Optimization of Non-Gaussian Background Rejection in XENON10
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1 Optimization of Non-Gaussian Background Rejection in XENON10? Peter Sorensen Brown University Peter Sorensen DSU 2007 p 1
2 Talk Summary 1. Brief overview of XENON10 installation/detector 2. Trigger Threshold / Analysis Threshold 3. Discrimination of ER v NR: Gaussian & non-gaussian populations 4. Optimization of discrimination Gaussian: corrections for detector non-uniformity (optimize sigma) Non-Gaussian: identification of anomalous pathologies 5. Discussion of candidate events in XENON Live-Day Result LNGS Underground Laboratory Peter Sorensen DSU 2007 p 2
3 XENON10 Installation at LNGS 3100 m.w.e. / 20 cm HDPE / 20 cm Pb Peter Sorensen DSU 2007 p 3
4 XENON10 Detector 15 cm drift (z) defined by SS mesh grids 20 cm defined by Teflon Can (max 13.5 kg LXe) 89 Hamamatsu R8520-AL PMTs (1 square) 48 Top Array 41 Bottom Array Liquid Xe maintained at T=180 K and p=2.2 atm. 12 kv cathode Ed = 0.73 kv/cm (drift) Egas = ~ 9 kv/cm (S2) J. Angle (UFL) Peter Sorensen DSU 2007 p 4
5 Direct Detection Event Rate Peter Sorensen DSU 2007 p 5
6 Typical Background Event at 4.5 kevee * S1: primary scintillation S2: ionization drifted, extracted, amplified in gas region (secondary scintillation) Background Discrimination: (S2/S1)ER > (S2/S1)NR x,y coords. : from S2 Hit-Pattern z coord. : from drift time t between S2 and S1 *(scaled using 3.0 phe/kevee from 122 kev gamma cal.) Peter Sorensen DSU 2007 p 6
7 Trigger Schemes S1 Trigger: 1. (1) n-fold coincidence in 80ns window (2) 80% single photo-electron acceptance (3) 80% light on bottom (trigger) PMTs 2. S2 Trigger: (1) Σ(34 top-center PMTs) (2) integrate with τ = 1 μs (3) threshold discriminator (Final Trigger Solution) 80 phe τ ~27 ns 2.3 kevee Background Event 150 ns 5 phe (!) 3150 phe A. Manzur (Yale) Peter Sorensen DSU 2007 p 7
8 S2 Trigger Threshold: single electron (!) Typical S2 at threshold: ER (2 kevee): 2800 phe (~100 e-) NR(4.5 kevr): 1100 phe (~40 e-) Smallest NR S2 at 4.5 kevr threshold: 300 phe (~12 e-) Non-primary S2 => found in event waveform, not necessarily correlated with event energy deposition Rate ~ 0.17 Hz Peter Sorensen DSU 2007 p 8
9 Analysis Threshold >> Trigger Threshold S1 Detection Efficiency Monte Carlo Simulation S1 [phe] Counts AmBe Calibration Data S2 [phe] S1-lookback efficiency (n 2) from MC : ~ 99% at 4.5 kevr Effective S2 analysis threshold ~300 phe (12 e-) set by S1 coincidence req. (n 2) and NR -3σ contour Peter Sorensen DSU 2007 p 9
10 Calibration Data Band Centroid / -3σ Peter Sorensen DSU 2007 p 10
11 For DM-Search Analysis because Ionization yield rises at lower energy Make a simple coordinate transform (based on μer) to remove energy-dependence DM-Search acceptance box in discrete bins Peter Sorensen DSU 2007 p 11
12 Discrimination Parameter appears Gaussian kevr μnr μnr Band Separation NR v ER Band Separation Improves at low E... but still need to minimize sigma to maximize discrimination 50% acceptance for Nuclear Recoils 99.9% discrimination > 99.5% discrimination σer (discrimination calculated from Gaussian fit params.) Example Region for Histogram σnr Peter Sorensen DSU 2007 p 12
![S2 Variation: 40 kev line J. Kwong (Princeton) x10 4 [phe] -100 0 100 X [mm] 1.6 2.4 3.](/docs-images/83/87232528/images/13-1.jpg "2-50 0 50 Top PMT Array Relative Sensitivity -100 0 100 X [mm] S1 Variation: 164 kev line K.")
