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

@inproceedings{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}, booktitle={IJCAI}, year={2021} }

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…

## 27 Citations

Report from the A.I. For Nuclear Physics Workshop.

- Physics
- 2020

This report is an outcome of the workshop "AI for Nuclear Physics" held at Thomas Jefferson National Accelerator Facility on March 4-6, 2020. The workshop brought together 184 scientists to explore…

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

- PhysicsSciPost Physics
- 2022

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…

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

- Physics, Computer SciencePhysics Letters B
- 2022

Exploring phase space with Nested Sampling

- Computer Science
- 2022

The adaptation of the algorithm, designed to perform Bayesian inference computations, to the integration of partonic scattering cross sections and the generation of individual events distributed according to the corresponding squared matrix element is described.

Machine Learning and LHC Event Generation

- Physics
- 2022

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and…

Modeling hadronization using machine learning

- Physics, Computer Science
- 2022

We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques. We successfully implement, validate, and train a conditional…

Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research

- Computer Science, Physics
- 2022

Challenges and opportunities are described for the focused development of algorithms, applications, software, hardware, and infrastructure to support both practical and theoretical applications of quantum computing to HEP problems within the next 10 years.

Targeting multi-loop integrals with neural networks

- PhysicsSciPost Physics
- 2022

Numerical evaluations of Feynman integrals
often proceed via a deformation of the integration contour into the complex plane. While valid contours are easy to construct, the numerical precision for a…

Uncertainties associated with GAN-generated datasets in high energy physics

- Computer ScienceSciPost Physics
- 2022

It is pointed out that data generated by a GAN cannot statistically be better than the data it was trained on, and the applicability of GANs in various situations is critically examined, including a) for replacing the entire Monte Carlo pipeline or parts of it, and b) to produce datasets for usage in highly sensitive analyses or sub-optimal ones.

Unsupervised Quantum Circuit Learning in High Energy Physics

- Computer Science
- 2022

This work uses non-adversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over 2 and 3 variables to generate synthetic data of high energy physics processes.

## References

SHOWING 1-10 OF 51 REFERENCES

A Kernel Method for the Two-Sample-Problem

- Mathematics, Computer ScienceNIPS
- 2006

This work proposes two statistical tests to determine if two samples are from different distributions, and applies this approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where the test performs strongly.

7.

- MedicineThe journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
- 2020

7。，7q，，，hypo。，，。，7q32.3-qter27.7 Mb。of，（diaphragm）。，7q。.

BA

- EducationSpringer Reference Medizin
- 2019

KEY: # = new course * = course changed † = course dropped University of Kentucky 2018-2019 Undergraduate Bulletin 1 BA 700 TEACHING METHODS IN BUSINESS. (1) A three part course that examines what…

How to GAN LHC events

- PhysicsSciPost 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…

Adam: A Method for Stochastic Optimization

- Computer ScienceICLR
- 2015

This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

Factorization of Hard Processes in QCD

- Mathematics
- 1989

We summarize the standard factorization theorems for hard processes in QCD, and describe their proofs.

Event generation and statistical sampling for physics with deep generative models and a density information buffer

- PhysicsNature communications
- 2021

Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories.