Pass 6: GR V13R9 Muons, All Gammas, & Backgrounds Muons, All Gammas, & Backgrounds Data Sets: Muons: allMuon-GR-v13r9 (14- Jan-2008) AG: allGamma-GR-v13r9 (14-Jan-2008) BKG: backgnd-GR-v13r9 (15-Jan-2008) Bill Atwood SCIPP/UCSC Initial Pruning Cuts: ObfGamStatus >= 0 Passed Onboard Filter (Only Bkg. Data set) TkrNumTracks > 0 At least 1 track CalCsIRLn > 4 Track intercepts CAL CTBCORE >.1 Not unreasonable recon

Normalizations & Irreducibles Live time = 2 sec/Run * No. Runs *.925 (Dead Time Corr.) DT Corr = 55,270,161 evts * 26  sec/event /(2* ( )) sec total run time =.925 Irreducible Event Filter Cuts ((McZDir < -.2 & McId < 20) | Acceptance FoV for e+, e- &  (McZDir 20)) & Acceptance FoV for protons McAcdZEnter > 100 & Enters Tracker McCharge !=0 & Isn't a Photon McTHPosHitOthers < 6 & Not enough hits to track anything but e+ & e- McAcdActDistTileEnergy <.2 Entering Tile has little energy Cuts to blind Irred. Filter to Ribbons !(((abs(McAcdXEnter) 485 ) | (abs(McAcdXEnter) 155 ))| ((abs(McAcdYEnter) 485 ) | (abs(McAcdYEnter) 155 ))| (abs(McAcdXEnter)> 820 & abs(McAcdYEnter) > 820 )) As noted in Pass 5: The ratio of proton events to QED is different - predict ~ 30% have 4%! ?? have 4%! ??

CPF Analysis Remove Irreducible Events: Bkg. events left Events use Pass 5 definition CPF Acceptance & Quality Bkg. Cut: Bkg. events left McZDir 100 & McCharge != 0 & CTBCORE >.1 & CalCsIRLn > 4 All Gammas: All Gammas: First 200k Events (corresponding to 2.65 x 10 6 generated) Quality Cuts: events Left CTBCORE >.1 & CalCsIRLn > 4 Background Events Live time = 7370 sec ( Used first 3984 runs) ( events)

Pass 6 CPF Analysis Use Ribbons to close gaps between ACD Tiles Kill Tracks pointed at the ACD Corner Gaps Veto Events with 1 st Track pointed at hit Tile Remove events with excess Total ACD Energy Cut to remove events with low pulse height near Tile edges Compute Energy compensated variables for CT usage Compute CPFGamProb using CT Ensemble and Clean-upSchematic What's new since Pass 5  Using Ribbons as intended  Usage of scaled ACD energies  Improved understanding of where self veto comes from

Ribbon Cut AcdRibbonActDist > -(2 +350/sqrt(max(20,CTBBestEnergy))) & AcdRibbonEnergy >.05 & (was.1) Tkr1SSDVeto < 3 Try eliminating blanking out Ribbons AcdTkr1RibbonDist > -2 & Tkr1SSDVeto < 2 Try eliminating blanking out Ribbons The >650 ZEROs maybe a problem Low Cut Used! Inefficiency circled in RED Percentages Very low pulse height cuts, makes analysis vulnerable to mismatch to as-built equipment

AcdCornerDocaENorm = AcdCornerDoca*(min(1000, max(30, CTBBestEnergy)))^.5/10. ACD Corner Cut (Tkr1LATEdge ^ 2 + (AcdCornerDocaENorm - 10)^ 2 < 3600 | (Tkr1LATEdge < 80 & abs(AcdCornerDocaENorm-2) < 4)) & Tkr1SSDVeto < 3 ACD Corner Cut Percentages Most distances used in this analysis are scaled by This makes the cut Energy dependent. Selection: Tkr1LATEdge < 150 & Tkr1SSDVeto < 3 Vetoed Events shown in PURPLE

