tracking at level 2 for the atlas high level trigger mark sutton university college london 26 th...

Tracking at Level 2 for the ATLAS High Level Trigger

Mark SuttonUniversity College London

26th September 2006

Vertex06 - 26th September, Relais San Clemente

M.Sutton – Tracking at Level 2 for the ATLAS High Level Trigger 2

Physics rates at the LHC

LHC pp collider, 14 TeV centre of mass energy.

Bunch crossing every 25ns – 40 MHz rate Data storage capability ~200 Hz

Reduction of ~200000 : 1 needed!

Low and high luminosity regimes, 2 x 1033 cm-2s-1 and 1034 cm-2s-1

Between ~5 and ~25 (soft) pp interactions per bunch crossing

Interesting high pT interactions complicated by “pile-up”

ATLAS will use a Three Level Trigger… Pipelined, hardware LVL1 High Level Trigger farms - LVL2

and Event Filter.



LV

L1

LV

L1

LV

L2

LV

L2

EF

EF

- Latency: 2.2s

- Hardware based (FPGA, ASIC)

- Calorimeter and Muon detectors only, with

coarse granularity

- Latency: ~10 ms, input rate < 100 kHz

- Software (specialised algorithms)

- All sub-detectors, full granularity

- Match different sub-detector information

- Work in Regions of Interest

- Latency: few seconds, input rate ~ 1 kHz

- Offline-type algorithms

- Full calibration and alignment information

- Access to full event possible

ATLAS Trigger-DAQ overview



The ATLAS Detector

Calorimeter

Muon Detector

Inner Detector



The ATLAS Inner Detector Transition Radiation Tracker

Pixel Detector

SemiConductor Tracker



Tracking in the ATLAS LVL2 Trigger High-pT electron/muon identification - Match Inner Detector tracks to information from outer

detector (calorimeter, muon detector) B Physics (at low luminosity) - Exclusive reconstruction of golden decays (e.g. B ) Inclusive b-jet tagging (e.g. in MSSM H hh bbbb)

LVL2 is the earliest stage where … Data from tracking detectors is available, It is possible to combine information from different sub-detectors

Precision tracking at ATLAS predominantly from the Inner Detector: 3 layer Pixel Detector (3 layers in the end caps) 4 Layer Semi-Conductor Tracker, SCT (9 layers in the end caps) Transition Radiation Tracker (TRT)

Two approaches for the Silicon tracking using space points Lookup table based tracking - SiTrack, Complete (all layer) silicon tracking - IdScan.



LVL2 processing in Regions of Interest (RoI’s) Most LVL1 accepted events are still uninteresting for physics studies Decision can be made by further processing only those sections of the detector that LVL1 found

interesting

Minimise data transfer to LVL2 processors Minimize processing time at LVL2

Average RoI data size ~2% of total event On average, ~1.6 RoI’s per LVL1 accepted event



H



Dealing with “Pileup” events Exploit differences between interesting

(high-pT) and uninteresting (low-pT) interactions

Each has a vertex at a different z position along the beamline.

The interesting pp collision should have more high-pT tracks, at least inside the region that generated the LVL1 RoI.

Ideally, we would want to Find the z position of the interesting pp interaction before any track reconstruction Select only groups of space points consistent with that z Only then get into combinatorial tracking.



LVL2 Tracking IdScan Algorithm overview

IdScan (Inner Detector Scan) Algorithm in four stages ZFinder to find event vertex - histogramming algorithm Pattern recognition:

• HitFilter for hits compatible with this z - histogramming

• GroupCleaner, find hit combinations consistent with single tracks. Track fitting with hits from previous stages - Kalman Filter Fitter, extrapolate to the TRT

Vertexing

ZFinderSpacePoints

Patternrecognition

trackcandidate

TracksTrackfitting

trackcandidate

trackcandidate

z-coordinate



ZFinder space point selection

Designed to be fast, without the need for detailed tracking

High pT tracks are (almost) linear in –z. Use (, z) from pairs of space points from a track for simple linear extrapolation to determine track z0

Search for hits consistent with high pT tracks Hits from high pT tracks will lie in a

restricted region of Bin hits in thin slices of , around

0.2-0.3 degrees Treat each slice (almost) independently

Take all pairs of hits and histogram their extrapolated intersection with beam line.

