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https://doi.org/10.1109/asap.2...
Article . 2012 . Peer-reviewed
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FPGA Based Particle Identification in High Energy Physics Experiments

Authors: H. Fatih Ugurdag; Ali Basaran; Taylan Akdogan; V. Ugur Güney; Sezer Gören 0001;

FPGA Based Particle Identification in High Energy Physics Experiments

Abstract

High energy physics experiments require on-the-fly processing of signals from many particle detectors. Such signals contain a high and fluctuating rate of pulses. Pulse shape hints particle type, and the amplitude relates to energy of the particle, while pulse occurrence times are needed for event reconstruction. Traditionally, these parameters have been extracted with the help of complete racks of dedicated electronics. Our FPGA design on a general-purpose DAQ card does real-time pulse detection and high-precision curve fitting. It greatly shrinks required equipment in terms of form factor, cost, power usage, and setup time. Unlike traditional systems, we can handle bursts of back-to-back pulses, pulses as narrow as 6 ns and at rates over 1M pulses per second. We have a novel scalable architecture that combines pipelining and parallelism. Moreover, the parallel part of the architecture uses loop pipelining in each of its interleaved identical parallel processors (IIPPs). An IIPP is a specialized CPU, which executes nested loops, with number of iterations that varies from pulse to pulse. IIPPs are fed data from a FIFO by a priority encoder based dispatcher. Number of IIPPs can be calculated to meet any pulse rate and average pulse width. The architecture is flexible enough to work with a variety of curve fitting algorithms.

Country
Turkey
Keywords

Signal processing, Curve fitting, Parallel processing, Field programmable gate arrays, Particle detectors

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green