Deuterium-deuterium fusion charged particle detection using CR-39 and Deep Learning Model
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908356834230272 |
|---|---|
| author | Wang, Yuxing Chen, Allan Xi Salazar, Matthew Abdalla, Nawar Li, Zhifei Wrixon, Benjamin |
| author_facet | Wang, Yuxing Chen, Allan Xi Salazar, Matthew Abdalla, Nawar Li, Zhifei Wrixon, Benjamin |
| contents | CR-39 solid-state nuclear track detectors are widely used in fusion research for detecting charged particles produced in fusion reactions. However, analyzing increasingly complex and large-scale CR-39 track images to extract meaningful information can be a tedious and time-consuming process, often prone to human errors and bias. To address these challenges, we developed an AI-based classification model capable of differentiating protons, tritons, and helions produced during D-D fusion, using CR-39 track images as input data. The CR-39 track images were processed and used to train a deep learning model. By preprocessing the track images for noise reduction and feature enhancement, we trained the YOLOv8 [1][2] network to distinguish the three particle types with high accuracy. The proposed model achieved a classification accuracy of over 96%, demonstrating its potential for improving automated track analysis in CR-39 detectors. Additionally, the model precisely identifies particle coordinates and counts, enabling comprehensive particle analysis. This study highlights the application of AI in track detection and classification, offering a robust solution for particle identification in CR-39 detector-based experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deuterium-deuterium fusion charged particle detection using CR-39 and Deep Learning Model Wang, Yuxing Chen, Allan Xi Salazar, Matthew Abdalla, Nawar Li, Zhifei Wrixon, Benjamin Instrumentation and Detectors High Energy Physics - Experiment Nuclear Experiment CR-39 solid-state nuclear track detectors are widely used in fusion research for detecting charged particles produced in fusion reactions. However, analyzing increasingly complex and large-scale CR-39 track images to extract meaningful information can be a tedious and time-consuming process, often prone to human errors and bias. To address these challenges, we developed an AI-based classification model capable of differentiating protons, tritons, and helions produced during D-D fusion, using CR-39 track images as input data. The CR-39 track images were processed and used to train a deep learning model. By preprocessing the track images for noise reduction and feature enhancement, we trained the YOLOv8 [1][2] network to distinguish the three particle types with high accuracy. The proposed model achieved a classification accuracy of over 96%, demonstrating its potential for improving automated track analysis in CR-39 detectors. Additionally, the model precisely identifies particle coordinates and counts, enabling comprehensive particle analysis. This study highlights the application of AI in track detection and classification, offering a robust solution for particle identification in CR-39 detector-based experiments. |
| title | Deuterium-deuterium fusion charged particle detection using CR-39 and Deep Learning Model |
| topic | Instrumentation and Detectors High Energy Physics - Experiment Nuclear Experiment |
| url | https://arxiv.org/abs/2503.06932 |