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This study focuses on the analysis of magnetic monopole data collected in 2008, emphasizing velocity reconstruction and data-MC comparisons using optimized cuts. Key results include the application of a blinding strategy on 15% of the data, while the remaining 85% was analyzed using a model rejection factor (MRF) and model discovery potential (MDP). Various configurations (10-line, 9-line, and 12-line detectors) were evaluated across specific velocity ranges. Our findings present discriminative variables and highlight the impact of cut optimization on sensitivity in detecting magnetic monopoles, leading to potentially significant results over 114.84 days of total data.
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Outlines Resolution of the velocity reconstruction Data-MC comparisonwith the 10 and 9-line data Sensivitycalculatedwithcutsoptimisedwith the MDP
Resolution of the velocity reconstruction M.M. with0.649<bs<0.651 bs= simulated b br= reconstructed b
Resolution of the velocity reconstruction M.M. with0.799<bs<0.801 bs= simulated b br= reconstructed b
Resolution of the velocity reconstruction M.M. with0.949<bs<0.951 bs= simulated b br= reconstructed b
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.
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.
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
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
Data-MC comparison (10/9 lines) Fit withmodified reconstruction + basic cut: tc²MM<bc²MM
Data-MC comparison (10/9 lines) Fit withmodified reconstruction + basic cut: tc²MM<bc²MM+ qzen< 90°.
Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.525 < br < 0.575:
Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.525 < br < 0.575:
Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.675 < br < 0.725:
Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.675 < br < 0.725:
Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.825 < br < 0.875:
Data-MC comparison (10/9 lines): Discrimative variables Upgoing events reconstructed with 0.825 < br < 0.875:
Sensivitycalculatedwithcutsoptimisedwith the MDP instead of MRF
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 ?
Cuts optimisation (12 lines) with MDP 10 independant sets of cuts:
