# Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)

@article{Alanazi2021SimulationOE, title={Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)}, author={Yasir Alanazi and Nobuo Sato and Tianbo Liu and W. Melnitchouk and Michelle P. Kuchera and Evan Pritchard and Michael Robertson and Ryan R. Strauss and Luisa Velasco and Yaohang Li}, journal={ArXiv}, year={2021}, volume={abs/2001.11103} }

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily… Expand

#### 19 Citations

cFAT-GAN: Conditional Simulation of Electron-Proton Scattering Events with Variate Beam Energies by a Feature Augmented and Transformed Generative Adversarial Network

- Computer Science
- 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
- 2020

This work extends the FAT-GAN framework by conditioning the component neural networks according to the given reaction energy, and demonstrates that this model, referred to as cFAT-GAN, can reliably produce inclusive event feature distributions and correlations for a continuous range of reaction energies. Expand

Explainable machine learning of the underlying physics of high-energy particle collisions

- Physics
- 2020

We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in… Expand

A survey of machine learning-based physics event generation

- Computer Science, Physics
- IJCAI
- 2021

The state-of-the-art of machine learning efforts at building physics event generators and some open questions related to super-resolution, fidelity, and extrapolation for physics event generation based on ML technology are surveyed. Expand

Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

- Physics
- 2021

The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators. Even when using sophisticated phase-space sampling techniques… Expand

Optimising simulations for diphoton production at hadron colliders using amplitude neural networks

- Computer Science, Physics
- ArXiv
- 2021

This work focuses on the case of loop-induced diphoton production through gluon fusion, and develops a realistic simulation method that can be applied to hadron collider observables. Expand

Generative Networks for Precision Enthusiasts

- Computer Science, Physics
- ArXiv
- 2021

It is shown how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Expand

A factorisation-aware Matrix element emulator

- Physics
- Journal of High Energy Physics
- 2021

Abstract
In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of… Expand

Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

- Computer Science
- ArXiv
- 2021

This work introduces a dual-head autoencoder to resolve sparsity and regression simultaneously, called Bicephalous Convolutional AutoEncoder (BCAE), which shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP. Expand

Generative Networks for LHC events

- Physics
- 2020

LHC physics crucially relies on our ability to simulate events efficiently from first principles. Modern machine learning, specifically generative networks, will help us tackle simulation challenges… Expand

Amplifying Statistics using Generative Models

- 2020

A critical question concerning generative networks applied to physics simulations is if the generated events add statistical precision beyond the training sample. We show for a simple example how… Expand

#### References

SHOWING 1-10 OF 51 REFERENCES

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

- Physics, Mathematics
- 2017

We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in high energy particle physics by applying a novel Generative Adversarial Network… Expand

LHC analysis-specific datasets with Generative Adversarial Networks

- Physics, Computer Science
- ArXiv
- 2019

It is shown how substantial performance improvements and convergence speed-up can be obtained by including regression terms in the loss function of the generator, and an objective criterion is developed to assess the geenrator performance in a quantitative way. Expand

DijetGAN: a Generative-Adversarial Network approach for the simulation of QCD dijet events at the LHC

- Physics
- Journal of High Energy Physics
- 2019

Abstract
A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using… Expand

Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters.

- Computer Science, Medicine
- Physical review letters
- 2018

A deep neural network-based generative model is introduced to enable high-fidelity, fast, electromagnetic calorimeter simulation and opens the door to a new era of fast simulation that could save significant computing time and disk space, while extending the reach of physics searches and precision measurements at the LHC and beyond. Expand

Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network

- Physics
- Computing and Software for Big Science
- 2019

Simulations of particle showers in calorimeters are computationally time-consuming, as they have to reproduce both energy depositions and their considerable fluctuations. A new approach to ultra-fast… Expand

Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks

- Physics, Computer Science
- Computing and Software for Big Science
- 2018

It is shown that deep neural networks can achieve high fidelity in this task, while attaining a speed increase of several orders of magnitude with respect to traditional algorithms. Expand

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

- Physics, Computer Science
- ArXiv
- 2017

CaloGAN, a new fast simulation technique based on generative adversarial networks (GANs) is introduced, which is applied to the modeling of electromagnetic showers in a longitudinally segmented calorimeter and achieves speedup factors comparable to or better than existing full simulation techniques. Expand

A deep learning-based reconstruction of cosmic ray-induced air showers

- Physics
- 2018

Abstract We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed… Expand

How to GAN LHC events

- Physics
- 2019

Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting. For top pair production we show how… Expand

Generative Adversarial Nets

- Computer Science
- NIPS
- 2014

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a… Expand