Search for Muon Neutrino Disappearance in a Short-Baseline

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arXiv:1010.5721v1 [hep-ex] 27 Oct 2010 atyatnurn emi created. is beam antineutrino nantly ν ∆ the Abstract Ky Sakyo-ku, Kitashirakawa-Oiwake-cho, University Collaboration Kyoto SciBooNE the Short-Baseline for Nakajima, a Yasuhiro in Disappearance Beam Neutrino Neutrino Muon for Search COLLISION IN PHYSICS XXIX the of Proceedings

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Sat Oct 17 15:44:00 2009 3.2. Neutrino Flux Measurement at SciBooNE Recostructed Muon Momentum Entries 20227 1600 Preliminary Data Charged Current Event Selection 1400 Other For the spectrum analysis at SciBooNE, we use inclu- 1200 Dirt NC sive νµ charged current (CC) interactions, whose signa- 1000 CC other 800 ture is long muon tracks. First, we identify muons by se- CC coherent π lecting the longest track with energy deposit consistent 600 CC resonance π with a minimum-ionizing particle. Second, we require 400 CCQE the vertex of the track to be within the SciBar fiducial 200 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 volume. The events are further divided into two sub- µ samples based on the location of the muon track end P (GeV) points: a “SciBar-stopped” sample containing muons Recostructed Muon Angle Sat Oct 17 15:44:01 2009 2500 Entries 20227 that have stopped inside the SciBar detector and a Preliminary Data Other “MRD-stopped” sample with muons that have stopped 2000 in the MRD. These two samples each contain approxi- Dirt 1500 NC mately 14k and 20k events with mean energies of 0.8 and CC other π 1.1 GeV, respectively. 1000 CC coherent CC resonance π 500 CCQE Spectrum Fitting 0 0 20 40 60 80 100 120 140 160 180 The neutrino spectrum at SciBooNE is extracted by µθ (deg) fitting muon momentum (Pµ) and muon angle (θµ) distributions from each sample. Fig. 3. Distribution of reconstructed muon momentum (top) and muon angle (bottom) for the MRD-stopped sample. The We prepare MC templates for Pµ and θµ distributions dots show the data, and histograms show the MC prediction for several true neutrino energy (Eν ) regions. The Eν with the contributions from neutrino interaction modes. The regions are divided by 250 MeV up to 1.25 GeV, and a MC distributions are tuned by the Eν scale factors obtained single region is assigned for Eν > 1.25 GeV. Then, the by the spectrum fit. scale factors for each Eν region are determined to mini- mize the χ2 between data and MC. Figure 2. shows the fit result. The systematic errors from SciBooNE detector 2 − 2 response and neutrino cross-section models are estimated where ∆M = Mn Mp ; M indicate the muon, proton, and shown in the plot. or neutron mass with appropriate subscripts; EB is the Figure 3. is the Pµ and θµ distributions of SciBooNE’s nucleon binding energy; Eµ is the reconstructed muon MRD-stopped sample, after applying scale factors ob- energy. tained by the spectrum fitting. We confirm the MC distributions agrees well to data after fitting. Rec MiniBooNE Eν prediction Rec To predict the Eν distribution at MiniBooNE, we 2 extrapolate the measured SciBooNE flux to MiniBooNE in two steps. 1.8 Preliminary First, we apply MiniBooNE/SciBooNE flux ratio to 1.6 make a prediction of the true neutrino energy distribu- 1.4 tion at MiniBooNE. Then, we smear the true neutrino energy prediction to the reconstructed neutrino energy. 1.2 Systematic uncertainties for the flux ratio is estimated 1 by varying the cross-section and flux models. Addition- 0.8 ally, the uncertainties of the smearing function, which Rec 0.6 convert true Eν to Eν , is estimated by varying the cross-section models. 0.4 Finally, we add MiniBooNE detector response error to Rec 0.2 the Eν prediction. 0 The predicted MiniBooNE reconstructed neutrino en- 0 0.5 1 1.5 2 2.5 Eν (GeV) ergy distribution and its systematic uncertainties are shown in the Figure 4.. Fig. 2. Scale factors obtained by SciBooNE spectrum fitting. The error bars show the sum of SciBooNE statistical and systematic uncertainties. 3.4. Oscillation Fit and Sensitivity Fit Method We test the oscillation hypothesis assuming the mixing 3.3. Flux Extrapolation to MiniBooNE between 2 neutrino flavors; νµ and νx. The νµ → νx disappearance probability is given as MiniBooNE Event Selection We select events in MiniBooNE by requiring single P (ν → ν ) = sin2 2θ sin2(1.27∆m2L/E), muon and its decay electron. Neutrino energy is recon- µ x structed from muon kinematics by assuming CC Quasi − 2 2 Elastic (CCQE) interaction (νµn → µ p): where θ is the mixing angle, ∆m [eV ] is the mass splitting between 2 flavors, L[km] is the distance traveled and − − 2 − 2 E[GeV] is the neutrino energy. Rec 2(Mn EB )Eµ (EB 2MnEB + ∆M + Mµ) Eν = , 2[(Mn − EB) − Eµ + pµ cos θµ] 3 ] 2 Total err. [eV 25000 2 Preliminary m

