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Projects / Programmes source: ARIS

Application of Machine Learning Methods in the Data Analysis at the Large Hadron Collider (LHC)

Research activity

Code Science Field Subfield
1.02.00  Natural sciences and mathematics  Physics   

Code Science Field
1.03  Natural Sciences  Physical sciences 
Keywords
xperimental particle physics, data analysis, CERN, LHC, ATLAS, LHC upgrade to High-Lumi LHC (HL-LHC), machine learning
Evaluation (rules)
source: COBISS
Points
11,865.08
A''
1,566.52
A'
9,431.47
A1/2
10,923.36
CI10
90,542
CImax
6,520
h10
127
A1
46.26
A3
0.74
Data for the last 5 years (citations for the last 10 years) on June 17, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  2,137  99,272  84,661  39.62 
Scopus  2,183  126,246  109,446  50.14 
Researchers (9)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  07525  PhD Andrej Filipčič  Physics  Researcher  2023 - 2024  2,054 
2.  18277  PhD Andrej Gorišek  Physics  Researcher  2021 - 2024  1,415 
3.  18278  PhD Borut Paul Kerševan  Physics  Head  2021 - 2024  1,412 
4.  15642  PhD Gregor Kramberger  Physics  Researcher  2021 - 2024  1,569 
5.  28481  PhD Boštjan Maček  Physics  Researcher  2021 - 2024  1,037 
6.  12313  PhD Igor Mandić  Physics  Researcher  2021 - 2024  1,552 
7.  37479  PhD Miha Muškinja  Physics  Researcher  2023 - 2024  698 
8.  21552  PhD Andrej Studen  Physics  Researcher  2022 - 2024  134 
9.  32169  PhD Luka Šantelj  Physics  Researcher  2021 - 2024  308 
Organisations (2)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  1554  University of Ljubljana, Faculty of Mathematics and Physics  Ljubljana  1627007  34,377 
2.  0106  Jožef Stefan Institute  Ljubljana  5051606000  91,415 
Abstract
With the increasing complexity of the research in experimental particle physics, looking for new physics signatures in progressively larger and more complex data sets that are being analyzed at the LHC experiments, new approaches to data analysis, from reconstruction to simulation, need to be investigated. The main objective of this project is to develop and test state-of-the-art scientific tools for HEP data simulation, reconstruction and analysis, using software technologies based on Machine Learning in general and Deep Learning in particular. These tools will be executing on the newest (accelerator-enabled) hardware solutions in the HPC super-computing clusters, in order to address the challenges of speed and accuracy, crucial for the existing and next generation of High Energy Physics (HEP) Collider experiments.
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