{"id":"https://openalex.org/W7214767978","doi":"https://doi.org/10.48550/arxiv.2609.34500","title":"Using Graph Neural Networks for the segmentation of overlapping objects in high granularity calorimeters","display_name":"Using Graph Neural Networks for the segmentation of overlapping objects in high granularity calorimeters","publication_year":2026,"publication_date":"2026-09-28","ids":{"openalex":"https://openalex.org/W7214767978","doi":"https://doi.org/10.48550/arxiv.2609.34500"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.34500","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34500","pdf_url":"https://arxiv.org/pdf/2609.34500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.34500","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116188720","display_name":"Matthieu Melennec","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I142476485","display_name":"\u00c9cole Polytechnique","ror":"https://ror.org/05hy3tk52","country_code":"FR","type":"education","lineage":["https://openalex.org/I142476485","https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I4210133362","display_name":"Institut National de Physique Nucl\u00e9aire et de Physique des Particules","ror":"https://ror.org/03fd77x13","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210133362"]},{"id":"https://openalex.org/I4210153175","display_name":"Laboratoire Leprince-Ringuet","ror":"https://ror.org/058t6p923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I142476485","https://openalex.org/I4210145102","https://openalex.org/I4210153175"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Melennec, Matthieu","raw_affiliation_strings":["Ecole Polytechnique , IN2P3-CNRS , Laboratoire Leprince-Ringuet , F-91120 Palaiseau , France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ecole Polytechnique , IN2P3-CNRS , Laboratoire Leprince-Ringuet , F-91120 Palaiseau , France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I142476485","https://openalex.org/I4210133362","https://openalex.org/I4210153175"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063191626","display_name":"F. Magniette","orcid":"https://orcid.org/0000-0002-8330-5197","observed_orcids":["https://orcid.org/0000-0002-8330-5197"]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I142476485","display_name":"\u00c9cole Polytechnique","ror":"https://ror.org/05hy3tk52","country_code":"FR","type":"education","lineage":["https://openalex.org/I142476485","https://openalex.org/I4210145102"]},{"id":"https://openalex.org/I4210133362","display_name":"Institut National de Physique Nucl\u00e9aire et de Physique des Particules","ror":"https://ror.org/03fd77x13","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I4210133362"]},{"id":"https://openalex.org/I4210153175","display_name":"Laboratoire Leprince-Ringuet","ror":"https://ror.org/058t6p923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I142476485","https://openalex.org/I4210145102","https://openalex.org/I4210153175"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Magniette, Fr\u00e9d\u00e9ric","raw_affiliation_strings":["Ecole Polytechnique , IN2P3-CNRS , Laboratoire Leprince-Ringuet , F-91120 Palaiseau , France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ecole Polytechnique , IN2P3-CNRS , Laboratoire Leprince-Ringuet , F-91120 Palaiseau , France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I142476485","https://openalex.org/I4210133362","https://openalex.org/I4210153175"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11044","display_name":"Particle Detector Development and Performance","score":0.9564999938011169,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11044","display_name":"Particle Detector Development and Performance","score":0.9564999938011169,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10048","display_name":"Particle physics theoretical and experimental studies","score":0.011699999682605267,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.003700000001117587,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.9940000176429749},{"id":"https://openalex.org/keywords/cms-high-granularity-calorimeter","display_name":"CMS High Granularity Calorimeter","score":0.8569999933242798},{"id":"https://openalex.org/keywords/high-luminosity-lhc","display_name":"High-Luminosity LHC","score":0.9660000205039978},{"id":"https://openalex.org/keywords/convolution-acceleration","display_name":"convolution acceleration","score":0.5740000009536743}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6690000295639038},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6402999758720398},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5997999906539917},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5654000043869019},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5332000255584717},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5185999870300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5001999735832214},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4293000102043152},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40639999508857727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37310001254081726},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.35929998755455017},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3109999895095825},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3018999993801117},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.2921999990940094},{"id":"https://openalex.org/C87668248","wikidata":"https://www.wikidata.org/wiki/Q40605","display_name":"Large Hadron Collider","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C2777759810","wikidata":"https://www.wikidata.org/wiki/Q149316","display_name":"Lemma (botany)","level":3,"score":0.27889999747276306}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2609.34500","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34500","pdf_url":"https://arxiv.org/pdf/2609.34500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-05781887v1","doi":null,"is_oa":false,"landing_page_url":"https://hal.science/hal-05781887","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2026","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2609.34500","doi":"https://doi.org/10.48550/arxiv.2609.34500","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.34500","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.34500","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34500","pdf_url":"https://arxiv.org/pdf/2609.34500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4948879953","display_name":"Optimal graph convolution for efficient particle identification","funder_award_id":"ANR-21-CE31-0030","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214767978.pdf","grobid_xml":"https://content.openalex.org/works/W7214767978.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7127163788","https://openalex.org/W4224903182","https://openalex.org/W3171268361","https://openalex.org/W6944061336","https://openalex.org/W6998767099","https://openalex.org/W7221140969","https://openalex.org/W3149235451","https://openalex.org/W7213485738","https://openalex.org/W3039220421","https://openalex.org/W7162966700"],"abstract_inverted_index":{"High-granularity":[0],"calorimeters":[1],"at":[2],"the":[3],"High-Luminosity":[4],"LHC":[5],"require":[6],"novel":[7],"algorithms":[8],"to":[9,29],"resolve":[10],"overlapping":[11,31],"particle":[12],"showers.":[13,33],"We":[14],"present":[15],"an":[16],"optimised":[17],"Graph":[18],"Neural":[19],"Network":[20],"(GNN)":[21],"segmentation":[22],"block":[23],"that":[24],"predicts":[25],"node-level":[26],"energy":[27],"fractions":[28],"disentangle":[30],"two-photon":[32],"By":[34],"accelerating":[35],"graph":[36],"construction":[37],"and":[38],"convolution":[39],"operations,":[40],"our":[41],"pipeline":[42],"achieves":[43],"competitive":[44],"separation":[45],"efficiency":[46],"with":[47],"significantly":[48],"reduced":[49],"algorithmic":[50],"complexity.":[51]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-30T00:00:00"}