Geometrical Acceptance Analysis for RPC PAC Trigger
- 주제(키워드) RPC PAC Trigger , CMS , RPC , Geometrical Acceptance
- 발행기관 고려대학교 대학원
- 지도교수 박성근
- 발행년도 2011
- 학위수여년월 2011. 2
- 학위구분 석사
- 학과 일반대학원 물리학과
- 원문페이지 85 p
- 실제URI http://www.dcollection.net/handler/korea/000000025226
- 본문언어 영어
- 제출원본 000045641001
초록/요약
The CMS(Compact Muon Solenoid) is one of the four experiments that will analyze the collision results of the protons accelerated by the Large Hardron Collider(LHC) at CERN(Conseil Europen pour la Recherche Nuclaire). In case of the CMS experiment, the trigger system is divided into two stages : The Level-1 Trigger and High Level Trigger. The RPC(Resistive Plate Chamber) PAC(PAttern Comparator) Trigger system, which is a subject of this thesis, is a part of the Level-1 Muon Trigger System. Main task of the PAC Trigger is to identify muons, measures transverse momenta and select the best muon candidates for each proton bunch collision occurring every 25 ns. To calculate the value of PAC Trigger efficiency for triggerable muon, two terms of different efficiencies are needed ; acceptance efficiency and chamber efficiency. Main goal of the works described in this thesis is obtaining the acceptance efficiency of the PAC Trigger in each logical cone. Acceptance efficiency is a convolution of the chambers geometry and PAC logicalsegmentation.
more목차
1 Introduction 1
1.1 Overview of the CMS Detector . . . . . . . . . . . . . . . . . 1
1.2 Subdetectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Trigger and data acquisition . . . . . . . . . . . . . . . . . . . 5
1.3.1 Level-1 Trigger . . . . . . . . . . . . . . . . . . . . . . 6
2 Level-1 RPC PAC Muon Trigger System 10
2.1 Tasks of RPC PAC trigger system . . . . . . . . . . . . . . . . 10
2.2 RPC chambers for the CMS detector . . . . . . . . . . . . . . 11
2.3 Front-End Board . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Chambers segmentation, geometry and naming convention . . 13
2.5 Algorithm of PAttern Comparator Trigger(PACT) . . . . . . . 13
2.6 PAC Trigger logical segmentation . . . . . . . . . . . . . . . . 14
2.6.1 Strips R-ϕ segmentation . . . . . . . . . . . . . . . . . 16
2.6.2 Strips ? segmentation . . . . . . . . . . . . . . . . . . . 18
2.7 Ghost Busting and Sorting . . . . . . . . . . . . . . . . . . . . 18
3 Geometrical Acceptance Analysis 21
3.1 Triggerable muon . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.1 Barrel region . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Endcap region . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Efficiency calculation of RPC PAC trigger . . . . . . . . . . . 22
3.3 Obtaining information from dataset . . . . . . . . . . . . . . . 22
3.3.1 Binning for the histograms . . . . . . . . . . . . . . . . 23
3.4 Acceptance Efficiency about 40M dataset . . . . . . . . . . . . 23
3.4.1 Firedplane information : Bitset Operator . . . . . . . . 25
3.4.2 Muon selection and constraints . . . . . . . . . . . . . 27
3.4.3 Probability plots : eta-pT and eta-phi . . . . . . . . . . 29
3.5 Verification of the Geometry and PAC Trigger Algorithm . . . 29
3.5.1 Probabilities about the number of firedplanes in each tower . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.5.2 PAC Trigger Algorithm verification . . . . . . . . . . . 29
4 Conclusion and Discussion 36
5 Analysis Code 37
5.1 RPCTrigger.cc . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2 RPCTrigger.h . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.3 probability.c . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55