Dell’Aquila, D. and Russo, M. (2025) Charged-particle identification with advanced artificial intelligence approaches. Il nuovo cimento C, 48 (2). pp. 1-8. ISSN 1826-9885
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Abstract
Modern nucleus-nucleus collision experiments require the use of advanced particle identification techniques. However, similar tasks are often time-consuming, enhancing the complexity of the data analysis process. We develop a novel approach capable to automatically identify charge and mass of detected ions with almost zero human supervision. Our method uses evolutionary computing and clustering algorithms and exploits previously developed analytical functionals to provide physics constraints. The new algorithm is successfully tested on ΔE-E telescopes based on annular silicon strip detectors and could be integrated in online and offline analysis pipelines of existing detection arrays.
| Item Type: | Article |
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| Subjects: | 500 Scienze naturali e Matematica > 530 Fisica |
| Depositing User: | Marina Spanti |
| Date Deposited: | 31 Mar 2025 13:47 |
| Last Modified: | 31 Mar 2025 13:47 |
| URI: | http://eprints.bice.rm.cnr.it/id/eprint/23541 |
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