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Towards a Pulseshape Simulation / Analysis. Kevin Kr öninger, MPI für Physik GERDA Collaboration Meeting, DUBNA, 06/27 – 06/29/2005. Outline. Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005. SIMULATION. Simulation Overview I.
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Towards a Pulseshape Simulation / Analysis Kevin Kröninger, MPI für Physik GERDA Collaboration Meeting, DUBNA, 06/27 – 06/29/2005
Outline Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Simulation Overview I Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Simulation Overview II • 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 Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Drifting Field / Bias Voltage I • 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 Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Drifting Field / Bias Voltage II Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Drifting Field / Bias Voltage III • Example: non-true coaxial n-type detector Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Drifting Process Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Mirror Charges – Ramo‘s Theorem • 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 : induced charge q : moving pointlike charge φ0 : weighting potential Q = - q · φ0(x) Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Weighting Fields I x Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Weighting Fields II • Example: true coaxial detector with 6 φ- and 3 z-segments φ = 0° φ = 90° φ = 180° φ = 270° z (Slices in φ showing ρ-z plane) y x Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Preamp / DAQ • 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) (signal after drift, preamp and DAQ) Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Setups / Geometries / Eventdisplays I • Full simulation of non-true coaxialdetector Charge Charge electrode core Time Current Current electrode core Time Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Setups / Geometries / Eventdisplays II Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Setups / Geometries / Eventdisplays III • Full simulation of true-coxial 18-fold segmented detector electrodes core Charge Time Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Pulseshape Analysis in MC: Spatial Resolution I • 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 Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Pulseshape Analysis in MC: Spatial Resolution II • Spatial information of radius and angle Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Pulseshape Analysis: SSE/MSE Discrimination I • 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 Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Pulseshape Analysis: SSE/MSE Discrimination II Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Pulseshape Analysis: SSE/MSE Discrimination III • Fraction of SSE and MSE for different filter widths Identified as SSE Identified as MSE Fraction of Events Fraction of Events Filter width Filter width Separation of SSE/MSE in principle possible, combine with information from neighboring segments SSE MSE Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Data to Monte Carlo Comparison I • Data from teststand (see X. Liu) • Later on used for SSE selection Work in progress Source Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Data to Monte Carlo Comparison II • Teststand data vs. Monte Carlo Work in progress Energy [MeV] Energy [MeV] • General agreement • No finetuning yet • Next: pulseshapes without • any additional selection criteria Energy [MeV] Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Data to Monte Carlo Comparison III • Comparison of pulseshapes Work in progress Data Monte Carlo Charge Charge Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Data to Monte Carlo Comparison IV • Comparison of pulseshapes Work in progress Data Monte Carlo Current Current Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Data to Monte Carlo Comparison V • Comparison of pulseshapes Work in progress Charge Current Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Data to Monte Carlo Comparison VI • Comparison of pulseshapes Work in progress Current amplitude Charge amplitude Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Data to Monte Carlo Comparison VII • Comparison of pulseshapes Work in progress Risetime [ns] Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005
Conclusion • 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 Kevin Kröninger, MPI München GERDA Collaboration Meeting DUBNA, 06/27 – 06/29/2005