1. Comparing Data and MC CITADL Variables on Ks and KK
With lots of help from Amir!
McFocus has been modified to include same observed accidental rate and photoelectron yield by Amir used by CITADL
We just randomly turn on cells for to simulate accidentals with measured rate
We also simulate e+e- pile up  double counting
Outline
Complications of CITADL Ks selection
Special background reducing cuts
Why are my Ks cuts more side-band friendly?
Side-bands after modifying ctdl_veeadd
Raw Wobs distribution for modified ctdl_veeadd
Comparisons of dW(K,) & dW(e,)
Summary
5/5/2019
J. Wiss (Cerenkov MC Studies)

2. Complications of CITADL Ks selection
Unlinked Vees get ctdl indices if goodks =2 or if goodlb =1 and satisfies  mass cut
The goodks = 2 mass distribution (top) is considerably distorted owing to the normalized mass cuts which means sideband methods don’t work well.
For this study, I require that the Ks daughters have ctdl indices and pass more stringent cuts on detachment and Wilson-². The resulting distribution looks more side-band friendly.
5/5/2019
J. Wiss (Cerenkov MC Studies)

3. Why are my Ks cuts more side-band friendly?
Black :Ks tracks have ctdl indices.
Red: ctdl indices +  route shut off
The goodks tight norm mass cut becomes evident when  route is shut off, but the route is open if one just want CTDL indices
The additional cuts significantly remove much of the background This plot shows effects of these cuts on Vees versus category. For this plot there is no cut requiring ctdl indices.
5/5/2019
J. Wiss (Cerenkov MC Studies)

4. Side-bands after modifying ctdl_veeadd
Comparison of Ks signal used for Cerenkov MC comparison
Release version of ctdl_veeadd
Modified version (looser goodks cuts)
Both histograms require that both Ks legs have a ctdl index and that the  route is shut off.
The black histograms use a modified ctdl_veeadd which assigns ctdl indices for goodks >0
The red histogram uses the release ctdl_veeadd which requires goodks>1 for icdl index.
It is clear that the background is much more uniform in the black histograms.
5/5/2019
J. Wiss (Cerenkov MC Studies)

5. Raw Wobs distribution for modified ctdl_veeaddWe W WK
MC versus Data
Overlay of the raw Wobs for pions from Ks  for various (threshold related) momentum regions.
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
5/5/2019
J. Wiss (Cerenkov MC Studies)

6. Comparisons: dW(K,) & dW(e,) (Modified)
Binned in Momentum regions
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
5/5/2019
J. Wiss (Cerenkov MC Studies)

7. Comparisons of raw Wobs distributions (Unmodified)We WK
MC versus Data
Overlay of the raw Wobs for pions from Ks  for various (threshold related) momentum regions.
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
5/5/2019
J. Wiss (Cerenkov MC Studies)

8. Comparisons: dW(K,) & dW(e,) (Unmodified)
Binned in Momentum regions
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
5/5/2019
J. Wiss (Cerenkov MC Studies)

9. Summary for Ks 
For monitoring the Cerenkov the CITADL Vee cuts give non-optimal side-bands
This effect diminished by use of tighter Ks cuts for studies and the “ gateway”
We could switch from goodks>0 (with mass cut) rather than goodks>1 ??
On the other hand, its easy to reprocess CITADL for Vees for specific tests
The Cerenkov Monte Carlo simulation is in good but (possibly) not perfect shape
The SB subtracted Wobs distribution for Ks  broken down by in large momentum ranges agree reasonably well with the MC distributions
Similar accidental “bumps” for sub-threshold hypotheses due to accidentals
dW(K,) & dW(e,) in data and mc have similar widths  proper NPE for cells
There is noticeable bump at low dW(K,) for 33 <P < 53.1GeV in data
Possible un-simulated inefficiency?
Spectrum mismatch within the band?
Background subtraction problem?
We have a double counting problem unless we blind Cerenkov to embedded pairs.
Not a great solution from correlated noise point-of-view, however.

10. Studies with kaons from Phi’s
This is the phi mass from the Cerenkov part of the phi skim and some sidebands with a gap. This data is based on 16 runs. The side-bands are not perfect because of a non-linearity in the background.
We also show the Monte Carlo which we will compare to the CITADL distributions observed in the data. 200K events were generated for this comparison.
5/5/2019
J. Wiss (Cerenkov MC Studies)

11. dW distributions for kaons from Phi’s
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
dW(P,K)
dW(,K)
5/5/2019
J. Wiss (Cerenkov MC Studies)

12. Comparison of Raw Wobs distributions for kaons from phi’s
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
5/5/2019
J. Wiss (Cerenkov MC Studies)

13. A different choice in sidebands
Here is a comparison of kaonicity or dW(,k) for the MC and Data
Here is a different choice of sidebands for the phi with a smaller gap. We want to gauge the sensitivity of the comparison to the exact side-band choice.
Here is a comparison of the Wobs  distributions.
There is little difference between these distributions and those with the other sidebands
5/5/2019
J. Wiss (Cerenkov MC Studies)

14. J. Wiss (Cerenkov MC Studies)
Higher Statistics
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
5/5/2019
J. Wiss (Cerenkov MC Studies)

15. Even Higher Statistics: Likelihood differences
dW(P,K)
dW(,K)
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
Agreement is not excellent but fair
5/5/2019
J. Wiss (Cerenkov MC Studies)

16. Comparison of Raw Wobs distributions for kaons from Phi’s
W(P)
W(K)
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
Agreement is not excellent but fair
5/5/2019
J. Wiss (Cerenkov MC Studies)

17. Comparison with different sidebands
dW(,K)
dW(P,K)
Here is a comparison of kaonicity and knotp with two different side-band choices. The blue choice has a larger “gap” between the signal and side-band region than the black choice. Both distributions are from data in these comparisons.
04.05 (2) 4.95  7.56 (1) 9.24  (3, 2k)
18.37  27. (1k,2p) 33  53.1 (3k,1p) 64.9  (3p)
5/5/2019
J. Wiss (Cerenkov MC Studies)

18. Comparison of different sidebands on raw Wobs
W(P)
W(K)
5/5/2019
J. Wiss (Cerenkov MC Studies)