ACD Basic Tile Energy Cut Example 1 Cut: Tkr1SSDVeto == 0 & AcdTkr1ActiveDist > 0 & AcdTkr1ActDistTileEnergy > 1. Self Veto Study Plot the ratio of events in Low & High energy bands as a function of the Tile Energy Cut. Having jumped ahead to the end of the analysis, there is an appreciable leakage of high energy events that can be missed by this cut. This sets limits on how liberal one can be with the Active Distance and associated Tile Energy. The scaled ACD energies become less capable as the reconstructed event energy increase (ie. > 10 GeV). These suggest that the Active Dist. Min is -16mm and Tile energy is.4 MeV. Fortunately the SSD req. is == 0 Example 2 Cut: Tkr1SSDVeto < 5 & AcdTkr1ActiveDist > 0 & AcdTkr1ActDistTileEnergy >.2 Note: This portion of the CPF Analysis was done with v13r7 since my v13r9 datasets all had the min. basics Active Dist. - Tile Energy cut applied in the Skimmer. Example 1 Low & High Energy Bands Story Tkr1SSDVeto == 0 Tkr1SSDVeto <= 1 Tkr1SSDVeto <= 2 Tkr1SSDVeto <= 4

Tkr1SSDVeto == 0 & AcdTkr1ActiveDist > 0 & AcdTkr1ActDistTileEnergy > 1. Tkr1SSDVeto == 0 & AcdTkr1ActiveDist > -16 & AcdTkr1ActDistTileEnergy >.4 Correlation between Basic and Total Energy Cuts Cost of #2 over #1: 936 Events (.7%) Two Choices for the Basic Veto Cut This was the cut used in the Skimmer This cut more reflects our usage in practice #1 #2 This really helps reduce the high energy residual Bkg. events USING #2

First Cut AcdTotalTileEventEnergyRatio >.8 ACD Total Tile Energy Cut Scaled ACD Tile Energies Scaling the ACD energies to the total reconstructed energy lessens the self veto effect dramatically. Two scale energy variables are considered: AcdTileEventEnergyRatio = AcdTkr1ActDistTileEnergy/CTBBestEnergy * 100 AcdTotalTileEventEnergyRatio = AcdTotalEnergy/CTBBestEnergy * 100 Advantages & Disadvantages  Scaled responses automatically increase amount in ACD require to Veto an event as E increases  The dependence on Tracking (along with its ~ 2% mis-tracking) is greatly reduced  However as CTBBestEnergy increase, eventually even a MIP is passed… Note: something akin to this could be done onboard the LAT!

Total Tile Energy Cut AcdTotalTileEventEnergyRatio >.8 | (AcdTkr1ActiveDistENorm > -300 & AcdTotalTileEventEnergyRatio > max(.005, *AcdTkr1ActiveDistENorm)) Tile Edge Energy Cut Tile Edge Energy Cut abs(AcdTkr1ActiveDistENorm) < 15 & AcdTotalTileEventEnergyRatio >.005 Vetoed Events shown in BLUE The simple cut can be improved on by using Tracking (AcdTkr1ActiveDistENorm) ZOOM IN More can be gained by using the single tile energy associated with the 1 st Track. This is helpful near the tile edges where the pulse height is low

1 CT 2 CTs CT Analysis: To divide the data or not to divide the data? A key factor in using the ACD are the SSD Vetoes. It is natural to break the data into subsets according the number of SSD Vetos Case 1: No division Case 2: 0 and > 0 SSD Vetoes

CPF CT CPF CT Choose no division as nothing gained. Greatly amplified the statistics here by including all the available data (nominally 20k sec worth)

CPFGamProb > 0 50 Events 2.8 x Events For McTkr1DirErr 0 (98.0 %) (0.6 x ) Muon Hermiticity Test 50 Events 10 Events

Pass 6 - Pass5 CPF Performance Comparison Note: These plots use the v13r7 data - skimmed v13r9 data have Basic Cut applied Conclusion: Conclusion: Pass 6 is an order of magnitude better the Pass 5 while retaining the same Gamma efficiency!

V13r9: Shapes different due to Skimmer cut