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

MINOS Coll Meet. Oxford, Jan 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

MINOS Coll Meet. Oxford, Jan Data Set  Near Det R MC 781 Files, 7.56e18 pot  Near Det R August Data 6.16e18 pot  Far Det R MC  Far Det Data ALL Runs –

MINOS Coll Meet. Oxford, Jan 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

MINOS Coll Meet. Oxford, Jan 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+)

MINOS Coll Meet. Oxford, Jan 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

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

MINOS Coll Meet. Oxford, Jan 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, % Efficiency and 50.2% Purity for NC Events This 24.5% decrease in efficiency is surprising, and will be looked at

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

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

MINOS Coll Meet. Oxford, Jan Still Lots of Low Completeness R 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

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

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

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

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

MINOS Coll Meet. Oxford, Jan 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

MINOS Coll Meet. Oxford, Jan 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

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

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

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

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

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

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

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

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

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

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

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

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

MINOS Coll Meet. Oxford, Jan 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.

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

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

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

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

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

MINOS Coll Meet. Oxford, Jan 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.

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

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

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

MINOS Coll Meet. Oxford, Jan 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

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

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

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

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

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

MINOS Coll Meet. Oxford, Jan 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