# Presentation: 363170

## EbE Vertexing for Mixing

- Alex
- For the LBLB group

## Scale factor from B decays

- Example: BK+
- Fit  to a single vertex
- “point”  back to K
- Measure Lxy wrt B vertex
- Pull is a proxy for a “seconday vertex” pull!

- K

- 

- 

- B

- Primary Vertex

- “Two”

- “One”

## Samples and Topologies used:

- BK+        (1:K   2: )
- BK*     (1:K 2: )
- D+K    (1:   2:K )
- ’    (1:   2:)
-                  (1: 2:  )

- “3-1”

- “2-2”

- 1

- 2

- 1

- 2

- Primary

- Primary

## The SV scale factor problem

- Pull grows as a function of lifetime!@#^$!
- Hidden dependencies!
- Detector acceptance?
- Kinematics?
- Multiplicity? (no: K*)
- Figure out which distributions are different
- Check dependency!

## Course of Action

- Trying to attack the problem from as many angles as I can!
- Take samples with sufficient statistics (’ and D+) and squeeze all the information I can out of them:
- Bin pull in several variables to test likely dependencies (, , z,  Pt, , R, Isolation, ct, Lxy, Si properties)
- ... not much success so far with this!

## Pull vs Ct / Lxy

- Pull vs Ct / Lxy

- D+  ’   K*

- Ct(one) distribution

- Lxy(one) distribution

- Lxy(two) pulls vs Lxy(one)

- Lxy(two) pulls vs ct(one)

- ’ !!!

- ’ !!!

- 1

- 2

- PV

## Including fit systematics

- D+

- ’

- Prompt D+ MC

- Secondary D+ MC

## Bottomline

- The only significant effect is visible in ct/Lxy of the object with respect to the PV!
- BUT the two samples are basically complementary in those variables:
- ’ are mostly prompt
- “My” D+ sample is mostly from secondaries (trigger biases are excluded, since the effect shows up also in the J/K(*) samples)
- Vertex position in space seems to play a role. What could explain that?

## Vertex Position and CTVMFT

- In principle the vertex is determined by two parameters (x,y) or (Lxy,d0)...

- D+

- ’

- d0 plays no clear role... Lxy seems to be relavant though...
- While looking into this we realized there was a likely candidate for this type of problem: CTVMFT does not swim the track error matrices to the vertex when computing the vertex resolution (next slide)
- Reprocessed samples with a kludge: no significant change

## CTVMFT and covariance swimming

- Vertex covariance in fit is computed using the track covariance matrix as-is
- In principle should correct, propagating the covariance at the vertex coordinate
- How big? Easiest way is reprocess data fixing the issue!
- Kludge on CharmMods (it would be nice to test what this does to lifetime fits!)
- No significant difference found in the pulls: still same dependancy on ct
- ... one step forward?

## Is the effect coming from measured or predicted?

- Measured:
- Assessed fit systematics using different models (1 gaus, 2 gaus, gaus + expo): discrepancy holds
- Predicted:
- CTVMFT seems ok:
- Above bug has no effect
- Tested on toy MC (two tracks at fixed kinematics, sliding in Lxy)
- Input: track covariances?
- Two terms dominate: d0 and 
- Scaled overall covariance terms by large factors: no effect
- Scaled COT covariance: no effect
- Planning to compare  pull with covariance term using pions from D* (skim in progress as we speak)

- Pull

**Mean predicted width**

**Measured width**

## Shift in perspective

- Focus so far has been on the low bin for D+ Maybe we are chasing a misleading evidence?
- There is room for other possibilities:
- Bin is low for other reasons (stat. Fluctuation, selection bias)
- Overall behavior is not completely consistent: pulls vs Lxy grow for D+ and decrease for ’!
- Let’s keep our mind open for other options!
- Next step: trying to break down contribution to the Lxy width
- Besides ct, what other qualitative differences are there between the samples?

## Samples are qualitatively different!

- 1

- 2

- PV

- a

- b

- b/a

- !? D+ are ‘washed’ more than anybody else!

## Quantifying the difference

- The samples look really different with respect to the size and shape of the error ellipsoid for the ‘D’ vertex, but no clear correlation emerges with the behavior of the Lxy pulls!

- “error bars” show RMS

## Track and vertex errors have different roles in the various samples!

- BD0[K] 

- ’

- D+K

- BK

- BD+[K] 

- Contribution of d0() and ‘D’ vertex error to ’s I.P. wrt ‘D’ vertex

## Montecarlo Plans

- In parallel to data studies we are carrying on a study to compare/complement data:
- Toy montecarlo to study pull of fixed kinematics vs Lxy
- We need several samples:
- As many of the modes we study on data as possible
- , K+, K*,D+
- Pythia (preferable to evaluate the PV pulls)
- Bgen (suitable for most SV studies)
- We are generating and analyzing most of those
- Some preliminary results in the next 2 pages

## L00 Reweighting on PV

- L00 efficiency and resolution in ‘out of the box’ MC not well reproduced
- Stephanie advertised last week a module that automatically shims the montecarlo distributions
- I am testing it on various MC samples
- In this case PV reconstruction in J/K PYTHIA
- Reweighted MC consistently shifts towards larger pulls in (x,y), compatible with data

## L00 Reweighting on SV

- L00 reweighting has an even larger effect on SV
- Effect seems to go in the direction of explaining our ‘problem’
- Need more samples (D+, ’, K*) to have a more complete picture
- If we find consistency with data, we can dissect the MC and get another tool to investigate the problem!

## Conclusions

- Still on our way to understand the lifetime-dependent SV scale factor
- Several sources ruled out
- Comparisons of ’ and D+ samples not as conclusive as we hoped
- MC studies are on their way!

## Backup

## Scale Factor from V1-V2

- Fit two independent subsets of ‘primary’ [I.e. non-B] tracks
- Measure (x1,y1,z1) and (x2,y2,z2)
- Obtain / for x, y and z
- Fit core with single gaussian (central value)
- Repeat fit with two
- gaussians (‘syst.’)
- Still using 1.38
- For what follows

## Distributions

- Detector acceptance (z) pretty similar
- No clear difference in Si properties
- Kinematics differs ( R Pt)
- However...

**L****xy**** Pull**

**Z**

**Isol. (0.7)**

******R**

**#L00 tracks**

****

**Pt at ********<0.15**

**#stereo tracks**

**Pt at ********>0.15**

**Pt of ‘one’**

****** ****Of two**

**Ct of ‘one’**

**one’s Lxy**

****** ****Of two**

**Pt one’s tracks**

**Pt of two**

## Pulls vs variables in prev. page

- Comparing ’ to average of other samples in each bin
- Everything excluded except ct
- Why? I can think of possible reasons, but in terms of bugs mostly! WORK IN PROGRESS

**L****xy**** Pull**

**Z**

**Isol. (0.7)**

******R**

**#L00 tracks**

****

**Pt at ********<0.15**

**#stereo tracks**

**Pt at ********>0.15**

**Pt of ‘one’**

****** ****Of two**

**Ct of ‘one’**

**one’s Lxy**

****** ****Of two**

**Pt one’s tracks**

**Pt of two**

## Ct and Lxy

- D+  ’   K*

- Ct(one) distribution

- Lxy distribution

- Lxy(two) pulls vs Lxy(B)

- Lxy(two) pulls vs ct(B)

- The problem does not show up with prompt objects!

- ’ !!!

- ’ !!!

## D+ Montecarlo vs data

- Ct(one) distribution

- Lxy(one) distribution

- Lxy(two) pulls vs Lxy(B)

- Lxy(two) pulls vs ct(B)

- The problem does not show up in montecarlo!

- Data
- MC

## ’ data vs D+ MC

- They are much more similar!
- The ‘bug’ affects non-prompt data only!!!

- Lxy(two) pulls vs ct(B)

- Lxy(two) pulls vs Lxy(B)

- D+ MC     ’ data

## Lxy (Lxy) and pulls for ’

- Lxy (Lxy) and pulls for ’

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

- Lxy mean

- (Lxy) mean

- Lxy/(Lxy) mean

- Lxy width

- (Lxy) width

- Lxy/(Lxy) width

## Same plots in D+ data: Is it in (Lxy) or in Lxy?

- Same plots in D+ data: Is it in (Lxy) or in Lxy?

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

- Lxy mean

- (Lxy) mean

- Lxy/(Lxy) mean

- Lxy width

- (Lxy) width

- Lxy/(Lxy) width

## Same plots for MC D+

- Same plots for MC D+

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

- Lxy mean

- (Lxy) mean

- Lxy/(Lxy) mean

- Lxy width

- (Lxy) width

- Lxy/(Lxy) width

## Same plots for D+ without L00

- Same plots for D+ without L00

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

**L****xy****(D****+****)**

- Lxy mean

- (Lxy) mean

- Lxy/(Lxy) mean

- Lxy width

- (Lxy) width

- Lxy/(Lxy) width

## Plots a la 7500

## Primary Vertex Pulls for:

- All B signals (left)
- ’ (below)

- Just no statistics!

- Non-statistical fluctuations dominated by fit model!

- Z

- R

- Isol(R<0.7)

- 

- Pt

- Z

- R

- Isol(R<0.7)

- 

- Pt

## Secondary Vertex Pulls for:

- All B signals (left)
- ’ (below)

- All ’ have pulls of ~1, all the B have pulls of ~1.2.. There seems to be no significant dependence except from ct!!!

- Z

- R

- Isol(R<0.7)

- 

- Pt

- Z

- R

- Isol(R<0.7)

- 

- Pt

## What do we know about EbE?

- Unbiased estimator of PVTX

|  | Transverse | Z |
| --- | --- | --- |
| Data (V1-V2) | 1.330.035 | 1.370.035 |
| MC (V1-V2) | 1.1920.034 | 1.260.035 |
| MC (V-truth) | 1.240.036 | 1.230.032 |
| J/ Prompt Peak | 1.2360.024 | ~ND~ |
| J/ d0/ | 1.1760.019 | ~ND~ |

- Reasonable (~5%) control of systematics

## Cross checks using I.P.(B)

- Lxy involves three ingredients:
- EbE
- Secondary vertex
- Beamline (in beamline constrained fits)

- Z dep. Beamline improves pulls!

- Scale factors work!

- Something funny when beamline is used!

- B

- d0

- Lxy

## Time dependence of Hourglass parameters

- Implementing DB access of time-dependent parameters

## Hourglass parameters from DBProfiles

## Relative PV/BV contribution to d0 and Lxy pulls

- B

- d0

- Lxy

- PV and BV are linear combinations of the same covariances (PV, SV), with different coefficients
- Lxy sensitive to the major axis of SV
- Relative weight of PV and SV covariances different for Lxy and d0
- Look at:

- w

- w

- Note: the two Lxy (or d0) pieces do not linearly add to 1!

## Relative PV/BV contribution to IP and Lxy pulls

- B

- d0

- Lxy

- Not Beam Constrained
- Beam constrained
- Beam constrained with run-dep. hourglass