13 Corrections to data improve sigma ±20% variation in S2 across x-y (± 25% in Full Volume) ± 10-15% variation in Relative Sensitivity ± 22% variation in S1 across z (± 28% in Full Volume) S2 Variation: 40 kev line J. Kwong (Princeton) x10 4 [phe] X [mm] Top PMT Array Relative Sensitivity X [mm] S1 Variation: 164 kev line K. Ni (Yale) < 2% variation in S2 with z (electron lifetime > 2ms) R.S. map obtained by comparing S1 signal in each nearest-neighbor pair, then diagonalizing the matrix Fiducial Volume Peter Sorensen DSU 2007 p 13
![4 7.5 10.2 13.5 ~600 mdru!0.8 4 5 6 7 8 9 10 11 radius / height of F.V. [cm]!1 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 kev r [0.98 phe/kev] R.](/docs-images/83/87232528/images/14-1.jpg "Gomez (Rice) Most (spurious) events in box are removed by Fiducial Volume Cuts Electric Field Simulation DRU = 1 cts/kevee/kg/day Event Rate (DRU) L.")
14 Before Fiducial Volume Cuts WS 58.6 Live-Day 5649 evts 2!12 kevee R<9.5cm, 2<z<14 cm) µ NR (50% Acc.) Integrated rate over inner volume rate for individual shell log 10 (s2/s1) [normalized] 0.2 0!0.2!0.4!0.6 3! NR ~ 2 DRU DRU 10 0 Mass of fiducial volume: [kg] ~600 mdru! radius / height of F.V. [cm]! kev r [0.98 phe/kev] R. Gomez (Rice) Most (spurious) events in box are removed by Fiducial Volume Cuts Electric Field Simulation DRU = 1 cts/kevee/kg/day Event Rate (DRU) L. deviveiros (Brown) Peter Sorensen DSU 2007 p z [cm] 0 r [cm] 10 DRU
15 After Fiducial Volume Cuts still have 23 events (a significant fraction appear non-gaussian) events (+) removed by (primary analysis) cut targeted at anomalous S1 hit-pattern + Removed by primary analysis Gamma X cuts Remaining after all primary cuts origin of non-gaussian tails in ER distribution: Gamma X events Peter Sorensen DSU 2007 p 15
16 Gamma X: what is it? normal multiple scatter a multiple scatter in which the S2 from one of the scatters is absent Gamma X multiple scatter Ground Example: 15cm e- e- e- e- e- S1 e- e- e- e- e- S1 if the 2nd scatter happened below the cathode grid, its S2 would be absent. Result: S2/S1 decreases, which can make an electron recoil look like a nuclear recoil 15cm e- e- e- e- e- S1 1cm -12 kv 1cm S1 log10(s2/s1)=2.34 log10(s2/s1)=2.10 Peter Sorensen DSU 2007 p 16
17 How to spot a Gamma X Event: S1 Hit-Pattern Scatter in center of FV Scatter close to bottom of FV Gamma X scatter S1 S1 log10 (phe) S1 S1 Events in Fiducial Volume => diffuse Hit Pattern Events near bottom => more localized Hit Pattern Events below cathode => highly localized Hit in ~ 1,2 PMTs Primary Analysis Gamma X cut: Peter Sorensen DSU 2007 p 17
18 XENON Live-Day Result 10 Events in box (Yellin Maximal Gap, no BG subtraction, 50% NR acceptance, 86% cut acceptance) SI Cross-Section Exclusion Limit 136 kg-days Events remaining in box are NOT dark matter... they are Gamma X events Peter Sorensen DSU 2007 p 18
19 A More Sophisticated Gamma X Cut cuts developed as part of an independent (secondary) blind analysis Monte Carlo Simulation S1max bottom / Σ S1bottom * S1 S1 S1 also for 2,3,4 PMT (w localization req.) Peter Sorensen DSU 2007 p 19
