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Tracker Software MECO/Mu2e Experience Yury Kolomensky UC Berkeley/LBNL January 24, 2008
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01/24/2009YGK, Tracker Software History Mu2e: benefit from years of detailed studies for MECO Also inherited the code base, which I will briefly review here Present effort: new, unified simulation/reconstruction platform, modern software tools and algorithms Rob Kutschke et al Based on (lighter) CMS Framework Would make it easier to adapt new pattern recognition, track fitting algorithms, use existing HEP code base
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01/24/2009YGK, Tracker Software MECO Software Overview Two simulations packages, based on Geant3 and Geant4 GMC (G3): most developed, used by Mu2e Detailed geometry, field maps Beamline simulations, signal and backgrounds Tightly integrated pattern recognition/tracking for L-tracker Standalone G3 for T-tracker simulations Step-wise approach: simulate signals and backgrounds, read into separate (C++-based) reco code Rudimentary G4/C++ Some work by Vladimir Tumakov on porting MECO geometry Some work at Irvine (Paul Huwe) on porting L-tracker PatRec to C++ No hardware response (hit digitization, efficiency) Resolution smearing
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01/24/2009YGK, Tracker Software Longitudinal Tracker Geometry: Octagon with Eight Vanes Straws:2.9 m length 5mm diameter, 25 mm thickness – 2800 total Three layers per plane, outer two resistive, inner conducting Pads:30 cm 5mm wide cathode strips affixed to outer straws 18500 total pads Position Resolution: 0.2 mm (r,f) 1.5 mm (z) Readout Channels: 20k each of ADC & TDC Main advantage: pattern recognition, intrinsic momentum resolution (180 o spectrometer)
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01/24/2009YGK, Tracker Software Transverse Tracker Geometry: 18 Modules of three planes each, 30° rotation between successive planes Straws: 70 – 130 cm length 5mm diameter, 15 or 25 mm thickness 12960 total straws One layer per plane All straws conducting Position Resolution: 0.2 mm (x,y) Main advantage: mechanical Biggest issue: pattern recognition
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01/24/2009YGK, Tracker Software Tracker Simulations Simulations, reproduced by Mu2e Detailed geometries, including straw walls, wires, gas manifolds Noise hits, at nominal and double rate, including highly- ionizing protons Energy loss and straggling in the stopping target L-tracker: Gaussian resolution model and average hit efficiency, “salt and pepper” backgrounds superimposed T-tracker: more sophisticated PatRec, including L-R ambiguities What wasn’t specifically done Hit digitization Limited scope: upshifting DIO electrons due to PatRec errors
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01/24/2009YGK, Tracker Software L-Tracker Reconstruction Helical pattern recognition based on space-points Likelihood-based fitter Reasonably robust against background hits Intrinsic momentum resolution ~180 keV Efficiency ~19%
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01/24/2009YGK, Tracker Software T-Tracker: Deterministic Annealing Filter Left and Right points are projected on straw center layer using fitted helix Calculate point prob Gauss(Xi, Mean, Vn) Kalman filter runs on all layers taking weighted mean according to point prob If combined hit prob < Threshold hit is rejected Combinatorial Collapse Filter (CCF) treats Left-Right problem keeping a set of best choices CPU-intensive
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01/24/2009YGK, Tracker Software T-Tracker Resolution Nominal background and 25 µm Delta-ray and straw inefficiency Average straw rate 550 kHz Kalman filter reconstruction Intrinsic Resolution = 190 keV Average efficiency ~19% P rec -P gen
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01/24/2009YGK, Tracker Software Mu2e Software Goal: integrated simulation/reconstruction framework using modern tools and practices Ongoing work by CD @ FNAL (R.Kutschke et al.) “CMS-lite” implementation Provide overall distribution/build/runtime infrastructure Geometry, constants management “Grid-enabled” to facilitate parallel farm processing Options for “one-shot” sim/reco process, or step-wise
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01/24/2009YGK, Tracker Software Infrastructure Deliverables 1)Framework proper 2)Services 3)Configuration 4)Geometry shell 5)Simulation shell 6)IO system 7)Initial documentation 8)Conditions 9)Full featured build and release mgt 10)Grid features 11)Full documentation Rob Kutschke ~ Immediate future Later
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01/24/2009YGK, Tracker Software Opportunities for Collaboration Common infrastructure, tools ? Take advantage of FNAL CPU farm Facilitate comparisons between options Cooperation on algorithms and simulations Backgrounds Pattern recognition/fitting Even if the details of geometry are different, concepts are the same
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