Publications
Publication Information
| Title | The optimal use of segmentation for sampling calorimeters |
| Authors | Fernando Torales-Acosta, Bishnu Karki, Piyush Karande, Aaron Angerami, Miguel Arratia, Kenneth Barish, Ryan Milton, Sebastian Moran Vasquez, Benjamin Nachman, Anshuman Sinha |
| JLAB number | JLAB-PHY-24-4090 |
| LANL number | arXiv:2310.04442 |
| Other number | DOE/OR/23177-7530 |
| 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) P06002 | |
| Publication Abstract: | One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. To inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub?optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities. |
| Experiment Numbers: | |
| Group: | Hall B |
| Document: | |
| DOI: | http://dx.doi.org/10.1088/1748-0221/19/06/P06002 |
| Accepted Manuscript: | 2310.04442v1.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |