Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913871494643712 |
|---|---|
| author | Kakati, Nilotpal Dreyer, Etienne Gross, Eilam |
| author_facet | Kakati, Nilotpal Dreyer, Etienne Gross, Eilam |
| contents | In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the cost and construction complexity of such detectors is substantial, making software-based approaches like super-resolution an attractive alternative. This study explores integrating super-resolution techniques into an LHC-like reconstruction pipeline to effectively enhance calorimeter granularity and suppress noise. We find that this software preprocessing step significantly improves reconstruction quality without physical changes to the detector. To demonstrate its impact, we propose a novel transformer-based particle flow model that offers improved particle reconstruction quality and interpretability. Our results demonstrate that super-resolution can be readily applied at collider experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16052 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction Kakati, Nilotpal Dreyer, Etienne Gross, Eilam High Energy Physics - Experiment Machine Learning In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the cost and construction complexity of such detectors is substantial, making software-based approaches like super-resolution an attractive alternative. This study explores integrating super-resolution techniques into an LHC-like reconstruction pipeline to effectively enhance calorimeter granularity and suppress noise. We find that this software preprocessing step significantly improves reconstruction quality without physical changes to the detector. To demonstrate its impact, we propose a novel transformer-based particle flow model that offers improved particle reconstruction quality and interpretability. Our results demonstrate that super-resolution can be readily applied at collider experiments. |
| title | Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction |
| topic | High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2409.16052 |