Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.15867 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908326232588288 |
|---|---|
| author | Kopp, Joachim Machado, Pedro MacMahon, Margot Martinez-Soler, Ivan |
| author_facet | Kopp, Joachim Machado, Pedro MacMahon, Margot Martinez-Soler, Ivan |
| contents | Faithful energy reconstruction is foundational for precision neutrino experiments like DUNE, but is hindered by uncertainties in our understanding of neutrino--nucleus interactions. Here, we demonstrate that dense neural networks are very effective in overcoming these uncertainties by estimating inaccessible kinematic variables based on the observable part of the final state. We find improvements in the energy resolution by up to a factor of two compared to conventional reconstruction algorithms, which translates into an improved physics performance equivalent to a 10-30% increase in the exposure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15867 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Neutrino Energy Reconstruction with Machine Learning Kopp, Joachim Machado, Pedro MacMahon, Margot Martinez-Soler, Ivan High Energy Physics - Phenomenology High Energy Physics - Experiment Faithful energy reconstruction is foundational for precision neutrino experiments like DUNE, but is hindered by uncertainties in our understanding of neutrino--nucleus interactions. Here, we demonstrate that dense neural networks are very effective in overcoming these uncertainties by estimating inaccessible kinematic variables based on the observable part of the final state. We find improvements in the energy resolution by up to a factor of two compared to conventional reconstruction algorithms, which translates into an improved physics performance equivalent to a 10-30% increase in the exposure. |
| title | Improving Neutrino Energy Reconstruction with Machine Learning |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2405.15867 |