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Title: Low-Level Track Finding and Completion using Random Fields.


Abstract not provided.

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Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the Electronic Imaging held February 14-18, 2016 in San Francisco, CA.
Country of Publication:
United States

Citation Formats

Quach, Tu-Thach, Malinas, Rebecca, and Koch, Mark William. Low-Level Track Finding and Completion using Random Fields.. United States: N. p., 2015. Web.
Quach, Tu-Thach, Malinas, Rebecca, & Koch, Mark William. Low-Level Track Finding and Completion using Random Fields.. United States.
Quach, Tu-Thach, Malinas, Rebecca, and Koch, Mark William. 2015. "Low-Level Track Finding and Completion using Random Fields.". United States. doi:.
title = {Low-Level Track Finding and Completion using Random Fields.},
author = {Quach, Tu-Thach and Malinas, Rebecca and Koch, Mark William},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month =

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  • Abstract not provided.
  • The track segment finding subsystem of the LEVEL 2 trigger in the CLAS detector has been designed and prototyped. Track segments will be found in the 35,076 wires of the drift chambers using a massively parallel array of 768 Xilinx XC-4005 FPGA's. These FPGA's are located on daughter cards attached to the front-end boards distributed around the detector. Each chip is responsible for finding tracks passing through a 4 x 6 slice of an axial superlayer, and reports two segment found bits, one for each pair of cells. The algorithm used finds segments even when one or two layers ormore » cells along the track is missing (this number is programmable), while being highly resistant to false segments arising from noise hits. Adjacent chips share data to find tracks crossing cell and board boundaries. For maximum speed, fully combinatorial logic is used inside each chip, with the result that all segments in the detector are found within 150 ns. Segment collection boards gather track segments from each axial superlayer and pass them via a high speed link to the segment linking subsystem in an additional 400 ns for typical events. The Xilinx chips are ram-based and therefore reprogrammable, allowing for future upgrades and algorithm enhancements.« less
  • No abstract prepared.
  • The Enable Machine is a systolic 2nd level trigger processor for the transition radiation detector (TRD) of ATLAS/LHC. It is developed within the EAST/RD-11 collaboration at CERN. The task of the processor is to find electron tracks and to reject pion tracks according to the EAST benchmark algorithm in less than 10[mu]s. Track are identified by template matching in a ([psi],z) region of interest (RoI) selected by a 1st level trigger. In the ([psi],z) plane tracks of constant curvature are straight lines. The relevant lines form mask templates. Track identification is done by histogramming the coincidences of the templates andmore » the RoI data for each possible track. The Enable Machine is an array processor that handles tracks of the same slope in parallel, and tracks of different slope in a pipeline. It is composed of two units, the Enable histogrammer unit and the Enable z/[psi]-board. The interface daughter board is equipped with a HIPPI-interface developed at JINR/-Dubna, and Xilinx 'corner turning' data converter chips. Enable uses programmable gate arrays (XILINX) for histogramming and synchronous SRAMs for pattern storage. With a clock rate of 40 MHz the trigger decision time is 6.5 [mu]s and the latency 7.0 [mu]s. The Enable machine is scalable in the RoI size as well as in the number of tracks processed. It can be adapted to different recognition tasks and detector setups. The prototype of the Enable Machine has been tested in a beam time of the RD6 collaboration at CERN in October 1993.« less
  • We describe the applications of a track segment recognition scheme called the Tiny Triplet Finder (TTF) that involves the grouping of three hits satisfying a constraint forming of a track segment. The TTF was originally developed solving straight track segment finding problem, however, it is also suitable in many curved track segment finding problems. The examples discussed in this document are among popular detector layouts in high-energy/nuclear physics experiments. Although it is not practical to find a universal recipe for arbitrary detector layouts, the method of the TTF application is illustrated via the discussion of the examples. Generally speaking, whenevermore » the data item to be found in a pattern recognition problem contains two free parameters, and if the constraint connecting the measurements and the two free parameters has an approximate shift invariant property, the Tiny Triplet Finder can be used.« less