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Cut | Purpose | Gamma Effic | Bkg Effic | Expression | |
|---|---|---|---|---|---|
BasicTileCut | Reject events with track pointing at struck tile: | 95.1% | 8.1% | Tkr1SSDVeto == 0 && AcdTkr1ActiveDist > -16 && AcdTkr1ActDistTileEnergy > .4 | |
RibbonCut | Reject events with track pointing at struck ribbon | 94.9% | 7.7% | (AcdRibbonActDist > -(2 +350/sqrt(max(20,CTBBestEnergy))) && Tkr1SSDVeto < 3 && AcdRibbonEnergy > .05 ) | |
TotalTileEnergyCut | Reject events with excess ACD total energy | 89.3% | 1.2% | AcdTotalTileEventEnergyRatio > .8 | (AcdTkr1ActiveDistENorm > -300 && AcdTotalTileEventEnergyRatio > max(.005, .1 - .0001*AcdTkr1ActiveDistENorm)) |
CornerCut | Reject event in the corner gap of the ACD | 88.3% | 1.1% | ((Tkr1LATEdge/1.5) ^ 2 + (AcdCornerDocaENorm - 10)^ 2 < 3800 | (Tkr1LATEdge < 80 && abs(AcdCornerDocaENorm-2) < 4)) && Tkr1SSDVeto < 3 |
TileEdgeCut: | Reject events at tile edges with decreased signal | 88.1% | 1.1% | abs(AcdTkr1ActiveDistENorm) < 15 && AcdTotalTileEventEnergyRatio > .005 |
ClassificationTree:
Variable | Importance |
AcdTotalTileEventEnergyRatio | 2034.17 |
Tkr1SSDVeto | 1201.47 |
CTBCORE | 812.28 |
AcdTkr1ActDistNorm | 575.06 |
AcdTkr1ActiveDistENorm | 476.57 |
AcdTotalEnergyNorm | 269.89 |
Tkr1ACDSideZ | 256.61 |
AcdActDistNorm | 118.93 |
Tkr1LATEdge | 107.11 |
AcdTileEnergyNorm | 106.51 |
AcdCornerDocaENorm | 81.52 |
AcdMaxTileEnergy | 74.32 |
AcdTileEventEnergyRatio | 73.48 |
AcdTkr1TileEnergyNorm | 25.67 |
AcdTileCount | 24 |
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Same plot, but the X-axis remapped with log10(VetoHit1+0.01) 
Note the small peak in the signal distribution around 7-9 is from low energy deposits in the tile pointed to by the track.
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Here is a plot of the low end of AcdTkr1VetoSigmaGap for all events:
For comparison, here is a plot of AcdTkr1VetoSigmaGap v. AcdTkr1RibbonDist: 
In analogy with VetoHit1 above we can define VetoGap1 to take into account the Trk1SSDVeto:
VetoGap1 = sqrt(Acd2Tkr1VetoSigmaGap*Acd2Tkr1VetoSigmaGap+1.5*Tkr1SSDVeto*Tkr1SSDVeto)
Here is a plot of the low end of VetoGap1 for all events.
If we look at only those events that pass weren't vetoed because of a track tile association or b/c of excess energy in the ACD we see this:
For Comparison, here is a plot of VetoGap1 v. AcdTkr1RibbonDist for the same set of events. 
AcdTkrVetoSigmaHit and VetoHit
We can also select the best association by looping over all tracks.
Here is a plot of that variable, AcdTkrVetoSigmaHit: 
In analogy to VetoHit1 we can make VetoHit from AcdTkrVetoSigmaHit.
Here is a plot of that:
Then if we only look at events that weren't rejected because of VetoHit1 or excess energy:
So, we could consider cutting very loose on this and rejecting some event with a high probablity match.
Cut | Reason | Gamma Effic | Bkg Effic |
|---|---|---|---|
VetoHit1 < 5 | Reject events with best rack pointing at struck tile/ribbon | 94.8% | 7.2% |
TotalTileEnergyCut | Reject events with excess ACD total energy | 89.3% | 1.2% |
VetoGap1 < 2 | Reject event in gaps of the ACD | 89.1% | 1.2% |
VetoHit < 2 | Reject events if other track points at hit tile | 89.0% | 1.1% |
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