Cargando…
Machine learning for surface prediction in ACTS
We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS...
| Autores principales: | , , |
|---|---|
| Lenguaje: | eng |
| Publicado: |
2021
|
| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1051/epjconf/202125103053 http://cds.cern.ch/record/2778220 |
| _version_ | 1780971733451276288 |
|---|---|
| author | Huth, Benjamin Salzburger, Andreas Wettig, Tilo |
| author_facet | Huth, Benjamin Salzburger, Andreas Wettig, Tilo |
| author_sort | Huth, Benjamin |
| collection | CERN |
| description | We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit. |
| id | cern-2778220 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2021 |
| record_format | invenio |
| spelling | cern-27782202023-01-31T11:02:36Zdoi:10.1051/epjconf/202125103053http://cds.cern.ch/record/2778220engHuth, BenjaminSalzburger, AndreasWettig, TiloMachine learning for surface prediction in ACTShep-exParticle Physics - Experimentcs.LGComputing and Computersphysics.ins-detDetectors and Experimental TechniquesWe present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit.We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit.arXiv:2108.03068oai:cds.cern.ch:27782202021 |
| spellingShingle | hep-ex Particle Physics - Experiment cs.LG Computing and Computers physics.ins-det Detectors and Experimental Techniques Huth, Benjamin Salzburger, Andreas Wettig, Tilo Machine learning for surface prediction in ACTS |
| title | Machine learning for surface prediction in ACTS |
| title_full | Machine learning for surface prediction in ACTS |
| title_fullStr | Machine learning for surface prediction in ACTS |
| title_full_unstemmed | Machine learning for surface prediction in ACTS |
| title_short | Machine learning for surface prediction in ACTS |
| title_sort | machine learning for surface prediction in acts |
| topic | hep-ex Particle Physics - Experiment cs.LG Computing and Computers physics.ins-det Detectors and Experimental Techniques |
| url | https://dx.doi.org/10.1051/epjconf/202125103053 http://cds.cern.ch/record/2778220 |
| work_keys_str_mv | AT huthbenjamin machinelearningforsurfacepredictioninacts AT salzburgerandreas machinelearningforsurfacepredictioninacts AT wettigtilo machinelearningforsurfacepredictioninacts |