# Presentation: 363166

## EbE Vertexing for MixingUpdate (CDF-7673)

- _Alessandro Cerri_, Marjorie Shapiro
- Aart Heijboer, Joe Kroll
- UPenn

## Why our yields are lower when compared with other analyses on the same samples?

- Short answer: fixed
- Long answer: most this talk
- Two cuts mostly responsible:
- 2fit was rather tight (23D<10-15 in most cases)
- Mass histograms were filled with pretty tight requirements on the Primary Vertex:
-  of at least two PV (for V1-V2)
- Various other cuts were tighter than necessary

## The samples (before relaxing cuts)

**B****d********D****+******
**~5500**

**B****+********D****0******
**~6800**

**B****0********J/********K****+**
**~1300**

**B****0********J/********K*****+**
**~1100**

**(non-prompt)**
**D****+********K****+******
**~69000**

******’********J/******
**~6000**

- ~15000 fully reco’d B, ~69000 Fully reco’d D+, ~6000 fully reco’d ’ (re-running)
- Montecarlo: mostly BGEN (basically all of the above+Bs), using Pythia if possible

## The samples (before relaxing cuts)

**B****d********D****+******
**~5500**

**B****+********D****0******
**~6800**

**B****0********J/********K****+**
**~****5500**

**B****0********J/********K*****+**
**~****3500**

**(non-prompt)**
**D****+********K****+******
**~****99500**

******’********J/******
**~****16000**

- ~22000 fully reco’d B, ~100000 Fully reco’d D+, ~16000 fully reco’d ’
- Montecarlo: mostly BGEN (basically all of the above+Bs), using Pythia if possible

## This comes with a price though!

|  | J/K+
BGEN | J/K+
Pythia | J/K+
Data | J/K*+
MC | J/K*+
Data | K+
MC | K+
Data | J/
MC | J/
Data |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| N-1 Lxy Pull | 1.180.02
0.4 | 1.240.016
0.12 | 1.350.017
0.4 | 1.180.02
0.3 | 1.560.02
0.2 | 1.140.009
0.02 | 1.220.004
0.03 | 1.160.02
0.1 | 1.210.01
0.2 |
| N-1 d0 Pull | 0.970.02
0.3 | 1.130.014
0.07 | 1.190.014
0.4 | 0.991.3
0.2 | 1.310.02
0.2 | 1.080.008
0.02 | 1.020.003
0.03 | 1.040.02
0.1 | 1.110.008
0.3 |
| MC XSV pull | 1.300.02
0.01 |  |  | 1.230.02
0.01 |  | 1.130.01
0.15 |  | 1.210.02
0.04 |  |
| MC YSV pull | 1.250.02
0.2 |  |  | 1.280.02
0.09 |  | 1.140.01
0.2 |  | 1.270.02
0.15 |  |
| MC ZSV pull | 1.170.02
0.03 |  |  | 1.150.02
0.01 |  | 1.160.01
0.01 |  | 1.090.02
0.07 |  |
| MC Lxy Pull | 1.150.02
0.04 |  |  | 1.180.02
0.04 |  | 1.170.01
0.15 |  | 1.200.02
0.01 |  |

- Large systematic uncertainties (up to 30%) and data/mc disagreement

## Differences with last BPAK

- We gain in statistics
- Secondary vertex pulls in general get larger
- We pay a price: larger discrepancy between data and montecarlo
- The main source of  1) and 2) seems to be the 2 cut:

- N-1 Lxy Pull vs 23D

- N-1 Lxy Pll vs 2xy

- -Data
- -MC

- -Data
- -MC

- This does not quite explain 3), since agreement between data and MC seems pretty good!

- Old cut

- New cut

## Data-MC disgreement

- Disagreement is as large as O(30%)
- Can’t be neglected
- A difference in the distributions? (kinematics, geometry, chi2 etc.)
- 23D is not well reproduced, but we moved to 2xy
- Other discrepancies?

