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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2023,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    A configurable calorimeter simulation for AI (CoCoA) applications is presented, based on the\n                    <jats:sc>Geant4<\/jats:sc>\n                    toolkit and interfaced with the\n                    <jats:sc>Pythia<\/jats:sc>\n                    event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic particle shower descriptions, such as reconstruction, fast simulation, and low-level analysis. Specifications such as the granularity and material of its nearly hermetic geometry are user-configurable. The tool is supplemented with simple event processing including topological clustering, jet algorithms, and a nearest-neighbors graph construction. Formatting is also provided to visualise events using the Phoenix event display software.\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/acf186","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T18:40:27Z","timestamp":1692297627000},"page":"035042","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Configurable calorimeter simulation for AI applications"],"prefix":"10.1088","volume":"4","author":[{"given":"Anton","family":"Charkin-Gorbulin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5769-7094","authenticated-orcid":true,"given":"Kyle","family":"Cranmer","sequence":"additional","affiliation":[]},{"given":"Francesco Armando","family":"Di Bello","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8955-9510","authenticated-orcid":true,"given":"Etienne","family":"Dreyer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1285-9261","authenticated-orcid":true,"given":"Sanmay","family":"Ganguly","sequence":"additional","affiliation":[]},{"given":"Eilam","family":"Gross","sequence":"additional","affiliation":[]},{"given":"Lukas","family":"Heinrich","sequence":"additional","affiliation":[]},{"given":"Marumi","family":"Kado","sequence":"additional","affiliation":[]},{"given":"Nilotpal","family":"Kakati","sequence":"additional","affiliation":[]},{"given":"Patrick","family":"Rieck","sequence":"additional","affiliation":[]},{"given":"Lorenzo","family":"Santi","sequence":"additional","affiliation":[]},{"given":"Matteo","family":"Tusoni","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"mlstacf186bib1","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1146\/annurev-nucl-101917-021019","article-title":"Deep learning and its application to LHC physics","volume":"68","author":"Guest","year":"2018","journal-title":"Ann. 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Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2023-03-14","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-08-17","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-09-05","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}