No Cover Image

Journal article 20 views

Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF

Sascha Caron, Andreas Ipp Orcid Logo, Gert Aarts Orcid Logo, Gábor Bíró, Daniele Bonacorsi, Elena Cuoco Orcid Logo, Caterina Doglioni, Tommaso Dorigo Orcid Logo, Julián García Pardiñas, Stefano Giagu Orcid Logo, Tobias Golling Orcid Logo, Lukas Heinrich Orcid Logo, Ik Siong Heng Orcid Logo, Paula Gina Isar, Karolos Potamianos Orcid Logo, Liliana Teodorescu Orcid Logo, John Veitch, Pietro Vischia, Christoph Weniger

Machine Learning: Science and Technology, Volume: 7, Issue: 1, Start page: 013002

Swansea University Author: Gert Aarts Orcid Logo

Full text not available from this repository: check for access using links below.

Check full text

DOI (Published version): 10.1088/2632-2153/ae35cd

Abstract

Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF i...

Full description

Published in: Machine Learning: Science and Technology
ISSN: 2632-2153
Published: IOP Publishing 2026
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa71411
first_indexed 2026-02-13T06:39:25Z
last_indexed 2026-02-14T05:31:40Z
id cronfa71411
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2026-02-13T06:39:22.9391674</datestamp><bib-version>v2</bib-version><id>71411</id><entry>2026-02-13</entry><title>Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF</title><swanseaauthors><author><sid>1ba0dad382dfe18348ec32fc65f3f3de</sid><ORCID>0000-0002-6038-3782</ORCID><firstname>Gert</firstname><surname>Aarts</surname><name>Gert Aarts</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2026-02-13</date><deptcode>BGPS</deptcode><abstract>Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&amp;D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.</abstract><type>Journal Article</type><journal>Machine Learning: Science and Technology</journal><volume>7</volume><journalNumber>1</journalNumber><paginationStart>013002</paginationStart><paginationEnd/><publisher>IOP Publishing</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2632-2153</issnElectronic><keywords/><publishedDay>1</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2026</publishedYear><publishedDate>2026-02-01</publishedDate><doi>10.1088/2632-2153/ae35cd</doi><url>https://doi.org/10.1088/2632-2153/ae35cd</url><notes/><college>COLLEGE NANME</college><department>Biosciences Geography and Physics School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BGPS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2026-02-13T06:39:22.9391674</lastEdited><Created>2026-02-13T06:34:24.3053600</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Biosciences, Geography and Physics - Physics</level></path><authors><author><firstname>Sascha</firstname><surname>Caron</surname><order>1</order></author><author><firstname>Andreas</firstname><surname>Ipp</surname><orcid>0000-0001-9511-3523</orcid><order>2</order></author><author><firstname>Gert</firstname><surname>Aarts</surname><orcid>0000-0002-6038-3782</orcid><order>3</order></author><author><firstname>G&#xE1;bor</firstname><surname>B&#xED;r&#xF3;</surname><order>4</order></author><author><firstname>Daniele</firstname><surname>Bonacorsi</surname><order>5</order></author><author><firstname>Elena</firstname><surname>Cuoco</surname><orcid>0000-0002-6528-3449</orcid><order>6</order></author><author><firstname>Caterina</firstname><surname>Doglioni</surname><order>7</order></author><author><firstname>Tommaso</firstname><surname>Dorigo</surname><orcid>0000-0002-1659-8727</orcid><order>8</order></author><author><firstname>Juli&#xE1;n Garc&#xED;a</firstname><surname>Pardi&#xF1;as</surname><order>9</order></author><author><firstname>Stefano</firstname><surname>Giagu</surname><orcid>0000-0001-9192-3537</orcid><order>10</order></author><author><firstname>Tobias</firstname><surname>Golling</surname><orcid>0000-0001-8535-6687</orcid><order>11</order></author><author><firstname>Lukas</firstname><surname>Heinrich</surname><orcid>0000-0002-4048-7584</orcid><order>12</order></author><author><firstname>Ik Siong</firstname><surname>Heng</surname><orcid>0000-0002-1977-0019</orcid><order>13</order></author><author><firstname>Paula Gina</firstname><surname>Isar</surname><order>14</order></author><author><firstname>Karolos</firstname><surname>Potamianos</surname><orcid>0000-0001-7839-9785</orcid><order>15</order></author><author><firstname>Liliana</firstname><surname>Teodorescu</surname><orcid>0000-0002-6974-6201</orcid><order>16</order></author><author><firstname>John</firstname><surname>Veitch</surname><order>17</order></author><author><firstname>Pietro</firstname><surname>Vischia</surname><order>18</order></author><author><firstname>Christoph</firstname><surname>Weniger</surname><order>19</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2026-02-13T06:39:22.9391674 v2 71411 2026-02-13 Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF 1ba0dad382dfe18348ec32fc65f3f3de 0000-0002-6038-3782 Gert Aarts Gert Aarts true false 2026-02-13 BGPS Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years. Journal Article Machine Learning: Science and Technology 7 1 013002 IOP Publishing 2632-2153 1 2 2026 2026-02-01 10.1088/2632-2153/ae35cd https://doi.org/10.1088/2632-2153/ae35cd COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University 2026-02-13T06:39:22.9391674 2026-02-13T06:34:24.3053600 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Physics Sascha Caron 1 Andreas Ipp 0000-0001-9511-3523 2 Gert Aarts 0000-0002-6038-3782 3 Gábor Bíró 4 Daniele Bonacorsi 5 Elena Cuoco 0000-0002-6528-3449 6 Caterina Doglioni 7 Tommaso Dorigo 0000-0002-1659-8727 8 Julián García Pardiñas 9 Stefano Giagu 0000-0001-9192-3537 10 Tobias Golling 0000-0001-8535-6687 11 Lukas Heinrich 0000-0002-4048-7584 12 Ik Siong Heng 0000-0002-1977-0019 13 Paula Gina Isar 14 Karolos Potamianos 0000-0001-7839-9785 15 Liliana Teodorescu 0000-0002-6974-6201 16 John Veitch 17 Pietro Vischia 18 Christoph Weniger 19
title Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF
spellingShingle Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF
Gert Aarts
title_short Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF
title_full Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF
title_fullStr Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF
title_full_unstemmed Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF
title_sort Strategic white paper on AI infrastructure for particle, nuclear, and astroparticle physics: insights from JENA and EuCAIF
author_id_str_mv 1ba0dad382dfe18348ec32fc65f3f3de
author_id_fullname_str_mv 1ba0dad382dfe18348ec32fc65f3f3de_***_Gert Aarts
author Gert Aarts
author2 Sascha Caron
Andreas Ipp
Gert Aarts
Gábor Bíró
Daniele Bonacorsi
Elena Cuoco
Caterina Doglioni
Tommaso Dorigo
Julián García Pardiñas
Stefano Giagu
Tobias Golling
Lukas Heinrich
Ik Siong Heng
Paula Gina Isar
Karolos Potamianos
Liliana Teodorescu
John Veitch
Pietro Vischia
Christoph Weniger
format Journal article
container_title Machine Learning: Science and Technology
container_volume 7
container_issue 1
container_start_page 013002
publishDate 2026
institution Swansea University
issn 2632-2153
doi_str_mv 10.1088/2632-2153/ae35cd
publisher IOP Publishing
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Physics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Physics
url https://doi.org/10.1088/2632-2153/ae35cd
document_store_str 0
active_str 0
description Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.
published_date 2026-02-01T06:47:16Z
_version_ 1857625822137942016
score 11.096768

Similar Items