1. Brandon McKinzie - Alex SchmahMay 20, 2015
D0 Cut Optimization
Brandon McKinzie, Alex Schmah
1
Heavy Flavour Meeting
2. Brandon McKinzie
Data
● Full preview 2 dataset with ~70M events (minimum bias).
● Based on LBNL picoDSTs.
● D0 event tree from Alex, using 2D vertex finding + linear approximation.
● Mixed event is using 13 bins in z-vertex with Delta z ~ 20 cm.
● Mixed event statistics limited so far (on purpose).
2
3. Brandon McKinzie
Strategy
● Vary topological cuts (next slide) on datasets to obtain invariant mass
histograms for same- and mixed-events (for ALL possible cut
combinations) per pT
bin.
● Total number of histograms:
○ N = 6[pT
bins] * 65
[cut combinations] = 46, 656
● Normalize associated same- and mixed-event histograms to each other.
Perform same-minus-mixed to obtain signal.
● Calculate signal significance for each.
3
4. Brandon McKinzie
Cuts Varied
Cut Name Cut Range - Set A Cut Range - Set B
|| Kaon DCA || 20 - 95 60 - 135
|| Pion DCA || 20 - 95 60 - 135
Kaon-Pion DCA* 40 - 90 40 - 80
Primary-to-Decay-Vertex Distance 50 - 200 120 - 370
cos(θ) 0.98 - 0.986 0.986 - 0.996
4
Set A: 1 < pT
< 1.5 Set B: 1.5 < pT
< 2.5
* These are upper-limit cuts
9. Brandon McKinzie
Summary and Conclusions
● Performed systematic scan of D0 topology cuts for optimal
signal significance per-pT
-interval. Cut resolution increased
about (apparent) relative minimum.
● D0 signal noticeably strongest in range: 1.5 < pT
< 2.5.
● Further increasing cut resolution yields small improvements.
● Similar plans for D+/-
9
10. Brandon McKinzie - Alex SchmahMay 20, 2015 10
Optional Slides
Past Results
