New Results for ν µ ν e oscillations in MINOS

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1 New Results for ν µ ν e oscillations in MINOS Jelena Ilic Rutherford Appleton Lab 4/28/10 RAL PPD Seminar 1

2 Neutrino Mixing Mass eigenstates flavour eigenstates Maki-Nakagawa-Sakata: Flavour composition of the neutrino changes as it propagates: Two neutrino case: 4/28/10 (1.27, 2.54 in units of GeVc 4 /ev 2 km) RAL PPD Seminar 2

3 Neutrino Mixing Mixing angles θ 12 = 34 o ± 3 o θ 23 = 45 o ± 5 o θ 13 < 11 o (@90% CL; CHOOZ exp) Two oscillation regimes: Solar, reactor exp Δm 2 sol = (7.6 ± 0.2) 10-5 ev 2 Atmospheric, accelerator exp Δm 2 atm = (2.43 ± 0.13) 10-3 ev 2 4/28/10 RAL PPD Seminar 3

4 Unanswered questions Why is θ 23 near maximal? Hierarchy of neutrino masses? What is θ 13 and why is it small? ν µ ν e in MINOS normal? inverted? Is CP violation present? Why so different from quark mixing? U MNS U CKM 4/28/10 RAL PPD Seminar 4

5 Measuring θ 13 nuclear reactors Neutrino source: nuclear reactor CHOOZ reactor experiment obtained best limit (θ 13 <11 0 ) Reactor principle: Look for ν e disappearance Inverse β decay: ν e + p e + + n + smallsolarterm 2 km 4 MeV Δm 2 atm (ev2 ) 4/28/10 RAL PPD Seminar 5

6 Measuring θ 13 accelerators Neutrino source: accelerator ν µ Accelerator principle: Look for appearance of ν e 200~2500 km poten-ally large + modifica-ons (CP, ma;er effects) 0.5 ~5 GeV MINOS near detector (at 1 km) MINOS far detector (at 735 km) Δm 2 23 >0, δ CP = 0 Disappearance (reactor): θ 13 Appearance (accelerator) θ 13, θ 23, CP, sign Δm /28/10 RAL PPD Seminar 6

7 ν µ ν e in MINOS 4/28/10 RAL PPD Seminar 7

8 MINOS Main Injector Neutrino Oscillation Search Detectors - Iron scintillator calorimeters functionally identical 1) Far 5kT, 8m x 8m x 30m 484 steel/scintillator planes 2) Near 1kT, 3.8m x 4.8m x 15m 282 steel planes; 153 active planes 4/28/10 RAL PPD Seminar 8

9 NuMI Beam ~10µs spill every ~2s currently ~3.5x1013 PoT/spill Adjustable Target and Horn positions tuneable neutrino energy spectrum Beam: 92.9% νµ 5.8% νµ 1.3% νe + νe 4/28/10 RAL PPD Seminar 9

10 NuMI Beam-Protons On Target ν µ ν µ 3.14x10 20 POT (PRL , 2009) 7.01x10 20 POT 4/28/10 RAL PPD Seminar 10

11 ν µ ν e Analysis ν What we are looking for: µ ν ND e FD ND beam FD If θ /28/10 RAL PPD Seminar 11

12 ν µ ν e Analysis ν What we are looking for: µ ν ND e FD ND FD beam ν e selection If θ 13 0 Background decomposition NC ν x Predict FD spectra CC ν µ Beam ν e 4/28/10 RAL PPD Seminar 12

13 ν µ ν e Analysis ν What we are looking for: µ ν ND e FD ND FD beam ν e selection Background If θ 13 0 Any excess of events selected in the FD data above the background is interpreted as ν e appearance Compare ν e selection decomposition NC ν x CC ν µ Beam ν e Predict FD spectra 4/28/10 RAL PPD Seminar 13

14 ν e Selection Neutrino interactions Signal 4/28/10 RAL PPD Seminar 14

15 ν e Selection-FD optimized 1) Data Quality Cuts Beam quality cuts; Detector quality cuts; Timing cuts; Cosmic rejection cuts; Fiducial volume cuts 2) ν e preselection - Reduce obvious background -Remove long tracks (CC-ν µ events) - Signal box 1.0 GeV < RecoEnergy < 8 GeV 4/28/10 RAL PPD Seminar 15

