Magnetic monopole analysis for the 2008 data Update

1. Magnetic monopole analysis for the 2008 data Update

2. Outlines Resolution of the velocity reconstruction Data-MC comparisonwith the 10 and 9-line data Sensivitycalculatedwithcutsoptimisedwith the MDP

3. Resolution of the velocity reconstruction

4. Resolution of the velocity reconstruction M.M. with0.649<bs<0.651 bs= simulated b br= reconstructed b

5. Resolution of the velocity reconstruction M.M. with0.799<bs<0.801 bs= simulated b br= reconstructed b

6. Resolution of the velocity reconstruction M.M. with0.949<bs<0.951 bs= simulated b br= reconstructed b

7. Data-MC comparisonwith the 10 and 9-line data

8. Blinding strategy Data-MC comparison with a sample of 15% of data. Optimisation of cuts with the Model Rejection Factor (MRF) or the Model Discovery Potential (MDP) on MC simulations. Sensitivity. After unblinding Apply cuts on the remaining 85% of data: 10-line detector, 3N, 3pe ~ 38.92 days. Analysis optimisation for 3 configurations in 2008: 9-line detector, 3N+2T3, 3pe ~ 39.43 days. 12-line detector, 3N+2T3, 3pe ~ 36.49 days. Upper limit for ~ 114.84 days of data taking.

9. Principle reminder 1 Apply: the standard muon reconstruction (b = 1, tc²m). the modified reconstruction (b free, tc²MM). Basic cuts: Ask for tc²MM < bc²MM, and qzen < 90°. tc²m We define a new parameter l = log( ) Weexpectl> 0 for M.M. tc²MM Example of a ldistribution: Events reconstructedwith 0.525<br<0.575 Muons (36.49 days) Neutrinos (36.49 days) MM with 0.55<bs<0.575 arbitrary normalized Number of events As expectedlM.M. > 0 l a discriminative variable for MM.

10. Principle reminder 2 Selection of events by their reconstructed velocity br: Velocity range of optimisation Examples: Number of events For 0.550 < bs < 0.575, selection of events with 0.525 < br < 0.575. For 0.675 < bs < 0.725, selection of events with 0.675< br < 0.725. For 0.825 < bs < 0.875, selection of events with 0.825 < br < 0.875. MRF (or MDP) optimised for each velocity range with the variables: l nhits

11. Data-MC comparison (10/9 lines) Fit withmodified reconstruction + basic cut: tc²MM<bc²MM Scale factor applied: 1.79 Scale factor applied: 1.64 Scale factor applied: 1.95

12. Data-MC comparison (10/9 lines) Fit withmodified reconstruction + basic cut: tc²MM<bc²MM

13. Data-MC comparison (10/9 lines) Fit withmodified reconstruction + basic cut: tc²MM<bc²MM+ qzen< 90°.

14. Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.525 < br < 0.575:

15. Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.525 < br < 0.575:

16. Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.675 < br < 0.725:

17. Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.675 < br < 0.725:

18. Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.825 < br < 0.875:

19. Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.825 < br < 0.875:

20. Sensivitycalculatedwithcutsoptimisedwith the MDP instead of MRF

21. Cuts optimisation (12 lines) Reminder 10 independant sets of cuts: A lot of backrgoundisexpected in few beta ranges for about 40 days. Whatis the impact on the expected background and on the final combinedsensitivity if cuts are optimisedwith the MDP ?

22. Cuts optimisation (12 lines) with MDP 10 independant sets of cuts:

23. Final combined sensitivity