Neural networks technique based signal-from-background separation and design optimization for a W/quartz fiber calorimeter
| dc.creator | Mavromanolakis, G. | |
| dc.date | 2003-03-14 | |
| dc.date | 2003-04-24 | |
| dc.date.accessioned | 2026-07-07T03:35:42Z | |
| dc.date.available | 2026-07-07T03:35:42Z | |
| dc.description | We present a signal-from-background separation study based on neural networks technique applied to a W/quartz fiber calorimeter. Performance results in terms of signal efficiency and improvement of the signal-to-background ratio are presented. We conclude that by using neural networks we can efficiently separate signal from background and achieve a signal enhancement over the background of the order of several thousands at high efficiency. | |
| dc.description | LaTeX 22 pages, 4 tables, 15 figures | |
| dc.identifier | https://arxiv.org/abs/hep-ex/0303021 | |
| dc.identifier | http://arxiv.org/abs/hep-ex/0303021 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/37455 | |
| dc.subject | High Energy Physics - Experiment | |
| dc.subject | Nuclear Experiment | |
| dc.title | Neural networks technique based signal-from-background separation and design optimization for a W/quartz fiber calorimeter | |
| dc.type | text |