CHEP 2018: Preface to the Proceedings

2019 ◽  
Vol 214 ◽  
pp. 00001
Alessandra Forti ◽  
Latchezar Betev ◽  
Maarten Litmaath ◽  
Oxana Smirnova ◽  
Petya Vasileva ◽  

The 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP) took place in the National Palace of Culture, Sofia, Bulgaria from 9th to 13th of July 2018. 575 participants joined the plenary and the eight parallel sessions dedicated to: online computing; offline computing; distributed computing; data handling; software development; machine learning and physics analysis; clouds, virtualisation and containers; networks and facilities. The conference hosted 35 plenary presentations, 323 parallel presentations and 188 posters.

2020 ◽  
Vol 245 ◽  
pp. 00001
Caterina Doglioni ◽  
Paul Jackson ◽  
Waseem Kamleh ◽  
Doris Y. Kim ◽  
Lucia Silvestris ◽  

The 24th International Conference on Computing in High Energy and Nuclear Physics (CHEP) took place at the Adelaide Convention Centre, Adelaide, South Australia from 4–8 November 2019. 525 registered participants took part in the conference, where there were plenary sessions as well as a wide ranging set of ten parallel tracks across all areas of work in the field and allied sciences. The conference hosted 34 plenary presentations, 370 oral presentations in parallel sessions and 131 posters.

2012 ◽  
Vol 396 (00) ◽  
pp. 001001
Michael Ernst ◽  
Dirk Düllmann ◽  
Ofer Rind ◽  
Tony Wong

2020 ◽  
Vol 245 ◽  
pp. 06015
Thomas Britton ◽  
David Lawrence ◽  
Gagik Gavalian

Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics (NP) experiments. An effort is underway to develop machine learning (ML) networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to high energy physics (HEP). In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differences and similarities between NP and HEP charged particle tracking and areas where ML learning may provide a benefit. The status of the specific effort taking place at Jefferson Lab will also be shown.

2020 ◽  
Vol 10 (11) ◽  
pp. 3681
Hosung Woo ◽  
JaMee Kim ◽  
WonGyu Lee

Artificial intelligence (AI) is bringing about enormous changes in everyday life and today’s society. Interest in AI is continuously increasing as many countries are creating new AI-related degrees, short-term intensive courses, and secondary school programs. This study was conducted with the aim of identifying the interrelationships among topics based on the understanding of various bodies of knowledge and to provide a foundation for topic compositions to construct an academic body of knowledge of AI. To this end, machine learning-based sentence similarity measurement models used in machine translation, chatbots, and document summarization were applied to the body of knowledge of AI. Consequently, several similar topics related to agent designing in AI, such as algorithm complexity, discrete structures, fundamentals of software development, and parallel and distributed computing were identified. The results of this study provide the knowledge necessary to cultivate talent by identifying relationships with other fields in the edutech field.

2021 ◽  
Vol 251 ◽  
pp. 00001
Catherine Biscarat ◽  
Simone Campana ◽  
Benedikt Hegner ◽  
Stefan Roiser ◽  
Chiara I. Rovelli ◽  

The 25th International Conference on Computing in High Energy and Nuclear Physics (CHEP), organised by CERN, took place as a virtual event from 17–21 May 2021. The conference attracted 1144 registered participants from 46 different countries. There were 207 scientific presentations made over the 5 days of the conference. These were divided between 30 long talks and 2 keynotes, which were presented in plenary sessions; and 175 short talks, which were presented in parallel sessions.

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