Analyse von Bolometersignalen der EDELWEISS Dark Matter Suche

Analyse von Bolometersignalen der EDELWEISS Dark Matter Suche Michael Unrau, Institut für Kernphysik EDELWEISS dark matter search Full Inter. Digitized detector technology Signal amplitude estimation with trapezoidal and optimal filtering

Direct detection of WIMPs (weak interacting massive particles) WIMP Scatt. WIMP Count rate: < 10 -2 evt/kg/day! Recoil nucleus ER ~10 ke. V Ways to go: • • • 2 • low background • powerful background discrimination • background studies

EDELWEISS-II Infrastructure 3

Background rejection with EDELWEISS-I Detectors 4

Background rejection with EDELWEISS-I Detectors EDELWEISS II 93. 5 kgd (2008) : s n o i t a t i Lim ith w s t n e v e ace Surf e g r a h c e t e l incomp collection 5

ID detectors: surface event rejection with interleaved electrodes A: +4 V Inter. Digitized electrodes (ID): B: -1. 5 V 50 % fid mass C: -4 V D: +1. 5 V 6 Modify E-field with biases to be: horizontal near surface vertical in the bulk A and C signals as ‚collection‘ electrodes B and D signals as veto against surface events Cuts on veto and guard electrodes define the fiducial zone

ID detectors: surface event rejection with interleaved electrodes ata: 133 Ba calibration d ts (no fiducial only ev signal eto observed on v electrodes) 50 % fid mass 5 nts with 1. 82 x 10 eve e. V 20 < E < 200 k est. ) r inv 6 events (unde tor of rejection fac -5 3 x 10 7 / A: +4 V B: -1. 5 V C: -4 V D: +1. 5 V Modify E-field with biases to be: horizontal near surface vertical in the bulk A and C signals as ‚collection‘ electrodes B and D signals as veto against surface events Cuts on veto and guard electrodes define the fiducial zone

FID 800 (Full Inter. Digitized) detectors >80% fid mass 8

FID 800 detector performance the s t n e v No e il o c e r r a e l nuc band! >80% fid massin Ge-ID (350000 ) Ge-FID 800 (412000 ) 9

Bolometer signals raw ionisation trace with heat channel crosstalk raw heat trace 10 after subtraction of pattern and baseline after baseline subtraction

Trapezoidal Filter transforms exponentional pulse with known fall time into trapezoid rise time and flat top width are set by filter parameters second derivative has a characteristic pattern 11

Using trapezoidal filter peak amplitude is 15*RMS(noise sample) estimation of amplitude by calculating the mean of the flat top estimation of peak position by calculating the correlation of second derivative of the filter output with the characteristic pattern 12

Accuracy of trapezoidal filter 13

Time Domain Fitting Measured signal: Expected signal at input Amplitude Pulse start time minimal at: 14 Noise

Optimal Filtering minimal at: Average noise power spectral density 15

Applying Optimal Filter amplitude 17 peak time

Conclusions & outlook trapezoidal filter: Ø robust Ø precise reconstruction of position Ø amplitude spreading o(5%) for large signals Ø not optimally filtering the noise optimal filter: Ø weighting the allowed frequencies depending on the noise Ø optimal discrimination signal-to-noise in frequency domain Ø depends on correct model of noise frequency spectrum Ø modified optimal filter used so far in Edelweiss-2 Ø full optimal filter under investigation 18

Conclusions & outlook optimal filter: Ø weighting the allowed frequencies depending on the noise Ø optimal discrimination signal-to-noise in frequency domain Ø depends on correct model of noise frequency spectrum Ø modified optimal filter used so far in Edelweiss-2 Ø full optimal filter under investigation ! ry a n i m i l e r P 19