1. Monica Dunford April 24th, 2007
B-tagging Efficiency
Monica Dunford
April 24th, 2007

2. Cuts and Jet Selection
Using WHbb and WHuu samples with 3mm Pixel and 5mm SCT FTKsim banks (incomplete training)
For all samples, the following are applied
-1.0 < Cot() < 1.0
-0.3 < curvature(GeV-1) < 0.3
No cuts on truth track association
Associate FTK tracks jets using the Cone 0.4 algorithm
Require r = (2+2) < 0.4
Jet Pt > 20 GeV and jet abs()< 1.0
Require at least two or more tracks associated with each jet

3. B-tagging Efficiency Method 1: Two or more tracks above with d0 > X
Method 3: d0/ Likelihood ratio
Likelihood ratio:
Acceptance Cut:
L2 Trigger uses a 2D selection with d0 and z0

4. Likelihood Ratio Distribution

FTK
iPat

5. FTK B-tagging Efficiency
FTK B-tagging efficiency for three different methods
Not entirely sure why the d0/ method is worse than d0 method

6. Compared to the L2 results
A. Coccaro’s March T&P talk
2D algorithm used
Black - L2
Green - EF
Red - offline
Note the difference in ordinate scale

7. Problems with L2 Output Re-ran L2 algorithm on WH samples
Multiple problems
Bug in Athena, 2D algorithm results are not stored correctly to ntuples
z0 results look bizarre (see next slide)
EF algorithm results are bogus (see next, next slide)

8. L2 z0 Likelihood Ratio Distr.
Not sure what this distribution should look like, but there is little discrimination power here

9. EF d0 Algorithm Results
Clearly just something wrong here

10. Comparing Efficiencies
FTK is on par with L2 d0 algorithm (but not entirely sure that L2 values results are correct)

11. Summary and Next Steps Calculate efficiency using
2D (d0 and z0) methods
Offline Algorithm
Use L1 ROI instead of Cone 4 jets
Fix L2/EF Athena ntuple issues to obtain reasonable output