Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.17325 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912366936981504 |
|---|---|
| author | Odyurt, Uraz Dobreva, Nadezhda Wolffs, Zef Zhao, Yue Sánchez, Antonio Ferrer Bazan, Roberto Ruiz de Austri Martín-Guerrero, José D. Varbanescu, Ana-Lucia Caron, Sascha |
| author_facet | Odyurt, Uraz Dobreva, Nadezhda Wolffs, Zef Zhao, Yue Sánchez, Antonio Ferrer Bazan, Roberto Ruiz de Austri Martín-Guerrero, José D. Varbanescu, Ana-Lucia Caron, Sascha |
| contents | Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorithmic design effort by utilising a simplified simulator (REDVID) to generate training data that is specifically composed for simplicity. We demonstrate the effectiveness of this data in guiding the development of optimal network architectures. Additionally, we investigate the application of image segmentation networks for this task, exploring their potential for accurate track reconstruction. Moreover, we approach the task from a different perspective by treating it as a hit sequence to track sequence translation problem. Specifically, we explore the utilisation of Transformer architectures for tracking purposes. Our preliminary findings are covered in detail. By considering this novel approach, we aim to uncover new insights and potential advancements in track reconstruction. This research sheds light on previously unexplored methods and provides valuable insights for the field of particle track reconstruction and hit clustering in HEP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17325 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering Odyurt, Uraz Dobreva, Nadezhda Wolffs, Zef Zhao, Yue Sánchez, Antonio Ferrer Bazan, Roberto Ruiz de Austri Martín-Guerrero, José D. Varbanescu, Ana-Lucia Caron, Sascha High Energy Physics - Experiment Machine Learning Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorithmic design effort by utilising a simplified simulator (REDVID) to generate training data that is specifically composed for simplicity. We demonstrate the effectiveness of this data in guiding the development of optimal network architectures. Additionally, we investigate the application of image segmentation networks for this task, exploring their potential for accurate track reconstruction. Moreover, we approach the task from a different perspective by treating it as a hit sequence to track sequence translation problem. Specifically, we explore the utilisation of Transformer architectures for tracking purposes. Our preliminary findings are covered in detail. By considering this novel approach, we aim to uncover new insights and potential advancements in track reconstruction. This research sheds light on previously unexplored methods and provides valuable insights for the field of particle track reconstruction and hit clustering in HEP. |
| title | Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering |
| topic | High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2405.17325 |