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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2508.09938 |
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| _version_ | 1866912536264179712 |
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| author | Halabi, Muaz Al Bajdel, Marcel Bormans, Jeroen Peter Bossi, Hannah Behling, Maria Calmon Ehmann, Florian Gotz, Niklas Jung, Jerome Kavak, Rafet Krüger, Mario Lakos, Robin Lauterbach, Annemarie Mccormack, Patrick Mithran, Akhil Murnane, Daniel Nolte, Mathis Rogoschinski, Tim Scharf, Jan Tyagi, Oddharak Våge, Liv Valialshchikov, Maxim Wagner, Stephan |
| author_facet | Halabi, Muaz Al Bajdel, Marcel Bormans, Jeroen Peter Bossi, Hannah Behling, Maria Calmon Ehmann, Florian Gotz, Niklas Jung, Jerome Kavak, Rafet Krüger, Mario Lakos, Robin Lauterbach, Annemarie Mccormack, Patrick Mithran, Akhil Murnane, Daniel Nolte, Mathis Rogoschinski, Tim Scharf, Jan Tyagi, Oddharak Våge, Liv Valialshchikov, Maxim Wagner, Stephan |
| contents | In both high-energy physics and industry applications, a crowd-sourced approach to difficult problems is becoming increasingly common. These innovative approaches are ideal for the development of future facilities where the simulations can be publicly distributed, such as the Electron-Ion Collider (EIC). In this paper, we discuss a so-called ``Power Week" where graduate students were able to learn about machine learning while also contributing to an unsolved problem at a future facility. Here, the problem of interest was the clustering of the forward hadronic calorimeter in the foreseen electron-proton/ion collider experiment (ePIC) detector at the EIC. The different possible approaches, developed over the course of a single week, and their performance are detailed and summarised. Feedback on the format of the week and recommendations for future similar programs are provided in the hopes to inspire future learning opportunities for students that also serve as a crowd-sourced approaches to unsolved problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters Halabi, Muaz Al Bajdel, Marcel Bormans, Jeroen Peter Bossi, Hannah Behling, Maria Calmon Ehmann, Florian Gotz, Niklas Jung, Jerome Kavak, Rafet Krüger, Mario Lakos, Robin Lauterbach, Annemarie Mccormack, Patrick Mithran, Akhil Murnane, Daniel Nolte, Mathis Rogoschinski, Tim Scharf, Jan Tyagi, Oddharak Våge, Liv Valialshchikov, Maxim Wagner, Stephan Nuclear Experiment High Energy Physics - Experiment In both high-energy physics and industry applications, a crowd-sourced approach to difficult problems is becoming increasingly common. These innovative approaches are ideal for the development of future facilities where the simulations can be publicly distributed, such as the Electron-Ion Collider (EIC). In this paper, we discuss a so-called ``Power Week" where graduate students were able to learn about machine learning while also contributing to an unsolved problem at a future facility. Here, the problem of interest was the clustering of the forward hadronic calorimeter in the foreseen electron-proton/ion collider experiment (ePIC) detector at the EIC. The different possible approaches, developed over the course of a single week, and their performance are detailed and summarised. Feedback on the format of the week and recommendations for future similar programs are provided in the hopes to inspire future learning opportunities for students that also serve as a crowd-sourced approaches to unsolved problems. |
| title | Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters |
| topic | Nuclear Experiment High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2508.09938 |