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Impact parameter studies with early data from ATLAS

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Presentation on theme: "Impact parameter studies with early data from ATLAS"— Presentation transcript:

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

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