16 ν e Selection-FD optimized 3) Shower identification Use topological variables 11 discriminant variables (shower profiles) Combine them to form a ANN Cut at 0.7 ANN-selected Data driven MINOS PRELIMINARY Osc ν e CC ν τ CC Signal:Background (before preselection) ~1:45 Signal:Background (after preselection) ~1:10 Signal:Background (after ANN) ~1:2 NC ν µ CC beam ν e CC Selection efficiencies: Signal: 42% NC: 5.4% CC: 0.4% 4/28/10 RAL PPD Seminar 16

17 ν e Selection - ND Apply ANN to ND Data and MC ν e candidates in ND Data and MC differ by up to 15% but they agree within uncertainties Extrapolate ND selected events (Background) to the FD For that we need to know ND ν e candidates composition How many CC ν µ? How many NC How many beam CC ν e? 4/28/10 RAL PPD Seminar 17

18 Background decomposition Events selected in ND come from different sources Each component will extrapolate differently Transport of CC components requires P osc (ν µ ν x ) Use MC to estimate CC ν µ and CC ν e background fractions? Better Use data taken in different beam configurations measure NC, CC components by adjusting horn focusing, modifying NC/CC fraction 4/28/10 RAL PPD Seminar 18

19 Background decomposition Solve a set of linear equations inputs - three data spectra unknowns - three ND background components MC inputs N on i /N off(high) i solve the equations in the bins of energy 4/28/10 RAL PPD Seminar 19

20 A crosscheck Background decomposition ν µ CC events mock NC events Use ν µ CC events (very clean sample) Remove the hits from µ tracks in ν µ CC events Apply ν e selection to the remnants (mock NC events) Compare obtained spectra with the NC spectra measured in the previous method 4/28/10 RAL PPD Seminar 20

21 Near to Far detector extrapolation When the fractions of the different background components are known they can be extrapolated to the FD Extrapolation Method: Far/Near ratio Use MC to calculate Far/Near Ratio Rescale the ND data Far/Near ratios in bins of energy 4/28/10 RAL PPD Seminar 21

22 Near to Far detector extrapolation When the fractions of the different background components are known they can be extrapolated to the FD Extrapolation Method: Far/Near ratio Use MC to calculate Far/Near Ratio Rescale the ND data FD Predicted Spectra 4/28/10 RAL PPD Seminar 22

23 Many sources of systematic errors Systematic Errors most systematics are evaluated by generating special MC with modified parameters in both the Near and Far detectors. The modified MC is used to extrapolate and calculate the difference with the standard results 2 Detectors many errors cancel out in the Far/Near extrapolation Far/Near extrapolation uncertainties 4/28/10 RAL PPD Seminar 23

24 Many sources of systematic errors Systematic Errors most systematics are evaluated by generating special MC with modified parameters in both the Near and Far detectors. The modified MC is used to extrapolate and calculate the difference with the standard results many errors cancel out in the Far/Near extrapolation Statistical error is one that dominates 4/28/10 RAL PPD Seminar 24

25 FD Prediction Background Estimation Note small ν τ CC component estimated from the Monte Carlo (ν µ ν τ ) with knowledge of ν µ disappearance Total NC ν µ CC beam ν e ν τ CC Background Prediction for 7x10 20 POT 49.1 ± 7.0 (stat)± 2.7 (sys) Signal Estimation (@ CHOOZ limit) Δm 2 32 = 2.43x10-3 ev 2 sin 2 (2θ 23 ) = 1.0 sin 2 (2θ 13 ) = 0.15 δ CP = 0 24 events 4/28/10 RAL PPD Seminar 25

26 Blind analysis check Validate ND decomposition & Far/Near ratio extrapolation Perform the full analysis with one modification: Invert the cut on the ν e discriminant: ANN<0.5 instead of ANN>0.7 Low cut- no statistically significant oscillated ν e sample would be the CHOOZ limit Expected events for ANN<0.5 sin 2 (2θ 13 )=0 sin 2 (2θ 13 )= ±13 (uncertainty in this test) 0.4x(uncertainty in main analysis) Observed 327 events 0.75σ excess if sin 2 (2θ 13 )=0 4/28/10 RAL PPD Seminar 26

