Machine learning methods for subpixel trajectory reconstruction in discretized position detectors
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911335804043264 |
|---|---|
| author | Romano, Matthew Mark Liu, Zhengzhi Bae, JungHyun |
| author_facet | Romano, Matthew Mark Liu, Zhengzhi Bae, JungHyun |
| contents | In this study, we demonstrate that compared with traditional centroid-based methods, machine learning methods (particularly transformer-based architectures) achieve superior subpixel position and therefore angular resolution in discretized particle detectors. Using Geant4 Monte Carlo simulated cosmic ray muon data from an 8x8 segmented scintillator detector array, we compare four reconstruction approaches: transformer neural networks, convolutional neural networks, linear regression, and energy-weighted centroids. The transformer architecture achieves the best angular reconstruction with a root mean square error of 1.14° and a position mean absolute error of 0.24 cm, representing improvements of 2.22x and 6.33x, respectively, over the centroid method. These results enable precise particle trajectory reconstruction for applications in muon tomography and cosmic ray detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20645 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning methods for subpixel trajectory reconstruction in discretized position detectors Romano, Matthew Mark Liu, Zhengzhi Bae, JungHyun Instrumentation and Detectors In this study, we demonstrate that compared with traditional centroid-based methods, machine learning methods (particularly transformer-based architectures) achieve superior subpixel position and therefore angular resolution in discretized particle detectors. Using Geant4 Monte Carlo simulated cosmic ray muon data from an 8x8 segmented scintillator detector array, we compare four reconstruction approaches: transformer neural networks, convolutional neural networks, linear regression, and energy-weighted centroids. The transformer architecture achieves the best angular reconstruction with a root mean square error of 1.14° and a position mean absolute error of 0.24 cm, representing improvements of 2.22x and 6.33x, respectively, over the centroid method. These results enable precise particle trajectory reconstruction for applications in muon tomography and cosmic ray detection. |
| title | Machine learning methods for subpixel trajectory reconstruction in discretized position detectors |
| topic | Instrumentation and Detectors |
| url | https://arxiv.org/abs/2512.20645 |