- -Data
- -MC

- 23D

- 2xy

- We compare systematically all the distributions and pull behaviors for the various samples, against MC

## How different are distributions between data/MC?

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

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

**B****0********J/********K****+**
**----****Data**
**----****MC (BGEN)**
**----MC (pythia)**

- Red boxes show qualitatively different distributions:
- Isolation

- Pythia shows pretty good agreement, BGEN has discrepancies in kinematics

## How different are distributions between data/MC?

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

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

**B****0********J/********K*****+**
**----****Data**
**----****MC**

- Red boxes show qualitatively different distributions:
- Kinematics
- 2xy

## How different are distributions between data/MC?

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

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

**D********K******
**----****Data**
**----****MC**

- Red boxes show qualitatively different distributions:
- Kinematics
- Si hits assignment

## How different are distributions between data/MC?

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

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

******’********J/******
**----****Data**
**----****MC**

- Red boxes show qualitatively different distributions:
- Kinematics
- (MC generated with FakeEv)

## How different are pulls between data/MC?

- How different are pulls between data/MC?

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

**B****0********J/********K****+**
**----****Data**
**----****MC**

- No statistical evidence of pull dependence, except for 2

## How different are pulls between data/MC?

- How different are pulls between data/MC?

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

**B****0********J/********K*****+**
**----****Data**
**----****MC**

- No statistical evidence of pull dependence, except for 2

## How different are pulls between data/MC?

- How different are pulls between data/MC?

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

**D********K******
**----****Data**
**----****MC**

- No statistical evidence of pull dependence, except for 2

## How different are pulls between data/MC?

- How different are pulls between data/MC?

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

******’********J/******
**----****Data**
**----****MC**

- No statistical evidence of pull dependence, except for 2

## Bottomline

- With larger statistics, better cuts:
- No more dependence on ct/Lxy
- Kinematics MC and data differ significantly
- However Pulls don’t seem to depend on those
- Pulls do depend on 2 but this is expected since 2 can be expressed as a linear function of the pulls themselves!
- Pulls generally larger but far from the ‘7500 numbers (~1.3)

## Repeating the ‘7500 approach

## Strategy

- Same sample
- Same selections
- |d0(D)|<100m
- |MD-MPDG|<8 MeV
- 5.4<MB<5.6
- 2XY<15
- plus:
- Right D- charge (x0.5)
- R(all B/D daughters)<2
- Lxy(D)>300 m
-       (~100% efficient because of trigger bias)
- Pt(D)>5.5 GeV
- ~170K events (working on figuring out what’s the source of the discrepancy in statistics!)
- Overall Lxy pull in good agreement with MIT fits: 1.3160.003 (width is rather sensitive on fit range & model though!)
- Dependencies?

## Caveat: Pull width depends a lot on fit details

- Example: switch fit range from 2 to 2

- We must be very careful in defining what we want to really measure: even legitimate changes in the “model” can produce significant variations!

## Distribution for “prompt” BD

- Distribution for “prompt” BD

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

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

**B****0********D****+********-**
**----****Data**

**Mass**

- R(track-B) looks very different from real signal

## Pulls for “prompt” BD

- Pulls for “prompt” BD

**Z SV**

**P****t**** SV**

**ct SV**

**L****xy**** SV**

**P****t**** Vertex Tracks**

****** ****SV**

**Isol. SV (********R<0.7)**

******R track-vertex**

****** ****track-vertex**

**P****t**** Vertex Tracks**

****** ****SV**

**#tracks w. l****00**** hits**

**#tracks w. stereo hits**

**P****t**** Vertex Tracks (********<0.3)**

**P****t**** Vertex Tracks (********>0.3)**

**Max ******

******2****3D**

******2****xy**

**B****0********D****+********-**
**----****Data**
**----****MC**

**Mass**

- 1.8

- 1.2

- Selected plots in the next page

- Significant dependence on 2

## Example of Pull vs Chi fits

- Prob.=0
- =1.27

- Prob.=0
- =1.29

- Prob.=0
- =1.32

- Prob.=0.0002
- =1.33

- Prob.=0.02
- =1.34

- Prob.=0.001
- =1.37

- Prob.=0.11
- =1.43

- Prob.=0.14
- =1.39

- Prob.=0.001
- =1.52

- Prob.=0.002
- =1.48

- Prob.=0.98
- =1.50

- Prob.=0.1
- =1.49

- Prob.=0.03
- =1.49

- Prob.=0.08
- =1.51

- Prob.=0.03
- =1.53

- Fit quality is good at low statistics
- Fit gets worse at low chi / larger statistics
- ...fit systematics dominates over statistical uncertainty...

