Klaus Dehmelt EIC Detector R&D Weekly Meeting November 28, 2011 GEM SIMULATION FRAMEWORK

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1 Klaus Dehmelt EIC Detector R&D Weekly Meeting November 28, 2011 GEM SIMULATION FRAMEWORK

2 Overview GEM Simulation Framework in the context of Simulation Studies for a High Resolution Time Projection Chamber at the International Linear Collider Dissertation by Astrid Münnich in 2007 (then RWTH Aachen) 2

3 Motivation GEANT4 allows to model detector geometry and simulate energy deposit in different materials Difficulties: No detailed detector response description which takes into account o transportation of produced charge to readout devices o effects of readout electronics Such processes will change measured position of energy deposit relative to point of origin. Need detailed simulation studies, to be performed for each subdetector. 3

4 The Framework Standalone simulation framework Each electron produced by primary ionization transferred through gas volume and amplified by GEM Output format identical to raw data from real TPC prototype Including readout electronics Optional: ion back-drift Ultimate goal: incorporate parameterization into full detector simulation 4

5 The Framework This framework: Simple Fast Independent of other packages Based on TPC properties, can be modified to other configurations Modular structure: TPCIonization TPCDrift TPCPads TPCElectronics Each module produces its output data file no need to repeat simulation steps 5

6 Module 1 TPCIonization: Input: Information of incident particle, as energy, momentum, charge Primary ionization occurs in clusters, i.e. several e - are produced in close proximity of each other, followed by distance w/o ionization Each step produces cluster of primary e - along trajectory, including propagation and multiple scattering of δ-electrons 6

7 Module 2 TPCDrift: Uses known gas properties like drift velocity, longitudinal and transverse diffusion from parameterizations Calculates coordinate of primary electron after drifting through the gas volume 7

8 Module 3 TPCPads: charge transfer parameterizations Drift arriving e - transferred through GEM stack Amplified through GEM holes Produced charge cloud mapped onto readout structure 8

9 Module 4 TPCElectronics: Consists of shaper determining time distribution of signal from arriving charge cloud binning charge distribution into counts and time bins of ADC (here for pad readout) 9

10 Data Flow 1 HEED based parameterizations: primary ionization # of clusters for certain track length, # of e - in these clusters HEED is a program that computes in detail the energy loss of fast charged particles in gases, taking d-electrons and optionally multiple scattering of the incoming particle into account. The program can also simulate the absorption of photons through photo-ionization in gaseous detectors. 10

11 Data Flow 1 Argon based HEED based parameterizations: primary ionization # of clusters for certain track length, # of e - in these clusters HEED is a program that computes in detail the energy loss of fast charged particles in gases, taking d-electrons and optionally multiple scattering of the incoming particle into account. The program can also simulate the absorption of photons through photo-ionization in gaseous detectors. 11

12 Data Flow 1 Ionization process forming clusters or δ-electrons Simulation of δ-electrons resolution dependent: Energy and range of first created electron in clusters from HEED Multiple Scattering of δ-electrons by comparison between HEED and TPC simulation based on parameterization: range for δ-electrons properly described 12

13 Data Flow 1 Simulation method Distances between clusters distributed exponentially random number generation based upon this distribution; <#> clusters at given p particle using parameterization Simulation follows particle track, placing e - at position of cluster Helix if B is present, otherwise straight track Propagation of δ-electrons # of e - in cluster randomly chosen from data file representing probability distribution of this quantity Energy-loss of 26 ev for each primary e - produced 13

14 Data Flow 2 MAGBOLTZ based parameterizations of gas properties: Drift velocity Longitudinal and Transverse diffusion Calculation of electron transport parameters using semi-classical Monte Carlo simulation 14

15 Data Flow 2 MAGBOLTZ based parameterizations of gas properties: Drift velocity Longitudinal and Transverse diffusion 15

16 Data Flow 2 Simulation method Calculates drift velocity, longitudinal and transverse diffusion based on E-/B-field Position (x,y) deviation due to drift: Gaussian distributed, s given by D transverse x z drift Position (z) due to drift time and drift velocity: Gaussian distributed, s given by D longitudinal x z drift 16

17 Data Flow 2 Drifted electrons 17

18 Data Flow 3 18

19 Data Flow 3 Gain fluctuations in GEMs Exponential distribution with parameter given by mean gain from charge transfer parameterization Charge broadening due to diffusion in gaps Obtained by measurement of charge width, in agreement with MAGBOLTZ Attachment to gas molecules Parameterized by exponential from MAGBOLTZ 19

20 Data Flow 3 Simulation method Each e - that drifted to first GEM is transferred through structure by means of charge transfer coefficients, obtained by measurements: Collection: binomial Gain: exponential Extraction: binomial Attachment: exponential Secondary e - due to amplification 2D-Gaussian distributed on pad plane, s given by diffusion 20

21 Data Flow 4 Very specific to hardware being used Not possible to produce generic electronics module Implemented module needs to be adapted, in particular for shaping 21

22 Data Flow 4 Gaussian shaping performed on channel by channel basis Input: # of e - in a certain time q and COG in time of this distribution is calculated Gaussian normalized: Area = q Rise-time specifies time needed until max is reached Electronics properties: length of time bin, rise time, and calibration of ADC Simulation of ADC binning: charge in one time bin obtained by integrating Gaussian within limits of each time bin Starting bin for integration by subtracting rise-time from COG of e cloud, upper integration limit selectable If total charge would be collected, upper limit is 3s (99.7% of Gaussian) but shaping cuts off at 1.5s: asymmetric range shifts COG # of e - translated into ADC counts according to given value of ADC dynamic range 22

23 Data Flow 4 Asymmetric range shift of COG Time distribution after amplification, shaping and binning 23

24 Data Format Simulation implemented in C++ Data format and reconstruction for real TPC realized with ROOT Simulation realized in ROOT GUI 24

25 Data Format 25

26 Data Format Basic data classes in simulation Data structure in reconstruction 26

27 Conclusion Simulation models signal creation and detection in TPC Simulation has been verified by TPC-prototype measurements Simulation can be modified to specific needs: Specific detector Electronics Readout board 27