{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T20:02:14Z","timestamp":1766088134434,"version":"3.37.3"},"reference-count":55,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T00:00:00Z","timestamp":1698710400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T00:00:00Z","timestamp":1698710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006208","name":"High Energy Physics","doi-asserted-by":"crossref","award":["DE-SC002118"],"award-info":[{"award-number":["DE-SC002118"]}],"id":[{"id":"10.13039\/100006208","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"crossref","award":["2018\/25225-9"],"award-info":[{"award-number":["2018\/25225-9"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"crossref","award":["77236"],"award-info":[{"award-number":["77236"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation","award":["OAC-1836650"],"award-info":[{"award-number":["OAC-1836650"]}]},{"DOI":"10.13039\/100006230","name":"Fermilab","doi-asserted-by":"crossref","award":["DE-AC02-07CH1135"],"award-info":[{"award-number":["DE-AC02-07CH1135"]}],"id":[{"id":"10.13039\/100006230","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2023,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the Large Hadron Collider (LHC), there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulfill that task. Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. Given the ConVAE\u2019s limitations, a normalizing flow (NF) network is coupled to it in a two-step training process, which shows improvements on the results for the generated jets. The ConVAE+NF network is capable of generating a jet in <jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mn>18.30<\/mml:mn>\n                        <mml:mo>\u00b1<\/mml:mo>\n                        <mml:mn>0.04<\/mml:mn>\n                        <mml:mrow>\n                           <mml:mi>\u03bc<\/mml:mi>\n                           <mml:mtext>s<\/mml:mtext>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                     <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"mlstad04eaieqn1.gif\" xlink:type=\"simple\"\/>\n                  <\/jats:inline-formula>, making it one of the fastest methods for this task up to now.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad04ea","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T22:22:53Z","timestamp":1697754173000},"page":"045023","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows"],"prefix":"10.1088","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4232-4743","authenticated-orcid":true,"given":"Breno","family":"Orzari","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2264-2229","authenticated-orcid":true,"given":"Nadezda","family":"Chernyavskaya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0852-2183","authenticated-orcid":false,"given":"Raphael","family":"Cobe","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5076-7096","authenticated-orcid":true,"given":"Javier","family":"Duarte","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5421-0789","authenticated-orcid":false,"given":"Jefferson","family":"Fialho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6339-1879","authenticated-orcid":false,"given":"Dimitrios","family":"Gunopulos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2445-1060","authenticated-orcid":false,"given":"Raghav","family":"Kansal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1939-4268","authenticated-orcid":true,"given":"Maurizio","family":"Pierini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1809-5226","authenticated-orcid":false,"given":"Thiago","family":"Tomei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3682-3258","authenticated-orcid":false,"given":"Mary","family":"Touranakou","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2023,10,31]]},"reference":[{"year":"2004","author":"Bruning","key":"mlstad04eabib1","doi-asserted-by":"publisher","DOI":"10.5170\/CERN-2004-003-V-1"},{"key":"mlstad04eabib2","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1016\/j.ppnp.2012.03.001","article-title":"The large hadron collider","volume":"67","author":"Bruning","year":"2012","journal-title":"Prog. Part. Nucl. Phys."},{"year":"2020","author":"B\u00e9jar Alonso","key":"mlstad04eabib3","doi-asserted-by":"publisher","DOI":"10.23731\/CYRM-2020-0010"},{"year":"2022","author":"","key":"mlstad04eabib4"},{"key":"mlstad04eabib5","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1088\/978-1-6817-4073-7","author":"Banfi","year":"2016"},{"key":"mlstad04eabib6","doi-asserted-by":"publisher","DOI":"10.1088\/1748-0221\/12\/10\/P10003","article-title":"Particle-flow reconstruction and global event description with the CMS detector","volume":"12","author":"","year":"2017","journal-title":"J. Inst."},{"key":"mlstad04eabib7","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1140\/epjc\/s10052-010-1314-6","article-title":"Towards jetography","volume":"67","author":"Salam","year":"2010","journal-title":"Eur. Phys. J. C"},{"key":"mlstad04eabib8","article-title":"Particle cloud generation with message passing generative adversarial networks","volume":"vol 34","author":"Kansal","year":"2021","edition":"ed"},{"key":"mlstad04eabib9","doi-asserted-by":"publisher","first-page":"130","DOI":"10.21468\/SciPostPhys.15.4.130","article-title":"EPiC-GAN: equivariant point cloud generation for particle jets","volume":"15","author":"Buhmann","year":"2023","journal-title":"SciPost Phys."