Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.18674 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910806213394432 |
|---|---|
| author | Li, Mingyang Kuchera, Michelle Ramanujan, Raghuram Anthony, Adam Hunt, Curtis Ayyad, Yassid |
| author_facet | Li, Mingyang Kuchera, Michelle Ramanujan, Raghuram Anthony, Adam Hunt, Curtis Ayyad, Yassid |
| contents | Modeling detector response is a key challenge in time projection chambers. We cast this problem as an unpaired point cloud translation task, between data collected from simulations and from experimental runs. Effective translation can assist with both noise rejection and the construction of high-fidelity simulators. Building on recent work in diffusion probabilistic models, we present a novel framework for performing this mapping. We demonstrate the success of our approach in both synthetic domains and in data sourced from the Active-Target Time Projection Chamber. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18674 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unpaired Translation of Point Clouds for Modeling Detector Response Li, Mingyang Kuchera, Michelle Ramanujan, Raghuram Anthony, Adam Hunt, Curtis Ayyad, Yassid Computer Vision and Pattern Recognition Machine Learning Nuclear Experiment Modeling detector response is a key challenge in time projection chambers. We cast this problem as an unpaired point cloud translation task, between data collected from simulations and from experimental runs. Effective translation can assist with both noise rejection and the construction of high-fidelity simulators. Building on recent work in diffusion probabilistic models, we present a novel framework for performing this mapping. We demonstrate the success of our approach in both synthetic domains and in data sourced from the Active-Target Time Projection Chamber. |
| title | Unpaired Translation of Point Clouds for Modeling Detector Response |
| topic | Computer Vision and Pattern Recognition Machine Learning Nuclear Experiment |
| url | https://arxiv.org/abs/2501.18674 |