{"type":"ScholarlyArticle","title":"Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)","authors":[{"name":"Yasir Alanazi","slug":"yasir-alanazi","given":"Yasir","family":"Alanazi","affiliation":""},{"name":"Nobuo Sato","slug":"nobuo-sato","given":"Nobuo","family":"Sato","affiliation":""},{"name":"Tianbo Liu","slug":"tianbo-liu","given":"Tianbo","family":"Liu","affiliation":""},{"name":"Wally Melnitchouk","slug":"wally-melnitchouk","given":"Wally","family":"Melnitchouk","affiliation":""},{"name":"Pawel Ambrozewicz","slug":"pawel-ambrozewicz","given":"Pawel","family":"Ambrozewicz","affiliation":""},{"name":"Florian Hauenstein","slug":"florian-hauenstein","given":"Florian","family":"Hauenstein","affiliation":""},{"name":"Michelle P. Kuchera","slug":"michelle-p-kuchera","given":"Michelle P.","family":"Kuchera","affiliation":""},{"name":"Evan Pritchard","slug":"evan-pritchard","given":"Evan","family":"Pritchard","affiliation":""},{"name":"Michael Robertson","slug":"michael-robertson","given":"Michael","family":"Robertson","affiliation":""},{"name":"Ryan Strauss","slug":"ryan-strauss","given":"Ryan","family":"Strauss","affiliation":""},{"name":"Luisa Velasco","slug":"luisa-velasco","given":"Luisa","family":"Velasco","affiliation":""},{"name":"Yaohang Li","slug":"yaohang-li","given":"Yaohang","family":"Li","affiliation":""}],"abstract":"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 by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of existing and future accelerator facilities, such as the Electron-Ion Collider.","doi":"10.24963/ijcai.2021/293","url":"https://ijcai.org/proceedings/2021/293","pdf":"https://ijcai.org/proceedings/2021/0293.pdf","markdown":"https://ijcai.org/proceedings/2021/293.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/293","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":293,"pages":{"first":2126,"last":2132},"keywords":["Multidisciplinary Topics and Applications: Natural Sciences","Machine Learning: Learning Generative Models","Machine Learning: Deep Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}