},{"article-title":"Point cloud generation using transformer encoders and normalising flows","year":"2022","author":"K\u00e4ch","key":"mlstad04eabib10"},{"article-title":"PC-JeDi: diffusion for particle cloud generation in high energy physics","year":"2023","author":"Leigh","key":"mlstad04eabib11"},{"key":"mlstad04eabib12","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevD.108.036025","article-title":"Fast point cloud generation with diffusion models in high energy physics","author":"Mikuni","year":"2023"},{"article-title":"Sparse data generation for particle-based simulation of hadronic jets in the LHC","year":"2021","author":"Orzari","key":"mlstad04eabib13"},{"key":"mlstad04eabib14","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s41781-017-0004-6","article-title":"Learning particle physics by example: location-aware generative adversarial networks for physics synthesis","volume":"1","author":"de Oliveira","year":"2017","journal-title":"Comput. Softw. Big Sci."},{"article-title":"Variational autoencoders for jet simulation","year":"2020","author":"Dohi","key":"mlstad04eabib15"},{"key":"mlstad04eabib16","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.97.014021","article-title":"CaloGAN : simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks","volume":"97","author":"Paganini","year":"2018","journal-title":"Phys. Rev. D"},{"key":"mlstad04eabib17","doi-asserted-by":"publisher","DOI":"10.1051\/epjconf\/201921402010","article-title":"3D convolutional GAN for fast simulation","volume":"214","author":"Sofia","year":"2019"},{"key":"mlstad04eabib18","doi-asserted-by":"publisher","DOI":"10.1051\/epjconf\/202125103003","article-title":"Decoding photons: Physics in the latent space of a BIB-AE generative network","volume":"251","author":"Buhmann","year":"2021"},{"article-title":"Deep generative models for fast photon shower simulation in ATLAS","year":"2022","author":"","key":"mlstad04eabib19"},{"key":"mlstad04eabib20","doi-asserted-by":"publisher","first-page":"JHEP08(2019)110","DOI":"10.1007\/JHEP08(2019)110","article-title":"DijetGAN: a generative-adversarial network approach for the simulation of QCD dijet events at the LHC","author":"Di Sipio","year":"2019","journal-title":"J. High Energy Phys."},{"key":"mlstad04eabib21","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac7c56","article-title":"Particle-based fast jet simulation at the LHC with variational autoencoders","volume":"3","author":"Touranakou","year":"2022","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"mlstad04eabib22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15709-8","volume":"vol 958","author":"Marzani","year":"2019"},{"key":"mlstad04eabib23","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.91.045003","article-title":"Jet substructure at the Large Hadron Collider","volume":"91","author":"Kogler","year":"2019","journal-title":"Rev. Mod. Phys."},{"article-title":"Variational autoencoders with normalizing flow decoders","year":"2020","author":"Morrow","key":"mlstad04eabib24"},{"key":"mlstad04eabib25","doi-asserted-by":"publisher","first-page":"610","DOI":"10.3390\/math10040610","article-title":"PFVAE: a planar flow-based variational auto-encoder prediction model for time series data","volume":"10","author":"Jin","year":"2022","journal-title":"Mathematics"},{"key":"mlstad04eabib26","first-page":"p 11","article-title":"CaloMan: fast generation of calorimeter showers with density estimation on learned manifolds","author":"Cresswell","year":"2022"},{"key":"mlstad04eabib27","doi-asserted-by":"crossref","DOI":"10.1088\/2632-2153\/acefa9","article-title":"New angles on fast calorimeter shower simulation","author":"Diefenbacher","year":"2023"},{"key":"mlstad04eabib28","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.3601436","article-title":"HLS4ML LHC Jet dataset (30 particles)","author":"Pierini","year":"2020"},{"key":"mlstad04eabib29","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1140\/epjc\/s10052-020-7608-4","article-title":"JEDI-net: a jet identification algorithm based on interaction networks","volume":"80","author":"Moreno","year":"2020","journal-title":"Eur. Phys. J. C"},{"key":"mlstad04eabib30","doi-asserted-by":"publisher","first-page":"1896","DOI":"10.1140\/epjc\/s10052-012-1896-2","article-title":"Fastjet user manual","volume":"72","author":"Cacciari","year":"2012","journal-title":"Eur. Phys. J. C"},{"key":"mlstad04eabib31","doi-asserted-by":"publisher","first-page":"JHEP04(2008)063","DOI":"10.1088\/1126-6708\/2008\/04\/063","article-title":"The anti- kT jet clustering algorithm","author":"Cacciari","year":"2008","journal-title":"J. High Energy Phys."},{"article-title":"Auto-encoding variational bayes","year":"2022","author":"Kingma","key":"mlstad04eabib32"},{"article-title":"Tutorial on variational autoencoders","year":"2021","author":"Doersch","key":"mlstad04eabib33"},{"key":"mlstad04eabib34","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1561\/2200000056","article-title":"An introduction to variational autoencoders","volume":"12","author":"Kingma","year":"2019","journal-title":"Found. Trends Mach. Learn."