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MINOS Coll Meet. Oxford, Jan 2006 1 CC/NC Data Cross Checks Thomas Osiecki University of Texas at Austin.

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Presentation on theme: "MINOS Coll Meet. Oxford, Jan 2006 1 CC/NC Data Cross Checks Thomas Osiecki University of Texas at Austin."— Presentation transcript:

1 MINOS Coll Meet. Oxford, Jan 2006 1 CC/NC Data Cross Checks Thomas Osiecki University of Texas at Austin

2 MINOS Coll Meet. Oxford, Jan 2006 2 Introduction  CC background Anti-neutrino contamination  Neutral Current Selection  Current understanding of NC data Data/MC comparisons for Near Detector  Present some CC/NC cross checks Batch Studies Event Timing Spectra in different sections of fiducial volume Do Data/MC do the same thing for different Annuli  Far Detector Data/MC comparison for NC-like Events Preliminary

3 MINOS Coll Meet. Oxford, Jan 2006 3 Data Set  Near Det R1.18.2 MC 781 Files, 7.56e18 pot  Near Det R1.18.2 August Data 6.16e18 pot  Far Det R1.18.2 MC  Far Det Data ALL Runs 31720 – 33277.

4 MINOS Coll Meet. Oxford, Jan 2006 4 Standard Snarl/Event Cuts  Beam Cuts Tortgt > 0.5e12 ppp -181 kA < Horn Current -177 kA  x and  y < 1.5 mm -2 mm < x < 0 mm 0 mm < y < 2 mm  Fiducial Volume Cut (Near) Sqrt( (1.488-x)^2 + (0.135-y)^2 ) <1.0 m 1.0 < Z < 5.0  Fiducial Volume Cut (Far) 0.25 < R^2 < 14.0 and (0.5<Z<14.3) or (16.2<Z<28)  CC Selection DavidPID>-0.2 and ntrack=1 and  q/p / (q/p) < 0.2 and q<0  NC Selection Next pages + nshower>0

5 MINOS Coll Meet. Oxford, Jan 2006 5 CC Selection and Backgrounds Selecting on q<0 Apparently does Get rid of anti-neu PID>-0.2 True CC (mu-) True NC True CC (mu+)

6 MINOS Coll Meet. Oxford, Jan 2006 6 NC Selection (Old MDC days) If (Event Has track) { if (track has error<0.2 and showerlen - tracklen > -10) { It’s NC } else { Reject } } else { if(event len < 50) { It’s NC } else { Reject } => 91.5 Eff 50.5 Pur

7 MINOS Coll Meet. Oxford, Jan 2006 7 NC Selection (New) MC Completeness < 0.5 NC CC Data Shower Len – Track LenEvent Num Planes  q/p / (q/p)

8 MINOS Coll Meet. Oxford, Jan 2006 8 Break Down of NC Selection CutTotalTrue CCTrue NCLess than 50% Complete Nshower>0 Satisfy Fid Vol 462,296336,65892,43333,205 Selected as NC-like151,12853,12168,50829,499 Event E > 0.5 GeV123,00649,77361,86111,372 67.0% Efficiency and 50.2% Purity for NC Events This 24.5% decrease in efficiency is surprising, and will be looked at

9 MINOS Coll Meet. Oxford, Jan 2006 9 What CC get identified as NC? Not surprising, it’s the High y CC Events Y-axis = Percent X-axis = E (GeV)

10 MINOS Coll Meet. Oxford, Jan 2006 10 NC-like Spectrum MC Completeness < 0.5 NC CC Data No CutE > 0.5 GeV Normalized to POT

11 MINOS Coll Meet. Oxford, Jan 2006 11 Still Lots of Low Completeness R1.18.2 R1.18 New Clean up cut got rid of PMT afterpulsing, but runt events still linger Luckily, it appears the MC is simulating the runts as in data. Normalized To Nevts

12 MINOS Coll Meet. Oxford, Jan 2006 12 Nshower and Ntrack MCData Normalized to POT

13 MINOS Coll Meet. Oxford, Jan 2006 13 Event PH / Strip MCData Normalized to POT

14 MINOS Coll Meet. Oxford, Jan 2006 14 Shower PH Development MCData Normalized to POT

15 MINOS Coll Meet. Oxford, Jan 2006 15 NC Vertices MCData Shower Event Normalized to POT

16 MINOS Coll Meet. Oxford, Jan 2006 16 Shower Lateral Spread MC Data Lots of >2m Showers? How? Using All hits in The shower Lets use hits with More information Normalized to POT

17 MINOS Coll Meet. Oxford, Jan 2006 17 Shower Lateral Spread Small hits get in shower And make it appear longer But its not really a Continuous length MC Data Using Strips with > 2pe Normalized to POT

18 MINOS Coll Meet. Oxford, Jan 2006 18 Shower Direction Cosines MC Data Normalized to POT

19 MINOS Coll Meet. Oxford, Jan 2006 19 Shower Direction Cosines (Zoom) MC Data Normalized to POT

20 MINOS Coll Meet. Oxford, Jan 2006 20 Event Time – Trigger Time 012345 R1.18.2 R1.18 Note the Accumulation of Low PH Junk

