Design and Performance of the ALICE Muon Trigger System

2006 ◽  
Vol 158 ◽  
pp. 21-24 ◽  
Author(s):  
R. Arnaldi ◽  
A. Baldit ◽  
V. Barret ◽  
N. Bastid ◽  
G. Blanchard ◽  
...  
Keyword(s):  
Trigger System ◽  
Muon Trigger ◽  
And Performance

scholarly journals FAST TRACK TRIGGERING FOR THE CDF II DETECTOR

2001 ◽  
Vol 16 (supp01c) ◽  
pp. 1172-1174
Author(s):  
◽  
KENNETH BLOOM
Keyword(s):  
Fast Track ◽  
Drift Chamber ◽  
Programmable Logic ◽  
Performance Tests ◽  
High Momentum ◽  
Trigger System ◽  
Tevatron Collider ◽  
Programmable Logic Devices ◽  
Cdf Ii ◽  
And Performance

In the upcoming Fermilab Tevatron collider run, [Formula: see text] collisions will occur at 132 ns intervals, and the CDF II trigger system requires that information about drift chamber tracks be provided within 2.2 μs of every collision, so that tracking information can be used in conjunction with data from other detector components to trigger on physics objects with little background. We have developed a fast online track processor for locating high-momentum tracks in the chamber. The design is highly parallel, and is implemented in programmable logic devices. We describe the design of the system and performance tests.

scholarly journals Upgrade of the CMS muon trigger system in the barrel region

2017 ◽  
Author(s):  
Dinyar Rabady ◽  
Carlo Battilana ◽  
Roberto Carlin ◽  
Giuseppe Codispoti ◽  
Marco Dallavalle ◽  
...  
Keyword(s):  
Trigger System ◽  
Muon Trigger

scholarly journals Implementation and performance of the third level muon trigger of the ATLAS experiment at LHC

2006 ◽  
Vol 53 (2) ◽  
pp. 500-505
Author(s):  
S. Armstrong ◽  
K.A. Assamagan ◽  
J.T.M. Baines ◽  
C.P. Bee ◽  
M. Bellomo ◽  
...  
Keyword(s):  
Atlas Experiment ◽  
The Third ◽  
Muon Trigger ◽  
And Performance

Commissioning and performance of a fast level-2 trigger system at VERITAS

2012 ◽  
Author(s):  
John T. Anderson ◽  
Karen Byrum ◽  
Gary Drake ◽  
Frank Krennrich ◽  
Andrew Kreps ◽  
...  
Keyword(s):  
Trigger System ◽  
And Performance ◽  
Level 2

scholarly journals Development of FPGA-based neural network regression models for the ATLAS Phase-II barrel muon trigger upgrade

2021 ◽  
Vol 251 ◽  
pp. 04031
Author(s):  
Rustem Ospanov ◽  
Changqing Feng ◽  
Wenhao Dong ◽  
Wenhao Feng ◽  
Shining Yang
Keyword(s):  
Neural Network ◽  
Phase Ii ◽  
Regression Models ◽  
Trigger System ◽  
Muon Trigger ◽  
Level Trigger ◽  
Lhc Physics ◽  
Effective Selection ◽  
Potential Applications ◽  
Selection Of

Effective selection of muon candidates is the cornerstone of the LHC physics programme. The ATLAS experiment uses a two-level trigger system for real-time selection of interesting collision events. The first-level hardware trigger system uses the Resistive Plate Chamber detector (RPC) for selecting muon candidates in the central (barrel) region of the detector. With the planned upgrades, the entirely new FPGA-based muon trigger system will be installed in 2025-2026. In this paper, neural network regression models are studied for potential applications in the new RPC trigger system. A simple simulation model of the current detector is developed for training and testing neural network regression models. Effects from additional cluster hits and noise hits are evaluated. Efficiency of selecting muon candidates is estimated as a function of the transverse muon momentum. Several models are evaluated and their performance is compared to that of the current detector, showing promising potential to improve on current algorithms for the ATLAS Phase-II barrel muon trigger upgrade.

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