1. HARP Anselmo Cervera Villanueva University of Geneva (Switzerland) K2K Neutrino CH Meeting Neuchâtel, June 21-22, 2004

2. Overview  HARP  K2K  HARP contribution to K2K  Geometrical acceptance  Tracking efficiency  Particle identification  Pion yields

3. HARP

4. The HARP experiment (CERN) 124 people24 institutes

5. Physics goals  Systematic study of HAdRon Production: 1.5-15 GeV/c  Beam momenta: 1.5-15 GeV/c from hydrogen to lead  Target: from hydrogen to lead  Motivation: neutrino factories super-beams  Pion/kaon yield for the design of the proton driver of neutrino factories and SPL-based super-beams atmospheric neutrino flux  Input for precise calculation of atmospheric neutrino flux MiniBooNEK2K  Input for prediction of neutrino fluxes for the MiniBooNE and K2K experiments Monte Carlo  Input for Monte Carlo generators (GEANT4, e.g. for LHC, space applications)

6. K2k

7. K2K Experiment (Japan)  First long base line neutrino experiment (250 km)  To confirm with beam neutrinos the Super-K results 250 km  = 1.3 GeV  almost pure  : ~98%  -like event at Super-K

8. Overview of K2K 12.9 GeV protonbeam ++++ ++++  p Target + Horn pion monitor (cerenkov) decay pipe muon monitor near detectors Super-K 200m 100m 250km no oscillation oscillation predicted measured  Far/Near spectrum ratio ≠ 1 confirmed by pimon Beam MC 1Kt

9. HARP contribution to K2K

10. Motivation of this analysis K2Kinterest K2K far/near ratio Beam MC Beam MC, confirmed by Pion Monitor To be measured by HARP 0.5 1.0 1.5 2.0 2.5 0 E  (GeV) oscillationpeak One of the largest systematic errors on the neutrino oscillation parameters measured by the K2K experiment comes from the uncertainty on the far/near ratio pions producing neutrinos in the oscillation peak

11. Forward Acceptance MC dipole NDC1 NDC2 B x z x z y top view

12. The ingredients tracking  p and  measurement (at the interaction vertex)  connect tracks with particle identification (PID) measurements PID  Identify pions  Reject protons, kaons and electrons (p,  ) absolute normalization bin migration matrix total efficiency pion yield pion purity (background) To measure all this one needs: data  We have reproduced in HARP the exact K2K conditions:  12.9 GeV/c proton beam  An exact replica of the K2K target (2 aluminium) acceptance pion id efficiency

13. Forward Tracking dipole magnet NDC1 NDC2 B x z NDC5 beam target Top view 1 2 NDC3 NDC4 2D segment 3  We distinguish 3 track types depending on the nature of the matching object upstream the dipole 1.3D-3D 2.3D-2D 3.3D-Target/vertex (independent of NDC1)  The idea is to recover as much efficiency as possible to avoid hadron model dependencies.  Saturation of NDC1 in the beam spot region  High density of hits in NDC1 provokes correlation between particles hadron model dependencies problems solutions systematic error

14. Momentum and angular resolutions The momentum and angular resolutions are well inside the K2K requirements MC data 1 type No vertex constraintincluded MC momentum resolution angular resolution

15. Tracking efficiency  It can be computed with the DATA as a function of x 2 and  x2  We use the MC to perform the conversion:  once demonstrated that DATA and MC agree in their x 2 and  x2 distributions dipole magnet NDC1 NDC2 B x z NDC5 beam target Top view 1 2D segment 2 3 extrapolation to this plane

16. Module efficiency  The efficiency of NDC2 and NDC5 is flat within ~5%.  The efficiency of the lateral modules (3 and 4) is flat within 10%  The combined efficiency is not sensible to these variations. NDC2 NDC5 NDC3 NDC4 NDC 2 NDC 5 NDC 4 NDC 3 data dipole

17. Downstream efficiency NDC2 NDC5 NDC3 NDC4 MC dipole

18. Up-down matching efficiency  Is the probability of matching a downstream track with the other side of the dipole dipole magnet NDC1 NDC2 B x z NDC5 beam target Top view 1 NDC3 NDC4 2D segment 2 3 MC and data agree within ~3% in their shapes We tune to the DATA the absolute scale of each track type MCdata +

19. Total tracking efficiency  The MC reproduces the up-down matching efficiency in terms of x 2 and  x2 within ~3%  The downstream efficiency is flat We can use the MC to compute the total efficiency as a function of p and  MCdata +

20. Particle identification e+e+ ++ p number of photoelectrons  inefficiency e+e+ h+h+ 0 1 2 3 4 5 6 7 8 9 10  p P (GeV)  e  k TOF CERENKOV CALORIMETER 3 GeV/c beam particles TOF CERENKOV TOF ? CERENKOV CALORIMETER TOF CERENKOV CAL ++ p data

21. Pion ID efficiency and purity tof cerenkov calorimeter momentumdistribution Using the Bayes theorem: 1.5 GeV 3 GeV 5 GeV data we use the beam detectors to establish the “true” nature of the particle

22. Pion yield  To be decoupled from absorption and reinteraction effects we have used a thin target data p-e/  misidentification background K2K replica target 5% Al target 200% Al target

23. data

24. Conclusions  The tracking efficiency is known at the level of ~5%  The pion ID correction factor is fully computed with data (except kaon contamination below 3GeV) Small systematic error Small systematic error  However, a detailed study of the PID systematic error is still missing Next  Increase tracking efficiency reduce systematic (<5%)  Use the MC to compute the systematic error on the pion ID correction factor  Larger MC and data statistics (p,  ) 2D distribution  Detailed study of migration effects  Replica target z dependence

25. Iterative Particle ID  An initial estimation of the yields is introduced. In this case we have used pi:p:e:k = 1:1:1:1  The output yield is introduced as input for the next iteration  We stop when the yields stabilize  Efficiency and purity increase progresively 3 GeV no target 5 GeV no target iter 1iter 2 iter 3 iter 4 iter 5 iter 1iter 2 iter 3iter 4 the line represents the true PID while the colored histo is the reconstructed PID  p e k  p e k