20 Identifying Anomalous Topologies Subset of Gamma X events with signal concentrated in 2 PMTs: Resistor-Chain Events Rare Effect: ~ 10 events in comined WS background data + Cs calibration data (175 Live-Days Equivalent) Resistor-Chain (for drift E-Field) Pocket is filled with LXe Peter Sorensen DSU 2007 p 20
21 Edge Gamma X Events Subset of Gamma X events with signal concentrated in Edge PMTs Monte Carlo Simulation Σ S1edge / Σ S1all could indicate a 2nd scatter near edge of the detector, where there are regions of reduced/zero drift field Peter Sorensen DSU 2007 p 21
22 Gamma-X Monte Carlo We have simulated the expected Gamma-X background due to gammas generated in the detector (i.e. PMT radioactivity) Simulated Gamma-X Spectrum with Secondary Scatters on the Reverse Field Region (for expected Gamma Background) We have found the rate for Reverse Field Region Gamma-X events to be subdominant for our ER vs NR discrimination their rate at low energies (<25keV) is 1mdru or less. log (S2/S1) log 10 DRU Comparing the spatial distribution of events 10-3 DRU x 10keVee x 5.3kg x 59livedays = ~3 events S1 [kevee] 0cm 0cm depth depth 15cm radius [cm] 15cm radius [cm] Peter Sorensen DSU 2007 p 22
23 Cut Efficiencies for AmBe NR Calibration Data kevr [QF=0.19] Efficiency shown for secondary analysis (primary analysis is within 1%) Peter Sorensen DSU 2007 p 23
24 Summary 10 Events in box: 5 are consistent w Gaussian ER background 5 are NOT. events 1,6, 8,10 removed by Secondary (blind) Cuts: #6: Gamma X (Resistor-Chain Hit-Pattern) #8: Gamma X (Resistor-Chain Hit-Pattern) #10: Gamma X (Anomalous Edge Hit-Pattern) #1: Coincidence n=1 (Requirement: n=2) event 2 almost removed by Secondary (blind) Cuts: Gamma X (signal concentrated in 3 PMTs) a posteriori 1% decrease in acceptance => would have been cut Discrimination challenges we can certainly overcome: Maximize discrimination against Gaussian leakage by correcting for instrumental (detector) variation (x,y,z position / PMT) Non-Gaussian Pathologies seem to arise predominantly from dead regions of LXe Peter Sorensen DSU 2007 p 24
25 XENON10 Collaboration Brown University Richard Gaitskell, Simon Fiorucci, Peter Sorensen and Luiz DeViveiros Case Western Reserve University Tom Shutt, Paul Brusov, Eric Dahl, John Kwong and Alexander Bolozdynya Coimbra University Jose Matias Lopes, Luis Coelho, Luis Fernandes and Joaquin Santos Columbia University Elena Aprile, Karl-Ludwig Giboni, Maria Elena Monzani, Guillaume Plante, Roberto Santorelli and Masaki Yamashita Lawrence Livermore National Laboratory Adam Bernstein, Chris Hagmann, Norm Madden and Celeste Winant LNGS Francesco Arneodo and Alfredo Ferella Rice University Uwe Oberlack, Roman Gomez, Christopher Olsen and Peter Shagin RWTH Aachen University Laura Baudis, Jesse Angle, Joerg Orboeck, Aaron Manalaysay and Stephan Schulte Yale University Daniel McKinsey, Louis Kastens, Angel Manzur and Kaixuan Ni Peter Sorensen DSU 2007 p 25
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