling the N^3 myth for the Kt jet-finder
• Tuning PYTHIA 8.1: the Monash 2013 Tune
• Identifying Boosted Objects with N-subjettiness
• Energy Correlation Functions for Jet Substructure
• Maximizing Boosted Top Identification by Minimizing N-subjettiness
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Energy Flow Networks: Deep Sets for Particle Jets
• How to GAN LHC Events
• OmniFold: A Method to Simultaneously Unfold All Observables
• Mining gold from implicit models to improve likelihood-free inference
• Constraining Effective Field Theories with Machine Learning
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Simulation Assisted Likelihood-free Anomaly Detection
• A Guide to Constraining Effective Field Theories with Machine Learning
• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• Regressive and generative neural networks for scalar field theory
• Fast and accurate simulation of particle detectors using generative adversarial networks
• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• 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
• What is the Top Quark Mass?
• Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• Lund jet images from generative and cycle-consistent adversarial networks
• Generative Models for Fast Calorimeter Simulation.LHCb case
• GANs for generating EFT models
• Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks
• LHC analysis-specific datasets with Generative Adversarial Networks
• Cherenkov Detectors Fast Simulation Using Neural Networks
• Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
• How to GAN Event Subtraction
• Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
• Resonance Searches with Machine Learned Likelihood Ratios
• Efficiency Parameterization with Neural Networks
• Parametric unfolding. Method and restrictions

Cited by in corpus (11)

• Event Generators for High-Energy Physics Experiments
• Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics
• Neural network reconstruction of the dense matter equation of state from neutron star observables
• E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
• Reconstructing spectral functions via automatic differentiation
• Learning Likelihood Ratios with Neural Network Classifiers
• Parameter Inference from Event Ensembles and the Top-Quark Mass
• Neural Conditional Reweighting
• BitHEP -- The Limits of Low-Precision ML in HEP
• Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators