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A New Approach to searching for n e Events in MINOS

This talk:. Motivation Basic Idea Technicalities First attempt “Results” Outlook. A New Approach to searching for n e Events in MINOS. Mark Thomson University of Cambridge. Introduction. Motivation:. To date all n m  n e analysis have used multi-variate techniques

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A New Approach to searching for n e Events in MINOS

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  1. This talk: • Motivation • Basic Idea • Technicalities • First attempt • “Results” • Outlook A New Approach to searching for ne Events in MINOS Mark Thomson University of Cambridge Mark Thomson, Cambridge

  2. Introduction Motivation: • To date allnm neanalysis have used multi-variate techniques using reconstructed quantities to separate ne and NC events. • Is this the best approach ? Maybe not…. • This analysis is a “special” case: Number of reco variables ≈ Number of strips in event • Try to perform event ID using strip information alone • NOTE: This is a pure pattern recognition problem Basic Idea: • Adopt Nearest Neighbour approach • Compare each event to “libraries” of MC events (ne and NC) • Select N best matches • Fraction of N best matches which are ne gives a measure of the likelihood of the event being a ne Mark Thomson, Cambridge

  3. Issues and Technicalities • In principle, this approach is optimal + has the “advantage” of being largely reconstruction free • But only optimal if phase space fully sampled by MC • Need VERY LARGE MC samples • don’t yet know what I mean by very large… (107-8 ?) • CPU/memory/disk implications NC Ntuples ne Ntuples Disk: MakeLibraries Events reduced to bare minimum: strips + some reco info + some MC info Memory: NC Events ne Ntuples for(int i =0;i<nData;++i){ Loop over data, compare event to MC events in libraries CPU: CompareEvent “Event Likelihood” } • Find best matches and construct PID likelihood Mark Thomson, Cambridge

  4. Pre-feasibility Study • Have developed code to investigate this approach • BUT currently insufficient MC events for full analysis • Gained better feel for how things will work • Some short cuts, e.g. • strips ganged together in 3s Current (small) MC Samples: • 500k CC ne(0-10 GeV) • 350k NC n(all energies) Event Processing: • Events passed through (nearly) Standard Reco chain with a couple of modifications • Use Atnu strip maker (faster+removes Xtalk) • Use new SRCam Track fitter (much faster) • Write out simplified Atnu ntuples (fairly compact) • Basically only interested instrips • Reco information (e.g. tracks) could be used in event preselection + rejection ofCC events, etc. Mark Thomson, Cambridge

  5. UZ UZ VZ VZ ∞ P = S S∫ P(n1,l)P(n2,l)dl planes strips 0 Event Comparison • Need a figure of merit for how well two events match • Ask question “what is the probability come from same hit pattern at PMTs ?” • Data are stored as PEs in discrete coordinates of strip/plane • Loop over all planes/strips and compare number of PEs 1st try: Poisson probs Mark Thomson, Cambridge

  6. Event Comparison cont • Events distributed throughout detector • First centre events based on charge weighted mean plane/U Strip/V strip • When matching shift whole trial event by ±1 plane, U strip, V strip “data” event Trial MC event • Code written for speed – 50000 event comparisons/second • hard to speed this up significantly Mark Thomson, Cambridge

  7. Example Matches MC ne Best match ne Best match NC Mark Thomson, Cambridge

  8. Example Matches MC ne Best match ne Best match NC Mark Thomson, Cambridge

  9. Example Matches MC ne Best match ne Best match NC Mark Thomson, Cambridge

  10. Example Matches MC ne Best match ne Best match NC Mark Thomson, Cambridge

  11. Sanity Check • For MC electron neutrino events look at energy of best match electron neutrino event • Good correlation, although matched event tends to be slightly lower in energy (due to finite MC stats) Mark Thomson, Cambridge

  12. ne NC First “Results” • Take sample of 1000 electron neutrino events • Find 10 best matches (from ne and NC libraries) • Plot fraction of top 10 matches which are true ne • Repeat for sample of 1000 NC events Dm2 = 0.0025 eV2 • Although low MC stats – performance good • Can’t be perfect as high y CC events look like NC events • too early to quote FoM – but looks very promising Mark Thomson, Cambridge

  13. Conclusions • New idea for electron neutrino appearance analysis • Based only on strip information • In the limit of infinite statistics this should be OPTIMAL • With limited MC statistics have demonstrated basic idea • Performance already rather good • However will do better: • More MC will allow better matching • Currently gang together strips in 3s (due to MC stats) (this degrades performance) Outlook • Actively pursuing this analysis • Currently generating large MC samples for proper feasibility study (5M ne + 10M NC) • approx. 250k/day generated and processed • Aim for FoM for next collaboration meeting • Aim for full analysis + ND check for Boston …….. it might just work Mark Thomson, Cambridge

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