},{"key":"mlstad04eabib35","first-page":"p 8024","article-title":"Pytorch: an imperative style, high-performance deep learning library","volume":"vol 32","author":"Paszke","year":"2019"},{"key":"mlstad04eabib36","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/3-540-59497-3_175","article-title":"The influence of the sigmoid function parameters on the speed of backpropagation learning","author":"Han","year":"1995","edition":"ed"},{"article-title":"Adam: a method for stochastic optimization","year":"2015","author":"Kingma","key":"mlstad04eabib37"},{"key":"mlstad04eabib38","doi-asserted-by":"publisher","first-page":"3964","DOI":"10.1109\/TPAMI.2020.2992934","article-title":"Normalizing flows: an introduction and review of current methods","volume":"43","author":"Kobyzev","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"mlstad04eabib39","first-page":"1","article-title":"Normalizing flows for probabilistic modeling and inference","volume":"22","author":"Papamakarios","year":"2021","journal-title":"J. Mach. Learn. Res."},{"article-title":"Density estimation using Real NVP","year":"2017","author":"Dinh","key":"mlstad04eabib40"},{"author":"Pomponi","key":"mlstad04eabib41","article-title":"Using a normalizing flow to generate image embeddings"},{"author":"Dinh","key":"mlstad04eabib42","article-title":"NICE: non-linear independent components estimation"},{"key":"mlstad04eabib43","first-page":"pp 315","article-title":"Deep sparse rectifier neural networks","volume":"vol 15","author":"Glorot","year":"2011","edition":"ed"},{"key":"mlstad04eabib44","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On information and sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Stat."},{"article-title":"\u03b2-VAE: learning basic visual concepts with a constrained variational framework","year":"2017","author":"Higgins","key":"mlstad04eabib45"},{"key":"mlstad04eabib46","doi-asserted-by":"publisher","first-page":"p 2463","DOI":"10.1109\/CVPR.2017.264","article-title":"A point set generation network for 3D object reconstruction from a single image","author":"Fan","year":"2017"},{"key":"mlstad04eabib47","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1023\/A:1026543900054","article-title":"The earth mover\u2019s distance as a metric for image retrieval","volume":"40","author":"Rubner","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"mlstad04eabib48","doi-asserted-by":"publisher","first-page":"JHEP03(2020)145","DOI":"10.1007\/JHEP03(2020)145","article-title":"Search for new resonances in mass distributions of jet pairs using 139 fb\u22121 of pp collisions at s=13 \u2009TeV with the ATLAS detector","author":"","year":"2020","journal-title":"J. High Energy Phys."},{"key":"mlstad04eabib49","doi-asserted-by":"publisher","first-page":"JHEP05(2020)033","DOI":"10.1007\/JHEP05(2020)033","article-title":"Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at s=13 \u2009TeV","author":"","year":"2020","journal-title":"J. High Energy Phys."},{"key":"mlstad04eabib50","doi-asserted-by":"crossref","DOI":"10.1145\/3292500.3330701","article-title":"Optuna: a next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"key":"mlstad04eabib51","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.107.076017","article-title":"Evaluating generative models in high energy physics","volume":"107","author":"Kansal","year":"2023","journal-title":"Phys. Rev. D"},{"key":"mlstad04eabib52","doi-asserted-by":"publisher","first-page":"JHEP04(2018)013","DOI":"10.1007\/JHEP04(2018)013","article-title":"Energy flow polynomials: a complete linear basis for jet substructure","author":"Komiske","year":"2018","journal-title":"J. High Energy Phys."},{"key":"mlstad04eabib53","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.5597893","article-title":"Jet-net\/jetnet: v0.0.3","author":"Kansal","year":"2021"},{"key":"mlstad04eabib54","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.101.056019","article-title":"Jet tagging via particle clouds","volume":"101","author":"Qu","year":"2020","journal-title":"Phys. Rev. D"},{"key":"mlstad04eabib55","first-page":"p 6626","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume":"vol 30","author":"Heusel","year":"2017","edition":"ed"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T10:19:02Z","timestamp":1698747542000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad04ea"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,31]]},"references-count":55,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,10,31]]},"published-print":{"date-parts":[[2023,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad04ea","relation":{},"ISSN":["2632-2153"],"issn-type":[{"type":"electronic","value":"2632-2153"}],"subject":[],"published":{"date-parts":[[2023,10,31]]},"assertion":[{"value":"LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2023 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2023-06-27","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-10-19","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-10-31","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}