Les Houches guide to reusable ML models in LHC analyses
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917920397852672 |
|---|---|
| author | Araz, Jack Y. Buckley, Andy Kasieczka, Gregor Kieseler, Jan Kraml, Sabine Kvellestad, Anders Lessa, Andre Procter, Tomasz Raklev, Are Reyes-Gonzalez, Humberto Rolbiecki, Krzysztof Sekmen, Sezen Unel, Gokhan |
| author_facet | Araz, Jack Y. Buckley, Andy Kasieczka, Gregor Kieseler, Jan Kraml, Sabine Kvellestad, Anders Lessa, Andre Procter, Tomasz Raklev, Are Reyes-Gonzalez, Humberto Rolbiecki, Krzysztof Sekmen, Sezen Unel, Gokhan |
| contents | With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_14575 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Les Houches guide to reusable ML models in LHC analyses Araz, Jack Y. Buckley, Andy Kasieczka, Gregor Kieseler, Jan Kraml, Sabine Kvellestad, Anders Lessa, Andre Procter, Tomasz Raklev, Are Reyes-Gonzalez, Humberto Rolbiecki, Krzysztof Sekmen, Sezen Unel, Gokhan High Energy Physics - Phenomenology High Energy Physics - Experiment With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches. |
| title | Les Houches guide to reusable ML models in LHC analyses |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2312.14575 |