1. Impact parameter studies with early data from ATLAS
Aaron Bundock
University of Liverpool

2. Overview
Analysis of transverse Impact Parameter distributions with the aim of characterising the resolution, misalignment and material budget within the ATLAS Inner Detector
IP's are used extensively in b-tagging – need to maximise IP resolution by alignment of Inner Detector
b quarks are heavy flavour and can be a sign of interesting physics, e.g. low mass higgs, SUSY…
b-tagging also needed for efficient background rejection (W + jets, tt jj)

3. Inner Detector Surrounds beampipe up to radius of ~1m
Pixel barrel layers at 5 cm (b-layer), 9 cm, and 12 cm with 2 x 3 endcaps
4 Silicon barrel layers between 30 cm and 51 cm with 2 x 9 endcaps
Both pixel and semiconductor tracker cover range |η| < 2.5
Transition radiation detector located at radius cm
Pixel layers provide 3 hits per track
SCT layers give 4 hits/track
TRT gives ~ 34 hits/track

4. Impact Parameter
Distance between the point of closest approach of a track and primary vertex
Transverse IP d0 is this distance in transverse plane x,y
Longitudinal IP z0 is the z-coordinate of this point

5. Impact Parameter resolution
Divided into intrinsic detector resolution (including misalignment) and multiple scattering terms:
σd0track = σintrinsic  σ MS
Multiple scattering depends on amount of material in detector and momentum of particle:
σMS = b
(pT2 sin θ)1/2
Full IP resolution:
σ2d0track = σ2intrinsic b2
pT2 sin θ
Total uncertainty in IP also depends on resolution of primary vertex:
σd0 = σd0track  σPV
Only valid for cylindrically symmetric material

6. Selection cuts for 900 GeV Select only well-defined tracks
Reject fake tracks, long-lived particle tracks, material interactions
Want to select a good primary vertex to reduce error in IP
Datasets used: 900 GeV
minimum bias trigger data
and Monte Carlo AODs
Good run lists applied
Cut parameter
Cut value pT
|η|
# Silicon hits
# Pixel hits
# b-layer hits
# tracks in PV
> 0.5 GeV/c
< 2.5
> 6
> 1
> 0
> 3

7. Resolution plots
Produced by fitting gaussian to a d0 distribution for each bin of 1/(p2 sin3θ)
Intercept of linear fit to resolution plot depends on alignment, and gradient depends on amount of multiple scattering (material budget)
Gaussian fit range ± 2σ

8. Resolution 900 GeV
900 GeV data + nominal MC for central region |η| < 1.2
Also shown is nominal + 10% and + 20% material

9. Resolution plots @ 900 GeV Data + nominal MC
Also MC with ‘day 1’ alignment – initial guess at module alignment

10. Resolution 900 GeV
Data + nominal MC: forward region of detector: 1.2 < |η| < 2.5
Slightly poorer resolution for endcaps (more multiple scattering)
Resolution parameterization still agrees well for endcaps

11. Selection cuts for 7 TeV Cut parameter Cut value
Similar to the cuts used for 900 GeV
Increase number of tracks in primary vertex due to higher track multiplicities
Increases resolution of primary vertex
At higher energies, can be many PVs
Need to restrict number of PVs to 1
Cut parameter
Cut value pT
|η|
# Silicon hits
# Pixel hits
# b-layer hits
# tracks in PV
# PVs
> 0.5 GeV/c
< 2.5
> 6
> 1
> 0
> 9
= 1

12. Resolution 7 TeV
Data + nominal MC (number of bins increased to 42)
Ran over 7 TeV minimum bias trigger AODs with good run selection

13. Resolution 7 TeV
Data + nominal MC, forward region 1.2 < |η| < 2.5

14. Resolution 7 TeV
Look at resolution in bins of eta to see detector behaviour in different regions
p0 represents intrinsic resolution and misalignment
p1 represents material budget
misalignment
This dataset is known to have a misalignment in one of the endcaps

15. April reprocessing: z vertex reweight
April reprocessing of data and nominal MC
Distribution of z-coordinate of primary vertex was broader in Monte Carlo than in data for April reprocessing
Performed reweight of z-coordinate:

16. Latest resolution plot @ 7 TeV
Primary vertex cut changed:
1 primary vertex with > 9 tracks
any other PVs must have < 5 tracks
Bunch crossing identification cut: BCID == 1

17. Summary
Impact parameter studies on early ATLAS data show an excellent agreement between data and simulation
Inner detector central region is well aligned, and most of forward region
Excellent modelling of ID material budget in simulation
Can move onto b-tagging studies now we have good impact parameter resolution…

18. Future Work
Study jet properties such as multiplicities, truth flavour, jet pT, jet eta, spatial distributions of jets
Look at weights of various b-tagging algorithms
Look at efficiencies and systematics
Track inefficiencies
Rejection vs. efficiency plots

19. Future Work

20. Future Work