## Pull depends on cuts and samples!

**Pull vs ********2****xy**

******2****xy ****Distribution**

- Signal

- “Prompt” BD

- Width depends on chi2

- Pull definitely depends on 2
- 2 distribution is different between signal and “prompt” BD

## The ‘7500 plots

**Z SV**

**Isol. SV (********R<0.7)**

****** ****SV**

**Mass**

**P****t**** Vertex**

******R track-vertex**

## Bottomline

- We are able to roughly reproduce the ‘7500 quantity (Lxy of ‘fake’ B)
- Remember this is a quantity which is DIFFERENT from what we usually use in our study
- For this sample there are reasons to believe that 2 shouldn’t be populated like for the signal:
- Presence of D+  and/or pions from secondaries will make it larger than in signal!
- Lxy pull is bound to grow indefinitely with 2 for “background”!
- Larger 2  wider pull

- In any approach: a tight cut on 2 will reflect in a modification of the expected Lxy pull, no matter what the definition is!

## Conclusions

- Changing cuts changes the scale factor
- Changing fit model changes the scale factor
- The scale factor is not really a “scale factor”: hidden dependencies
- A scale factor of 1.4 for the current analyses is “conservative” in terms of the limit we obtain
- For the future We know we can improve things!

## Backup

## Plan

- Measure PV scale factor from V1-V2

- Data

- MC

- Consistency

- PV scale factor from V1-V2 on data

- Measure “N-1” Lxy and d0

- Data

- MC

- Consistency,
- Validate MC

- PV scale factor from V-truth on Monte Carlo

- Measure d0(B):
- Beam, TrackbasedEbE, BeamconstrainedEbE

- Relevance of beam resolution on Lxy

- Beam  scale factor not necessary

- Primary Vertex

- Secondary Vertex

- Beamline

## PV Scale Factor (no beam constr.)

- PV Scale Factor (no beam constr.)

- Can be probed directly on data using V1-V2
- Consistent picture in data: O(1.38)
- Monte Carlo after L00 re-weighting shows similar numbers (bottom right)
- Measured systematics from fit model and across samples [effect is O(5%)]

- Pull fit:
- Reference:
- Gauss (2)
- Model Syst.:
- Bigauss
- GaussExp

## PV scale factor: other plots (X,Y,Z)

- X

- Y

- Z

- Pull uncertainty is dominated by:
- Variability among samples
- Systematic uncertainty from fit model

- 5% Uncertainty

## PV scale factor dependencies (X)

**Pull vs Z**

**Pull vs # Tracks**

**Pull vs # tracks w. z hits**

**Pull vs # tracks w.L00 hits**

**Pull vs # Tracks Pt>2**

**Pull vs Tracks <Pt>**

**Pull vs Pt B candidate**

**Pull vs ********R****max**** B candidate**

**Pull vs Isol. B candidate**

**Pull vs ******** B candidate**

## PV scale factor: details (à la CDF7500)

- Just no statistics!

- Non-statistical fluctuations dominated by fit model!

- Z

- R

- Isol(R<0.7)

- 

- Pt

## Conclusions on PV

- Scale factor measured on data
- Stable (within 5%):
- Among samples
- No evidence of dependencies
- We can move to the next step!

## Beamline

- Measure d0(B):
- Beam, TrackbasedEbE, BeamconstrainedEbE

- Relevance of beam resolution on Lxy

- Beam  scale factor not necessary

## d0(B): properties and limitations

- Three possible ways of measuring PV:
- Beamline
- Track based Primary Vertex (TBPV)
- TBPV constrained to beamline (“EbE”)
- What enters in (d0):
- Beam (1,3)
- Secondary vertex (1,2,3)
- TBPV (2,3)
- None of (1,2,3) probes only one piece!
- Regime (relative contribution of a,b,c) differs between (1,2,3) but also between Lxy and d0!

- Let’s see what happens in a real case...

