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Learning to simulate high energy particle collisions from unlabeled data
In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9085893/ https://www.ncbi.nlm.nih.gov/pubmed/35534506 http://dx.doi.org/10.1038/s41598-022-10966-7 |
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author | Howard, Jessica N. Mandt, Stephan Whiteson, Daniel Yang, Yibo |
author_facet | Howard, Jessica N. Mandt, Stephan Whiteson, Daniel Yang, Yibo |
author_sort | Howard, Jessica N. |
collection | PubMed |
description | In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. Identifying the probabilistic autoencoder’s latent space with the space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle physics examples, Z-boson and top-quark decays, but stress that OTUS can be widely applied to other fields. |
format | Online Article Text |
id | pubmed-9085893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90858932022-05-11 Learning to simulate high energy particle collisions from unlabeled data Howard, Jessica N. Mandt, Stephan Whiteson, Daniel Yang, Yibo Sci Rep Article In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. Identifying the probabilistic autoencoder’s latent space with the space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle physics examples, Z-boson and top-quark decays, but stress that OTUS can be widely applied to other fields. Nature Publishing Group UK 2022-05-09 /pmc/articles/PMC9085893/ /pubmed/35534506 http://dx.doi.org/10.1038/s41598-022-10966-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Howard, Jessica N. Mandt, Stephan Whiteson, Daniel Yang, Yibo Learning to simulate high energy particle collisions from unlabeled data |
title | Learning to simulate high energy particle collisions from unlabeled data |
title_full | Learning to simulate high energy particle collisions from unlabeled data |
title_fullStr | Learning to simulate high energy particle collisions from unlabeled data |
title_full_unstemmed | Learning to simulate high energy particle collisions from unlabeled data |
title_short | Learning to simulate high energy particle collisions from unlabeled data |
title_sort | learning to simulate high energy particle collisions from unlabeled data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9085893/ https://www.ncbi.nlm.nih.gov/pubmed/35534506 http://dx.doi.org/10.1038/s41598-022-10966-7 |
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