1. Update on my oscillation analysis Reconstructed Near detector data event Reconstructed Near detector MC event Truth Near detector MC event Truth Far detector MC event Reconstructed Far detector MC event Reconstructed Far detector data event Oscillation parameters near-far beam differences Compare Peter Litchfield 3 rd August 2005

2.  How to do event by event association:  Given the near detector data event parameters;  nc/cc  total observed energy  measured y for cc events  Look for similar reconstructed events in the near detector MC  Smear the MC event parameters with a normalised gaussian Associating events  Apply a weight to each MC event given by the value of the gaussian at the distance of the data event parameter from the MC  Ideally the MC should be flat in all parameters, using the MDC Monte Carlo, apply another weight to flatten the generated distributions MC event Smeared MC event weight Data event

3. Associating events  For each reconstructed near detector MC event, look up its truth.  Look for similar truth events in the far detector in nc/cc, energy, y  Make a similar gaussian smearing and weight for the far detector truth event  Make a weight to flatten the far detector truth MC distributions  Add an oscillation weight depending on the truth energy and  m 2, sin 2 2 , f sterile  Add a weight for the near-far beam differences currently based on the near-far truth MC spectra.  For each near detector data event we now have a set of far detector MC events each with a weight that depends on how close it is in parameter space and on the oscillation parameters and beam difference parameters.

4. Reconstructed energy distributions  Using the reconstruction of the far detector MC event, define the far detector event as nc or cc and find the reconstructed energy.  Sum the weights to produce a predicted far detector nc/cc event energy distribution corresponding to the near detector event.  Sum all near detector events with appropriate near/far normalisation to produce predicted far nc anc cc energy distributions.  Compare with the far detector data nc and cc energy distributions and calculate a likelihood surface as a function of the oscillation parameters

5. Progress since Ely  Use the R1.16 PAN ntuples and David’s latest cc selection  Compiled the code instead of using the interpreter, runs much much faster!  Sorted out the normalisation so near and far distributions are normalised to pot.  Included sterile neutrinos in the same way as the nc group, i.e. with a fitted f sterile

6. Results  m 2 v sin 2 2  CC only NC only Combined 68 and 90% allowed regions m2m2 sin 2 2  LL

7. CC energy distributions Data Predictions Total energy (GeV)

8. Results sin 2 2  v f sterile 68 and 90% Allowed region  NC alone CC alone Combined f sterile sin 2 2  LL f sterile

9. NC energy distributions Data Predictions Total energy (GeV)

10. What now?  Generate the same events in the near and far detector  Program is slow because it has a three deep loop:  Near detector event-> near detector MC -> far detector MC  Would be much faster if it had the same kinematic event in the near and far MC, eliminating one of the loops.  Not trivial, have to worry about overlapping near detector events  Have some information from Panos who did a similar thing  Look at event selection  Currently use David’s selection for cc, everything else nc  Could use an nc group selection for nc  What about events selected by both groups or neither?  Look at beam weighting near-far  Sacha has expressed concern that correlations between the near and far beams are lost ion the current methods -> Adam’s matrix method  In principle each near detector MC event can be weighted for a probability of appearing in the far detector dependent on production mode, near detector position, energy etc.