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 Using   Using New Variables

 AcdTkr1VetoSigmaHit and VetoHit1

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For completeness here is a plot of  AcdTkr1VetoSigmaHit v. AcdTkr1ActiveDist

 
VetoHit1 is the same variable, including the tracker SSDVeto. 
VetoHit1 = sqrt(Acd2Tkr1VetoSigmaHit*Acd2Tkr1VetoSigmaHit+1.5*Tkr1SSDVeto*Tkr1SSDVeto)
Here is a plot of VetoHit1 

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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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 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.
   

Reproducing Existing Performance with simple cuts 

Some other useful new Variables, (for Classification Tree)

 Another type of variable we consider is the scaled energy in a cone around the track.  We have three variables that reflect 15, 30 and 45 degreee cones.