Neural networks for impact parameter determination
- Abstract: An accurate impact parameter determination in a heavy ion collision is crucial for almost all further analysis. The capabilities of an artificial neural network are investigated to that respect. A novel input generation for the network is proposed, namely the transverse and longitudinal momentum distribution of all outgoing (or actually detectable) particles. The neural network approach yields an improvement in performance of a factor of two as compared to classical techniques. To achieve this improvement simple network architectures and a 5 × 5 input grid in (pt, pz) space are suffcient.
| Author: | Steffen A. BassORCiDGND, Arnd Bischoff, Joachim MaruhnORCiDGND, Horst StöckerORCiDGND, Walter GreinerGND |
|---|---|
| URN: | urn:nbn:de:hebis:30-24030 |
| ArXiv Id: | http://arxiv.org/abs/9601024v1 |
| Document Type: | Preprint |
| Language: | English |
| Date of Publication (online): | 2006/01/23 |
| Year of first Publication: | 1996 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2006/01/23 |
| Tag: | Kollisionen schwerer Ionen; heiße und dichte Kernmaterie heavy ion collisions; hot and dense nuclear matter |
| Page Number: | 18 |
| First Page: | 1 |
| Last Page: | 18 |
| Source: | Phys.Rev.C53:2358-2363,1996 ; http://arxiv.org/abs/nucl-th/9601024 |
| HeBIS-PPN: | 185203264 |
| Institutes: | Physik / Physik |
| Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
| Licence (German): |  Deutsches Urheberrecht |