## Limit to the d0 / Lxy analogy

- Limit to the d0 / Lxy analogy

- SV resolution ellipsoid is elongated and “seen from” different angles  by d0 and Lxy !

| d0 | Lxy |
| --- | --- |
| 23 | 27 |
| 12 | 36 |
| 27 | 45 |

| d0 | Lxy |
| --- | --- |
| 17 | 17 |
| 12 | 36 |
| 21 | 43 |

- B

- d0

- Lxy

- PV

- SV

- Sum

- Beam Constrained

- Not Beam Constrained

- ‘D’ Vertex error ellipsoid anisotropy (meanRMS)

- ‘D’ Vertex error scale [in 100m units] (meanRMS)

- d0 and Lxy probe different regimes of PV/SV: d0 dominated by PV, Lxy dominated by SV

## Back to d0: Comparison among samples and with MC

- Beamline

- Track based EbE

- EbE (with beam constr.)

- Beamline and SV

- SV

- Beamline and SV

- Source of deviations from 1

- Evidences of underestimate of beamline and SV errors!

## Why blow-up on the beamline does not concern Lxy

- Why 30%?
- Back-of-the-envelope calculations:
- Typical ‘long run’
- Initial and final luminosities
- On-line (SVT) beam width measurement confirms estimate
- Tested on single run
- Why it is of marginal relevance:
- Using ‘average beam width’ attenuates the effect: 30%20%:

|  |  [m] | Pull [%] |
| --- | --- | --- |
| Lxy | +0.5 | +2% |
| d0 | +2 | +6% |

- Other sources not investigated, however: not much of a concern for Lxy, relevant for d0

## Bottom line

- d0 pulls show effect of non unitarity of:
- Beamline pulls
- Secondary vertex pulls
- Restoring beamline pulls’ unitarity is of marginal (2%) relevance for Lxy
- Let’s move on to the secondary vertex!

## Secondary Vertex

- Measure “N-1” Lxy and d0

- Data

- MC

- Consistency,
- Validate MC

- PV scale factor from V-truth on Monte Carlo

## “N-1” Lxy: data and MC

- “N-1” Lxy: data and MC

- Computed Lxy pulls for the various samples
- Compared to MC evaluation
- Pretty good agreement!
- MC seems to account for (possible) inter-sample variations and absolute scale of pulls!

## Dependencies

- Look for evidence of dependencies on geometry, kinematics etc:
- Pick a suitable set of variables:
- Compare how various samples probe them
- Check pull vs variables

| Z of SV |  single track-rest of vertex |
| --- | --- |
| Pt of SV | Pt of single track |
| Combined Pt of tracks in SV |  of SV |
| Ct of SV | #tracks with L00 hits in SV |
| Lxy of SV | #tracks with stereo hits in SV |
|  of SV | Combined Pt of tracks in SV (<0.3) |
| Isolation of candidate B (R<0.7) | Combined Pt of tracks in SV (>0.3) |
| R single track-rest of vertex |  |

## Selected Plots

- We expect some variation as a function of Z (for instance, because of detector structure)
- Ct dependence?
- All variations well within 10% when integrated over kinematics

- ~20%/mm

## SV scale factor: details (à la CDF7500)

- Non-statistical fluctuations dominated by fit model!

- Z

- R

- Isol(R<0.7)

- 

- Pt

## “N-1” d0: a cross check!

- “N-1” d0: a cross check!

- Compute also d0 pulls for the various samples
- Compare to MC evaluation
- Pretty good agreement here as well!
- Good job with the realistic simulation+reweighting!

## SV scale factor from MC

- Now that we know to what extent we can rely on MC, let’s look at reconstructed-truth!

- SVreco-Svtruth: X

- SVreco-Svtruth: Y

- SVreco-Svtruth: Z

## SV scale factor from MC

- ...projected along Pt, and broken down into PV and SV contribution:

- Lxyreco-Lxytruth

- Lxyreco-Lxytruth: PV

- Lxyreco-Lxytruth: SV

- Amazingly stable and consistent with X, Y and Z!
- Variations well within 10%

## SV Pull Strategy

- “N-1” d0 and Lxy validate montecarlo
- Dependencies studied in “N-1” d0/Lxy are mostly due to choice of variables (to be confirmed by last bullet!)
- MC predicts a SV scale factor of 1.210%
- Before blessing: dependencies of MC scale factor