Publications
Publication Information
| Title | Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation |
| Authors | Fernando Torales-Acosta, Vinicius Mikuni, Benjamin Nachman, Miguel Arratia Munoz, Bishnu Karki, Ryan Milton, Piyush Karande, Aaron Angerami |
| JLAB number | JLAB-PHY-24-4103 |
| LANL number | arXiv:2307.04780 |
| Other number | DOE/OR/23177-7543 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Journal Compiled for Journal of Instrumentation Volume 19 Page(s) P05003 | |
| Publication Abstract: | Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. |
| Experiment Numbers: | |
| Group: | Hall B |
| Document: | |
| DOI: | http://dx.doi.org/10.1088/1748-0221/19/05/P05003 |
| Accepted Manuscript: | 2307.04780v2.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |