towards a pulseshape simulation / analysis kevin kröninger, mpi für physik gerda collaboration...
TRANSCRIPT
![Page 1: Towards a Pulseshape Simulation / Analysis Kevin Kröninger, MPI für Physik GERDA Collaboration Meeting, DUBNA, 06/27 – 06/29/2005](https://reader036.vdocuments.mx/reader036/viewer/2022062511/55146099550346494e8b58e9/html5/thumbnails/1.jpg)
Towards a Pulseshape Simulation / Analysis
Kevin Kröninger, MPI für Physik
GERDA Collaboration Meeting, DUBNA, 06/27 – 06/29/2005
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Outline
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
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SIMULATION
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Simulation Overview I
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
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Simulation Overview II
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• What happens inside the crystal? • Local energy depositions translate into the creation of electron-hole pairs
with Edep : deposited enery
Eeh : 2.95 eV at 80 K in Ge
• Egap = 0.73 eV at 80 K → ¾ of energy loss to phonons
• Corresponds to approximatly 600,000 e/h-pairs at 2 MeV
• Due to bias voltage electrons and holes drift towards electrodes
(direction depends on charge and detector type)
• Charge carriers induce mirror charges at the electrodes
while drifting
<N> = Edep / Eeh
→ SIGNAL
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Drifting Field / Bias Voltage I
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• In order to move charge carriers an electric field is needed
• Calculate field numerically: • 3-D grid with spatial resolution of 0.5 mm
• Define Dirichlet boundary conditions (voltage, ground)
→ depend on geometry (true coxial? non-true coxial? etc.)
• So far: no depletion regions, zero charge density inside crystal,
no trapping
• Solve Poisson equation ∆φ = 0 inside crystal using a Gauss-Seidel
method with simultaneous overrelaxiation
• Need approximatly 1000 iterations to get stable field
• Electric field calculated as gradient of potential
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Drifting Field / Bias Voltage II
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
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Drifting Field / Bias Voltage III
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Example: non-true coaxial n-type detector
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Drifting Process
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
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Mirror Charges – Ramo‘s Theorem
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Ramo‘s Theorem: • Induced charge Q on electrode by point-like charge q is given by
• Calculation of weighting field:• Set all space charges to zero potential
• Set electrode under investigation to unit potential
• Ground all other electrodes
• Solve Poisson equation for this setup (use numerical method explained)
Q = - q · φ0(x)Q : induced charge
q : moving pointlike charge
φ0 : weighting potential
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Weighting Fields I
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
x
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Weighting Fields II
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Example: true coaxial detector with 6 φ- and 3 z-segments
(Slices in φ showing ρ-z plane)
φ = 0° φ = 90°
φ = 180° φ = 270°
yx
z
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Preamp / DAQ
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Drift and mirror charges yield charge as function of time
• Preamp decreases accumulated charge exponentially,
fold in gaussian transfer function with 35 ns width
• DAQ samples with 75 MHz → time window 13.3 ns
• Example:
(signal after drift, preamp and DAQ)(signal after drift)
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Setups / Geometries / Eventdisplays I
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Full simulation of non-true coaxial detector
electrode
electrode
core
core
Cha
rge
Time
TimeC
harg
e
Cur
rent
Cur
rent
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Setups / Geometries / Eventdisplays II
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
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Setups / Geometries / Eventdisplays III
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Full simulation of true-coxial 18-fold segmented detector
Time
Cha
rge core
electrodes
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Analysis Approach
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Pulseshape Analysis in MC: Spatial Resolution I
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Is it possible to obtain spatial information of hits from
pulseshapes? In principal YES! • Risetime of signal (10% - 90% amplitude) is correlated with radius
of hit due to different drift times of electrons and holes
• Relative amplitude of neighboring segments is correlated to angle
• Events with more than one hit in detector give ambiguities
• Studied in Monte Carlo with 2-D 6-fold segment detector,
no DAQ, no sampling
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Pulseshape Analysis in MC: Spatial Resolution II
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Spatial information of radius and angle
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Pulseshape Analysis: SSE/MSE Discrimination I
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Do 0νββ signals differ from background signals? • Background mainly photons that Compton-scatter: multiple hits
in crystal → Multisite events (MSE)• Signal due to electrons with small mean free path: localized energy
deposition → Singlesite events (SSE)• Expect two ‘shoulders‘ at most from SSE and more from MSE
• Count number of shoulders in current• Apply mexican hat filter with integral 0 and different widths (IGEX method)
• Count number of shoulders: ≤2 : SSE
>2 : MSE
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Pulseshape Analysis: SSE/MSE Discrimination II
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
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Pulseshape Analysis: SSE/MSE Discrimination III
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Fraction of SSE and MSE for different filter widths
Separation of SSE/MSE in principle possible, combine with information from neighboring segments
Identified as SSE Identified as MSE
SSEMSE
Filter width Filter widthF
ract
ion
of E
vent
s
Fra
ctio
n of
Eve
nts
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Data to Monte Carlo Comparison
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Data to Monte Carlo Comparison I
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Data from teststand (see X. Liu)
• Later on used for SSE selection
Source
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Data to Monte Carlo Comparison II
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Teststand data vs. Monte Carlo
Energy [MeV]Energy [MeV]
Energy [MeV]
• General agreement • No finetuning yet• Next: pulseshapes without any additional selection criteria
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Data to Monte Carlo Comparison III
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Comparison of pulseshapesC
harg
e
Cha
rge
Data Monte Carlo
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Data to Monte Carlo Comparison IV
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Comparison of pulseshapesC
urre
nt
Cur
rent
Data Monte Carlo
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Data to Monte Carlo Comparison V
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Comparison of pulseshapes
Cur
rent
Cha
rge
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Data to Monte Carlo Comparison VI
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Comparison of pulseshapes
Charge amplitude Current amplitude
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Data to Monte Carlo Comparison VII
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• Comparison of pulseshapes
Risetime [ns]
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Conclusion
Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
• First approach towards a simulation of pulseshapes
• Different geometries / fields available
• Package available and linked to MaGe
• Pulseshape analysis to further reduce background via
SSE/MSE identification is feasible → need sampling rate
as large as possible (1 GHz ↔ 1 ns possible?)
• Data to Monte Carlo comparison using teststand data
yields coarse agreement → finetune parameters of simulation