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Analyse von Bolometersignalen der EDELWEISS Dark Matter Suche

Analyse von Bolometersignalen der EDELWEISS Dark Matter Suche. Scatt. WIMP. WIMP. Count rate: < 10 -2 evt /kg/ day !. Recoil nucleus  E R ~10 keV. Direct detection of WIMPs (weak interacting massive particles). EDELWEISS-II Infrastructure.

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Analyse von Bolometersignalen der EDELWEISS Dark Matter Suche

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  1. Analyse von Bolometersignalen der EDELWEISS Dark Matter Suche

  2. Scatt. WIMP WIMP Count rate: < 10-2evt/kg/day! Recoil nucleus  ER~10 keV Directdetectionof WIMPs (weak interacting massive particles)

  3. EDELWEISS-II Infrastructure • Place:Laboratoire Souterrain de Modane • cosmicmuonflux: 4 • Cryogenicinstallation (18mK): • Reversedgeometrycryostat • Can hostupto 40kg ofdetectors • Shieldings: • Clean room + deradonizedair • Activemuonveto (>98% coverage) • 50 cm PE shield • 20 cm leadshield • Other items: • Remotelycontrolledsourcesforcalibrations + regenerations • AmBesourcesforneutroncalibrations • Radon detector down tofew • neutrondetector (thermal neutronmonitoring) • Liquid scintillatorneutroncounter (studyofinducedneutrons)

  4. Background rejectionwith EDELWEISS-I Detectors • Simultaneous measurementofheatandionization • Event byeventbackgroundrejectionbyratio • Forelectronrecoil: • Fornuclearrecoil:

  5. Background rejectionwith EDELWEISS-I Detectors EDELWEISS II 93.5 kgd (2008) Limitations: Surfaceeventswithincompletechargecollection • Simultaneous measurementofheatandionization • backgroundrejectionbyratio • Forelectronrecoil: • Fornuclearrecoil:

  6. ID detectors: surfaceeventrejectionwithinterleavedelectrodes A: +4 V B: -1.5V 50 % fid mass C: -4 V D: +1.5V InterDigitizedelectrodes (ID): • Modify E-fieldwithbiasestobe: • horizontal nearsurface • vertical in thebulk • A and C signalsas ‚collection‘ electrodes • B and D signalsasvetoagainstsurfaceevents • Cuts on vetoandguardelectrodesdefinethefiducialzone

  7. ID detectors: surfaceeventrejectionwithinterleavedelectrodes A: +4 V B: -1.5V 133Ba calibration data: fiducial only evts (no signal observed on veto electrodes) 1.82 x 105 events with 20 < E < 200 keV 6 events (under invest.)  rejection factor of 3 x 10-5 / g 50 % fid mass C: -4 V D: +1.5V • Modify E-fieldwithbiasestobe: • horizontal nearsurface • vertical in thebulk • A and C signalsas ‚collection‘ electrodes • B and D signalsasvetoagainstsurfaceevents • Cuts on vetoandguardelectrodesdefinethefiducialzone

  8. FID800 (Full InterDigitized) detectors >80% fid mass

  9. FID800 detectorperformance Noevents in thenuclearrecoil band! >80% fid mass Ge-ID (350000 ) Ge-FID800 (412000 ) Increasedmassandsensitivity: 800g crystal 2 heatsensors pro detector interleavedelectrodes on all surface fiducialvolume640g

  10. Bolometer signals rawionisationtracewithheatchannelcrosstalk after subtractionofpatternandbaseline rawheattrace after baselinesubtraction

  11. Trapezoidal Filter transformsexponentional pulse withknown fall time intotrapezoid rise time and flat top widtharesetby filter parameters second derivative has a characteristicpattern

  12. Usingtrapezoidalfilter peakamplitudeis 15*RMS(noise sample) estimationofamplitudebycalculatingthemeanofthe flat top estimationofpeakpositionbycalculatingthecorrelationofsecond derivative ofthe filter outputwiththecharacteristicpattern

  13. Accuracyoftrapezoidalfilter amplitudeandpeakpositionestimationfor 1795 different noisesamples meanofamplitudeestimationisunbiased Standard deviationofamplitudeestimationis 4.7% peakpositionestimate was alwaysright!

  14. Time Domain Fitting Expectedsignalatinput Measuredsignal: Amplitude Pulse start time Noise For whitenoisewithvariancethebestparameterestimationminimizes in time domain: minimal at:

  15. Optimal Filtering minimal at: Average noise power spectraldensity Ifnoiseis not white, thenthevalues in different time binsarecorrelatedand in time domainis not properlynormalized better: minimizing in frequencydomain, weightingeachfrequencybyitsnoisevariance the bestestimategivesthelargestvaluefor (scanover a rangeofvaluestoestimatethepeak time)

  16. Applying Optimal Filter amplitude peak time

  17. Conclusions & outlook trapezoidal filter: optimal filter: • robust • precise reconstruction of position • amplitude spreading o(5%) for large signals • not optimally filtering the noise • weighting the allowed frequencies depending on the noise • optimal discrimination signal-to-noise in frequency domain • depends on correct model of noise frequency spectrum • modified optimal filter used so far in Edelweiss-2 • full optimal filter under investigation

  18. Conclusions & outlook optimal filter: • weighting the allowed frequencies depending on the noise • optimal discrimination signal-to-noise in frequency domain • depends on correct model of noise frequency spectrum • modified optimal filter used so far in Edelweiss-2 • full optimal filter under investigation Preliminary!

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