27 Blind analysis check Validate ND decomposition & Far/Near ratio extrapolation We cannot use signal-box data (blind analysis) Use ν µ CC µ track removed data instead (muon removed MR data) Apply all analysis steps If the discrepancy between ND data and MC is also present in the FD, then a prediction made by scaling the far MC by the ND data to MC ratio would agree with the observed FD data. So, use MR data (&MC); run ν e selection; extrapolate ND background and compare 4/28/10 RAL PPD Seminar 27

28 Blind analysis check Verify signal efficiency 1) Test selection on pure EM showers 2) Test importance of hadronic shower from CalDet modelling Use µ removed data (MC) Data(MC) Apply ν e selection to the merged events Compare Data & MC Efficiency understood to better than 3% Apply a systematic error based on the limits 4/28/10 RAL PPD Seminar 28

29 Results ν e charged current candidate events Available data: 7x10 20 POT Background expectation: 49.1 ± 7.0(stat.) ± 2.7(syst.) 4/28/10 RAL PPD Seminar 29

30 Results ν e charged current candidate events Available data: 7x10 20 POT Background expectation: 49.1 ± 7.0(stat.) ± 2.7(syst.) OBSERVED: σ excess 4/28/10 RAL PPD Seminar 30

31 Implications for θ 13 sin 2 (2θ 13 ) allowed range depends on: CP phase δ mass hierarchy [sign(δm 2 23 )] Limits in the case of δ=0 & θ 23 = π/4: Normal mass hierarchy sin 2 2θ 13 90% CL (θ ; 0.17 rad) Inverted mass hierarchy sin 2 2θ 13 90% CL (θ ; 0.23 rad) 4/28/10 RAL PPD Seminar 31

32 Implications for θ 13 sin 2 (2θ 13 ) allowed range depends on: CP phase δ mass hierarchy [sign(δm 2 23 )] Limits in the case of δ=0 & θ 23 = π/4: Normal mass hierarchy sin 2 2θ 13 90% CL (θ ; 0.17 rad) Inverted mass hierarchy sin 2 2θ 13 90% CL (θ ; 0.23 rad) If excess in the data is considered to be a signal, the data can be fitted with a ν µ ν e oscillation hypothesis: The best fit results in the case of δ=0 & θ 23 = π/4: Normal mass hierarchy sin 2 2θ 13 = (θ ; 0.08 rad) Inverted mass hierarchy sin 2 2θ 13 = (θ ; 0.12 rad) 4/28/10 RAL PPD Seminar 32

33 Comparison with previous MINOS result New Previous Limits: Limits: Normal mass hierarchy Normal mass hierarchy sin 2 2θ 13 90% CL sin 2 2θ 13 90% CL (θ ; 0.17 rad) (θ ; 0.29 rad) Inverted mass hierarchy Inverted mass hierarchy sin 2 2θ 13 90% CL sin 2 2θ 13 90% CL (θ ; 0.23 rad) (θ ; 0.35 rad) Significant improvement! 4/28/10 RAL PPD Seminar 33

34 Conclusions The MINOS experiment is the first experiment to have been able to probe the θ 13 angle with sensitivity beyond the CHOOZ limit No significant excess of candidate ν e CC events: Expected Background: 49.1 ± 7.0(stat.) ± 2.7(syst.) events Observed: 54 events MINOS sets the tightest limits on θ 13 (normal mass hierarchy): Normal mass hierarchy sin 2 2θ 13 90% CL Inverted mass hierarchy sin 2 2θ 13 90% CL Near future: MINOS will take at least 2x10 20 POT more neutrino data this year, and has already accumulated ~2x10 20 POT anti-neutrino data Analysis improvements, combined with the additional data, could yield a substantial increase in sensitivity 4/28/10 RAL PPD Seminar 34

35 Acknowledgments The MINOS Collaboration would like to thank the many Fermilab groups who provided technical expertise and support in the design, construction, installation and operation of the MINOS experiment. Thank you to the Accelerator Division for the neutrinos! We also acknowledge the financial support from DOE; NSF; STFC(UK); the University of Athens, Greece; Brazil's FAPESP, CNPq, and CAPES. We are grateful to the University of Minnesota and the Minnesota Department of Natural Resources for hosting us. 4/28/10 RAL PPD Seminar 35

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