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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.06983 |
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| _version_ | 1866915354212564992 |
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| author | Ragoni, Simone Seger, Janet Anson, Christopher Tlusty, David |
| author_facet | Ragoni, Simone Seger, Janet Anson, Christopher Tlusty, David |
| contents | The increasing data rates in modern high-energy physics experiments such as ALICE at the LHC and the upcoming ePIC experiment at the Electron-Ion Collider (EIC) present significant challenges in real-time event selection and data storage. This paper explores the novel application of machine learning techniques, to enhance the identification of rare low-multiplicity events, such as ultraperipheral collisions (UPCs) and central exclusive diffractive processes. We focus on utilising machine learning models to perform early event classification, even before full event reconstruction, in continuous readout systems. We estimate data rates and disk space requirements for photoproduction and central exclusive diffractive processes in both ALICE and ePIC. We show that machine learning techniques can not only optimize data selection but also significantly reduce storage requirements in continuous readout environments, providing a scalable solution for the upcoming era of high-luminosity particle physics experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06983 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning opportunities for online and offline tagging of photo-induced and diffractive events in continuous readout experiments Ragoni, Simone Seger, Janet Anson, Christopher Tlusty, David High Energy Physics - Experiment The increasing data rates in modern high-energy physics experiments such as ALICE at the LHC and the upcoming ePIC experiment at the Electron-Ion Collider (EIC) present significant challenges in real-time event selection and data storage. This paper explores the novel application of machine learning techniques, to enhance the identification of rare low-multiplicity events, such as ultraperipheral collisions (UPCs) and central exclusive diffractive processes. We focus on utilising machine learning models to perform early event classification, even before full event reconstruction, in continuous readout systems. We estimate data rates and disk space requirements for photoproduction and central exclusive diffractive processes in both ALICE and ePIC. We show that machine learning techniques can not only optimize data selection but also significantly reduce storage requirements in continuous readout environments, providing a scalable solution for the upcoming era of high-luminosity particle physics experiments. |
| title | Machine learning opportunities for online and offline tagging of photo-induced and diffractive events in continuous readout experiments |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2410.06983 |