21 MINOS Coll Meet. Oxford, Jan 2006 21 Energy Spectra by Batch Normalized by Number of Events

22 MINOS Coll Meet. Oxford, Jan 2006 22 Spectra by Batch, No Norm. No Normalization

23 MINOS Coll Meet. Oxford, Jan 2006 23 Energy Spectra By Batch, no norm. (before) Other 3 points statistically higher No Normalization

24 MINOS Coll Meet. Oxford, Jan 2006 24 Shower Energy by Batch No Normalization

25 MINOS Coll Meet. Oxford, Jan 2006 25 Trk Mom. By Batch (Range) Normalized to Number of Events

26 MINOS Coll Meet. Oxford, Jan 2006 26 Time in Spill vs Event Vtx Z Straight line fit yields slope of 0.

27 MINOS Coll Meet. Oxford, Jan 2006 27 Time in Spill vs Event Vtx X Straight line fit yields slope of 0.

28 MINOS Coll Meet. Oxford, Jan 2006 28 Time in Spill vs Shower Energy Straight line fit yields slope of 0.

29 MINOS Coll Meet. Oxford, Jan 2006 29 Time in Spill vs Track Momentum Straight line fit yields slope of 0.

30 MINOS Coll Meet. Oxford, Jan 2006 30 Quanitities in Different r,z If I plot the same quantity in different quadrants of my fiducial volume, do things change? What about different Z in the detector The Center of my fiducial volume is where the beam spot is Green/Blue are closer to coil hole.

31 MINOS Coll Meet. Oxford, Jan 2006 31 Shw Energy in different quads No Normalization

32 MINOS Coll Meet. Oxford, Jan 2006 32 Trk Momentum in Different Quads No Normalization

33 MINOS Coll Meet. Oxford, Jan 2006 33 Trk Momentum in diff. quads No Normalization

34 MINOS Coll Meet. Oxford, Jan 2006 34 Different Z in Detector CC Data MC 1.0<Z<2.0 4.0<Z<5.0 Normalized to Nevt

35 MINOS Coll Meet. Oxford, Jan 2006 35 Different Z in Detector (NC) Data MC 1.0<Z<2.0 4.0<Z<5.0 Normalized to Nevt

36 MINOS Coll Meet. Oxford, Jan 2006 36 Using Different Annuli  Accurate MC, should reproduce spectra even in regions of the detector we don’t want to really (or do we) look at. r1 r2 Check to see if we reproduce different detector region spectra.

37 MINOS Coll Meet. Oxford, Jan 2006 37 Energy Spectra at Diff. Annuli Normalized to Number of Events RED = 1.0 < r < 1.1 BLACK = r < 0.35 r=0 => beam spot

38 MINOS Coll Meet. Oxford, Jan 2006 38 Shw E at Different Annuli Normalized to Number of Events RED = 1.0 < r < 1.1 BLACK = r < 0.35 r=0 => beam spot

39 MINOS Coll Meet. Oxford, Jan 2006 39 Trk Mom. At Different Annuli Normalized to Number of Events RED = 1.0 < r < 1.1 BLACK = r < 0.35 r=0 => beam spot

40 MINOS Coll Meet. Oxford, Jan 2006 40 Cuts for Far Det Neutrinos  -4us < Timing < 10 us  Litime < 0  Event Pulse Height > 2000 ADC (11/09/2005 NC Meeting)  Exclude Runs with 2 events Magnet coil trip GPS errors HV trips  When I Apply Ntrack>0 and track error < 0.2 Same Fiducial Volume No trk cosine  I get 143 events. David’s paper says 2 selections give 137 and 142 events respectively.  I get 71 Selected NC Events  NOTE – Not all events were hand scanned. Only what I showed at the 11/09/2005 meeting were scanned. From that I can tell that this selection is robust. Not to mention it’s almost exactly what David does. Not surprising, there are a few dedicated quantities to find them. => PRELIMINARY

41 MINOS Coll Meet. Oxford, Jan 2006 41 Far Detector NC Selection MC Completeness < 0.5 NC CC Data Same cuts as for Near (for now) PRELIMINARY

42 MINOS Coll Meet. Oxford, Jan 2006 42 Selection Comparison MC Data PRELIMINARY

43 MINOS Coll Meet. Oxford, Jan 2006 43 Event Quantities MC Data PRELIMINARY

44 MINOS Coll Meet. Oxford, Jan 2006 44 Shower Quantities MC Data Agrees with David’s Paper. PRELIMINARY

45 MINOS Coll Meet. Oxford, Jan 2006 45 Event Vertices MC Data PRELIMINARY

46 MINOS Coll Meet. Oxford, Jan 2006 46 Conclusions and Plans  NC Selection needs some more understanding, as things have changed.  NC Data/MC comparisons not too bad in near det Encouraging to see visible energy distributions very close  Far Det seems to be in order PRELIMINARY  Data/MC in far not too bad (for statistics) Some discrepancy in selection Again, as in near, things have changed


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