Configurable calorimeter simulation for AI applications

A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic partic...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-03
Main Authors: Di Bello, Francesco Armando, Anton Charkin-Gorbulin, Cranmer, Kyle, Dreyer, Etienne, Ganguly, Sanmay, Gross, Eilam, Heinrich, Lukas, Santi, Lorenzo, Kado, Marumi, Kakati, Nilotpal, Rieck, Patrick, Tusoni, Matteo
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Clustering
Cocoa
Machine learning
Simulation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Di Bello, Francesco Armando
Anton Charkin-Gorbulin
Cranmer, Kyle
Dreyer, Etienne
Ganguly, Sanmay
Gross, Eilam
Heinrich, Lukas
Santi, Lorenzo
Kado, Marumi
Kakati, Nilotpal
Rieck, Patrick
Tusoni, Matteo
description A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia 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.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2783520507</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2783520507</sourcerecordid><originalsourceid>FETCH-proquest_journals_27835205073</originalsourceid><addsrcrecordid>eNqNikEKwjAQAIMgWLR_CHguxE1jepWi6N17iSWRlLQbs8n_FfEBngZmZsUqkPLQdC3AhtVEkxACjhqUkhXTPS7OP0syj2D5aAImP9tsEyc_l2Cyx4U7TPx04ybG4Mevoh1bOxPI1j9u2f5yvvfXJiZ8FUt5mLCk5ZMG0J1UIJTQ8r_rDUjbNi8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2783520507</pqid></control><display><type>article</type><title>Configurable calorimeter simulation for AI applications</title><source>Publicly Available Content Database</source><creator>Di Bello, Francesco Armando ; Anton Charkin-Gorbulin ; Cranmer, Kyle ; Dreyer, Etienne ; Ganguly, Sanmay ; Gross, Eilam ; Heinrich, Lukas ; Santi, Lorenzo ; Kado, Marumi ; Kakati, Nilotpal ; Rieck, Patrick ; Tusoni, Matteo</creator><creatorcontrib>Di Bello, Francesco Armando ; Anton Charkin-Gorbulin ; Cranmer, Kyle ; Dreyer, Etienne ; Ganguly, Sanmay ; Gross, Eilam ; Heinrich, Lukas ; Santi, Lorenzo ; Kado, Marumi ; Kakati, Nilotpal ; Rieck, Patrick ; Tusoni, Matteo</creatorcontrib><description>A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia 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.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Algorithms ; Artificial intelligence ; Clustering ; Cocoa ; Machine learning ; Simulation</subject><ispartof>arXiv.org, 2023-03</ispartof><rights>2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2783520507?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25753,37012,44590</link.rule.ids></links><search><creatorcontrib>Di Bello, Francesco Armando</creatorcontrib><creatorcontrib>Anton Charkin-Gorbulin</creatorcontrib><creatorcontrib>Cranmer, Kyle</creatorcontrib><creatorcontrib>Dreyer, Etienne</creatorcontrib><creatorcontrib>Ganguly, Sanmay</creatorcontrib><creatorcontrib>Gross, Eilam</creatorcontrib><creatorcontrib>Heinrich, Lukas</creatorcontrib><creatorcontrib>Santi, Lorenzo</creatorcontrib><creatorcontrib>Kado, Marumi</creatorcontrib><creatorcontrib>Kakati, Nilotpal</creatorcontrib><creatorcontrib>Rieck, Patrick</creatorcontrib><creatorcontrib>Tusoni, Matteo</creatorcontrib><title>Configurable calorimeter simulation for AI applications</title><title>arXiv.org</title><description>A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia 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.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Clustering</subject><subject>Cocoa</subject><subject>Machine learning</subject><subject>Simulation</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNikEKwjAQAIMgWLR_CHguxE1jepWi6N17iSWRlLQbs8n_FfEBngZmZsUqkPLQdC3AhtVEkxACjhqUkhXTPS7OP0syj2D5aAImP9tsEyc_l2Cyx4U7TPx04ybG4Mevoh1bOxPI1j9u2f5yvvfXJiZ8FUt5mLCk5ZMG0J1UIJTQ8r_rDUjbNi8</recordid><startdate>20230308</startdate><enddate>20230308</enddate><creator>Di Bello, Francesco Armando</creator><creator>Anton Charkin-Gorbulin</creator><creator>Cranmer, Kyle</creator><creator>Dreyer, Etienne</creator><creator>Ganguly, Sanmay</creator><creator>Gross, Eilam</creator><creator>Heinrich, Lukas</creator><creator>Santi, Lorenzo</creator><creator>Kado, Marumi</creator><creator>Kakati, Nilotpal</creator><creator>Rieck, Patrick</creator><creator>Tusoni, Matteo</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20230308</creationdate><title>Configurable calorimeter simulation for AI applications</title><author>Di Bello, Francesco Armando ; Anton Charkin-Gorbulin ; Cranmer, Kyle ; Dreyer, Etienne ; Ganguly, Sanmay ; Gross, Eilam ; Heinrich, Lukas ; Santi, Lorenzo ; Kado, Marumi ; Kakati, Nilotpal ; Rieck, Patrick ; Tusoni, Matteo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_27835205073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Clustering</topic><topic>Cocoa</topic><topic>Machine learning</topic><topic>Simulation</topic><toplevel>online_resources</toplevel><creatorcontrib>Di Bello, Francesco Armando</creatorcontrib><creatorcontrib>Anton Charkin-Gorbulin</creatorcontrib><creatorcontrib>Cranmer, Kyle</creatorcontrib><creatorcontrib>Dreyer, Etienne</creatorcontrib><creatorcontrib>Ganguly, Sanmay</creatorcontrib><creatorcontrib>Gross, Eilam</creatorcontrib><creatorcontrib>Heinrich, Lukas</creatorcontrib><creatorcontrib>Santi, Lorenzo</creatorcontrib><creatorcontrib>Kado, Marumi</creatorcontrib><creatorcontrib>Kakati, Nilotpal</creatorcontrib><creatorcontrib>Rieck, Patrick</creatorcontrib><creatorcontrib>Tusoni, Matteo</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Di Bello, Francesco Armando</au><au>Anton Charkin-Gorbulin</au><au>Cranmer, Kyle</au><au>Dreyer, Etienne</au><au>Ganguly, Sanmay</au><au>Gross, Eilam</au><au>Heinrich, Lukas</au><au>Santi, Lorenzo</au><au>Kado, Marumi</au><au>Kakati, Nilotpal</au><au>Rieck, Patrick</au><au>Tusoni, Matteo</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Configurable calorimeter simulation for AI applications</atitle><jtitle>arXiv.org</jtitle><date>2023-03-08</date><risdate>2023</risdate><eissn>2331-8422</eissn><abstract>A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia 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.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2023-03
issn 2331-8422
language eng
recordid cdi_proquest_journals_2783520507
source Publicly Available Content Database
subjects Algorithms
Artificial intelligence
Clustering
Cocoa
Machine learning
Simulation
title Configurable calorimeter simulation for AI applications
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T13%3A52%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Configurable%20calorimeter%20simulation%20for%20AI%20applications&rft.jtitle=arXiv.org&rft.au=Di%20Bello,%20Francesco%20Armando&rft.date=2023-03-08&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2783520507%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_27835205073%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2783520507&rft_id=info:pmid/&rfr_iscdi=true