Flux + X-sec. err. ∆

20000 MiniBooNE det. err. 10

15000

10000 1 Preliminary CDHS 90% CL CCFR 90% CL 5000 MiniBooNE only 90% CL sensitivity SciBooNE + MiniBooNE 90% CL expected

SciBooNE + MiniBooNE 90% CL ± 1 σ 0 -1 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 10 -2 -1 10 10 1 Reconstructed Eν(GeV) sin2 2 θ

Fig. 4. Predicted MiniBooNE reconstructed neutrino energy Fig. 5. The expected sensitivity for νµ disappearance. The distribution. MiniBooNE detector error, flux and cross-sec- dotted curve shows the 90% CL limits from CDHS [9] and tion uncertainty, and the total systematic uncertainty are sep- CCFR [10] experiments. The thin solid curve is the Mini- arately shown. BooNE-only 90% CL sensitivity. The thick solid curve and the filled region are the 90% CL sensitivity and ±1σ band from SciBooNE-MiniBooNE joint analysis, respectively. Rec We fit the MiniBooNE Eν distribution to find the best fit parameter minimizing the χ2 value: 5. Acknowledgements 16 bins SciBooNE collaboration gratefully acknowledges the 2 data − p −1 data − p χ = X (Ni Ni )Mij (Nj Nj ), support from various grants and contracts from the De- i,j partment of Energy (U.S.), the National Science Foun- dation (U.S.), the MEXT (Japan), the INFN (Italy) the where i, j denote ERec bins, N data and N p denote ob- Ministry of Education and Science and CSIC (Spain), ν i,j i,j and the STFC (UK). We thank MiniBooNE collab- served and predicted number of events at each bin, re- oration for various informations and simulation out- spectively, and Mij represents statistical and systematic puts. The author was supported by Japan Society for Rec uncertainties for the final Eν prediction. the Promotion of Science, and by the Grant-in-Aid for Then we define the allowed region by ∆χ2 = the Global COE Program “The Next Generation of χ2(true) − χ2(best) values, where χ2(true) is the χ2 at Physics, Spun from Universality and Emergence” from the oscillation prediction being tested, and χ2(best) is the MEXT of Japan. 2 the smallest χ2 value across the (∆m2, sin 2θ) plane. References To obtain the confidence level at each oscillation pa- [1] A. Aguilar et al., Phys. Rev. D64 (2001) 112007. rameter point (∆m2, sin2 2θ), we use Feldman-Cousins’ [2] A. A. Aguilar-Arevalo et al., Phys. Rev. Lett. 98 method [8]. In this method, 1000 “fake-data” predic- (2007) 23180. tions are formed, using random draws of the statistical [3] A. A. Aguilar-Arevalo et al. Phys. Rev. Lett. 103 and systematic uncertainties and some underlying oscil- (2009) 111801. lation hypothesis. Then, each fake-data is fit to obtain [4] A. A. Aguilar-Arevalo et al., Phys. Rev. Lett. 103 the relation between the ∆χ2 values and the correspond- (2009) 061802. ing probabilities. This process is repeated for each pair [5] K. Hiraide et al., Phys. Rev. D78 (2008) 112004. of (∆m2, sin2 2θ) true oscillation parameter being tested. [6] A. A. Aguilar-Arevalo et al., Phys. Rev. D79 (2009) 072002. [7] A. A. Aguilar-Arevalo et al., Nucl. Instrum. Meth. Expected Limit A599 (2009) 28. The sensitivity is defined as the average of limits ob- [8] G. J. Feldman and R. D. Cousins., Phys. Rev. D57 tained from fake experiments with null underlying oscil- (1998) 3873. lation. [9] F. Dydak et al., Phys. Lett. B134 (1984) 281. [10] I. E. Stockdale et al., Phys. Rev. Lett. 52 (1984) Figure 5. shows the 90% CL. sensitivity for the νµ disappearance. The expected ±1σ band is also shown in 1384. the plot. The expected sensitivity directly supersedes the MiniBooNE only νµ disappearance result, as sub- stantial flux and cross section uncertainties have been reduced. 4. Summary and Prospects We present SciBooNE-MiniBooNE joint analysis of a search for νµ disappearance in a accelerator neutrino beam. The analysis is sensitive to the oscillation at the 2 2 ∆m region of 0.5 − 40 eV . The sensitivity to νµ disappearance has been improved relative to the MiniBooNE shape-only analysis, with results to be released soon. In addition, a joint anti-neutrino oscillation analysis will be performed using the anti-neutrino data set.