| Article | |
| Report number | physics/0402070 ; TAUP-2764-04 ; TAUP-2764 |
| Title | Using a neural network approach for muon reconstruction and triggering |
| Author(s) | Etzion, E. (Tel Aviv U.) ; Abramowicz, H. (Tel Aviv U.) ; Benhammou, Y. (Tel Aviv U.) ; Dror, G. (Tel Aviv Yaffo Academic Coll.) ; Horn, D. (Tel Aviv U.) ; Levinson, L. (Weizmann Inst.) ; Livneh, R. (Tel Aviv U.) |
| Publication | 2004 |
| Imprint | 16 Feb 2004 |
| Number of pages | 5 p, 5 |
| In: | Nucl. Instrum. Methods Phys. Res., A 534 (2004) 222-227 |
| In: | 9th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Tsukuba, Japan, 1 - 5 Dec 2003, pp.222-227 |
| DOI | 10.1016/j.nima.2004.07.091 |
| Subject category | Other Fields of Physics |
| Accelerator/Facility, Experiment | CERN LHC ; ATLAS |
| Abstract | The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedicated electronic muon triggering system. Relatively simple architecture was used to solve a complicated inverse problem. A comparison with a realistic example of the ATLAS first level trigger simulation was in favour of the neural network. A similar architecture trained after the simulation of the electronics first trigger stage showed a further background rejection. |
Corresponding record in: Inspire
Email contact: [email protected]
Record created 2005-02-05, last modified 2023-03-20
PDF