Using a neural network approach for muon reconstruction and triggering

E. Etzion; H. Abramowicz; Y. Benhammou; G. Dror; D. Horn; L. Levinson; R. Livneh

doi:10.1016/j.nima.2004.07.091

Using a neural network approach for muon reconstruction and triggering

E. Etzion, H. Abramowicz, Y. Benhammou, G. Dror, D. Horn, L. Levinson, R. Livneh

School of Physics and Astronomy

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	222-227
Number of pages	6
Journal	Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Volume	534
Issue number	1-2
DOIs	https://doi.org/10.1016/j.nima.2004.07.091
State	Published - 21 Nov 2004
Event	Proceedings of the IXth International Workshop - Tsukuba, Japan Duration: 1 Dec 2003 → 5 Dec 2003

Keywords

HEP
Neural network
Trigger

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10.1016/j.nima.2004.07.091

Cite this

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title = "Using a neural network approach for muon reconstruction and triggering",

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.",

keywords = "HEP, Neural network, Trigger",

author = "E. Etzion and H. Abramowicz and Y. Benhammou and G. Dror and D. Horn and L. Levinson and R. Livneh",

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doi = "10.1016/j.nima.2004.07.091",

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note = "null ; Conference date: 01-12-2003 Through 05-12-2003",

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Using a neural network approach for muon reconstruction and triggering. / Etzion, E.; Abramowicz, H.; Benhammou, Y. et al.

In: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 534, No. 1-2, 21.11.2004, p. 222-227.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

T1 - Using a neural network approach for muon reconstruction and triggering

AU - Etzion, E.

AU - Abramowicz, H.

AU - Benhammou, Y.

AU - Dror, G.

AU - Horn, D.

AU - Levinson, L.

AU - Livneh, R.

PY - 2004/11/21

Y1 - 2004/11/21

N2 - 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.

AB - 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.

KW - HEP

KW - Neural network

KW - Trigger

UR - http://www.scopus.com/inward/record.url?scp=8344263886&partnerID=8YFLogxK

U2 - 10.1016/j.nima.2004.07.091

DO - 10.1016/j.nima.2004.07.091

M3 - מאמר מכנס

AN - SCOPUS:8344263886

VL - 534

SP - 222

EP - 227

JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

SN - 0168-9002

IS - 1-2

Y2 - 1 December 2003 through 5 December 2003

ER -

Using a neural network approach for muon reconstruction and triggering

Abstract

Keywords

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