| _version_ | 1866901497362513920 |
|---|---|
| author | El Amrani, Rachid Buccelli, Giorgia Rosalia |
| author_facet | El Amrani, Rachid Buccelli, Giorgia Rosalia |
| contents | <p>In this work, we introduce a possible approach to the task of detecting the positions where particles pass in their trajectories which is one of the long-standing tasks within the field of particle physics. In particular, the proposed approach consists in retrieving all available information from the characteristics of the signals measured by the pads of a sensor for each event, referring to the passage of a particle through the sensor, and analyzing the dataset in order to use just the most informative part of it. Then, by using it as input for regression modeling, we propose a model capable of predicting for each event, the 2D coordinates where the particle of interest passed while obtaining overall satisfactory results.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15299674 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Particle position prediction Using multi-output regression models El Amrani, Rachid Buccelli, Giorgia Rosalia <p>In this work, we introduce a possible approach to the task of detecting the positions where particles pass in their trajectories which is one of the long-standing tasks within the field of particle physics. In particular, the proposed approach consists in retrieving all available information from the characteristics of the signals measured by the pads of a sensor for each event, referring to the passage of a particle through the sensor, and analyzing the dataset in order to use just the most informative part of it. Then, by using it as input for regression modeling, we propose a model capable of predicting for each event, the 2D coordinates where the particle of interest passed while obtaining overall satisfactory results.</p> |
| title | Particle position prediction Using multi-output regression models |
| url | https://doi.org/10.5281/zenodo.15299674 |