dorsal/arxiv
View SchemaUsing a neural network approach for muon reconstruction and triggering
| Authors | E. Etzion, H. Abramowicz, Y. Benhammou, D. Horn, L. Levinson, R. Livneh |
|---|---|
| Categories | |
| ArXiv ID | physics/0402070 |
| URL | https://arxiv.org/abs/physics/0402070 |
| DOI | 10.1016/j.nima.2004.07.091 |
| Journal | Nucl.Instrum.Meth. A534 (2004) 222-227 |
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.
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"abstract": "The extremely high rate of events that will be produced in the future Large\nHadron Collider requires the triggering mechanism to take precise decisions in\na few nano-seconds. We present a study which used an artificial neural network\ntriggering algorithm and compared it to the performance of a dedicated\nelectronic muon triggering system. Relatively simple architecture was used to\nsolve a complicated inverse problem. A comparison with a realistic example of\nthe ATLAS first level trigger simulation was in favour of the neural network. A\nsimilar architecture trained after the simulation of the electronics first\ntrigger stage showed a further background rejection.",
"arxiv_id": "physics/0402070",
"authors": [
"E. Etzion",
"H. Abramowicz",
"Y. Benhammou",
"D. Horn",
"L. Levinson",
"R. Livneh"
],
"categories": [
"physics.data-an",
"cond-mat.dis-nn"
],
"doi": "10.1016/j.nima.2004.07.091",
"journal_ref": "Nucl.Instrum.Meth. A534 (2004) 222-227",
"title": "Using a neural network approach for muon reconstruction and triggering",
"url": "https://arxiv.org/abs/physics/0402070"
},
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