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Wing Attack - Plan R or How I learned to stop worrying and love the Monte Carlo 06/06/06.

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Presentation on theme: "Wing Attack - Plan R or How I learned to stop worrying and love the Monte Carlo 06/06/06."— Presentation transcript:

1 Wing Attack - Plan R or How I learned to stop worrying and love the Monte Carlo 06/06/06

2 MC development items New IR geometryCB Revision of constants in GEANFICB Generators: h  pp ee, ee  wp 0, K S  pp ee CG RV ADS Nuclear interactions/regenerationCB Adjustments to EmC energy responsePG Adjustments to EmC time resolutionCG Simulation of EmC cluster efficiencyMP TS SQZ (compression) fixMM CB Fix CELE/CSPS/cluster banks/structuresMM MP Data-quality parameters (√ s, etc)GV Trigger params.: quality, DC thresh, DISH mapsMP BS lsb background insertionMM MP Correlated noise for charged kaonsEDL PDS dE/dx simulationVP Check ECL code: ee  wp 0 SG Split large (> 2GB) DSTs (MCDSTs)MM

3 To-do list 1.Finish copying bad-wire maps from test to production DB2 tables 2.Insert DQ parameters (root s) in HepDB for Clean DB of all test runs 4.Tag and build executable 5.Carefully examine reconstruction UIC file 6.Confirm presence of SIMKBCK parameters in DB 7.Reconfigure kmc queue: 214 B80

4 EmC and DC hits from random events inserted in proportion to L inst EmC hits require careful MC/data timescale alignment Special treatment for “noise” hits on EmC (missing t A or t B ) Background from random triggers –data MC Cluster time distributions ( t 0 from K S secondaries) K S  p 0 p 0, K L crash Overall accidental level MC/data = 1.05 K S  p + p -, K L crash Overall accidental level MC/data = 1.19 Track reconstruction efficiency higher in MC when t 0 incorrect t cl (ns) t cl (ns) ~3% of clusters in tail ( t < 0)


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