| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01342 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/epjconf/202533701342 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701342
Computing and machine learning for the SHiP experiment
INFN Sezione di Napoli, Complesso Universitario di Monte Sant’Angelo, 80126 Napoli, Italy
* e-mail: oliver.lantwin@cern.ch
Published online: 7 October 2025
The recently approved SHiP experiment aims to search for new physics at the intensity frontier, including feebly interacting particles and light dark matter, and perform precision measurements of tau neutrinos.
To fulfil its full discovery potential, the SHiP software framework is crucial, and faces some unique challenges due to the broad range of models under study, and the extreme statistics necessary for the background studies. The SHiP environment also offers unique opportunities for machine learning for detector design and anomaly detection.
This contribution gives an overview of the general software framework and of past, ongoing and planned simulation and machine learning studies.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.