Fast - reduces hit combinations from lower momentum tracks



Single electron RoI (0.2 x 0.2)

O(7) good electron hits

from O(200) hits



ZFinder – Jet RoI

Jet RoI from WH event (120GeV)



ZFinder performance - single leptons

Resolution for single lepton events with no pile-up around 200m.

Varies with both andpT

Efficiencies around 95-97%.

pT (GeV)

z (mm) for muons

25 GeV electrons



ZFinder efficiency

Efficiency for finding vertex within 1 mm of true position for muons with pileup.

Approaches 100% for central pseudo rapidity, even at low pT.

Falls off for large || due to degradation of resolution.

effi

cien

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Zero suppressed scale

6 GeV muons

26 GeV muons



The Hit Filter All space points on a track originating from a given z0 have the same when calculated with

respect to z0.

Steps: Put all hits in a 2D histogram in (, ) - (currently use 0.005, 2.4 degrees) Accept hits in a bin if it contains hits in at least 4 (out of 7) layers Reject all other hits (at high luminosity, ~95% of hits are rejected!)

Limits number of combinations. Latency scales approximately linearly with number of hits.



- z view

x-y view

histogram

zz

Pattern recognition in Pile-up events

If correct vertex is found, Hit Filter track finding efficiency approaches 100%.



Group Cleaner A group from the Hit Filter may contain hits from more than one tracks, and/or some random

hits.

In GroupCleaner, we exploit the (pT ,) information to select final track candidates Similar to Hit Filter: make a 2d-histogram in 1/pT and

Select triplets of Space Points, calculate (1/pT,), fill the 2d-histogram Track candidates consist of bins with Space Points in at least 4 (out of 7) layers If two track candidates share a significant number of Space Points, keep only the longest

candidate (“clone” removal)

Resulting parameters, d0=0, zV, 1/pT, used as starting parameters for the Kalman fitter.



LVL2 Track Fit Two Distributed Kalman Filter track fits are available to take account of energy loss and multiple

scattering.

Perigee parameter estimation: Track state is a set of perigee parameters at the perigee, surface to surface extrapolation is

not needed. Uniform B field. Helical track approximation.

Full track fit: Non-uniform B-field using full ATLAS field map. Creates the set of surfaces for the extrapolation Extrapolates track using a parabolic approximation Gives better pT and 0 estimates for tracks with large .



Track resolution from Pixel and SCT

20 GeV muons. CPU time per track, Xeon 2.4 GHz

Perigee fit – 0.06 ms Full fit - 0.3 ms

LVL2 track fit Offline

Pull Pull

pT / pT 0.036 1.064 0.032 0.934

0 (mrad)

0.278 1.157 0.248 0.952

d0 (m) 21.4 1.061 18.8 0.843

z0 (mm) 0.16 1.028 0.15 0.915

0.0007 1.040 0.0007 0.899



Track extrapolation to the TRT

Tracks found in Pixel and SCT are used as seed tracks for extrapolation

Track is extrapolated to the next TRT layer. Hits within a road of extrapolated position are

used. The track parameters are updated including the

hits using the Probabilistic Data Association Filter (PDAF) - NIM A566 (2006), pp 50-53. Pixel

SCT

TRT



Timing performance

Single pT = 40 GeV electron RoI at high Luminosity

Mean number of space points ~ 200 Mean execution time ~ 1ms1

Efficiency ~95%

B physics (low Luminosity), full Silicon Tracker reconstruction

Mean execution time O(10 ms)

Latency scales with number of tracks.

1 CPU speed of 1GHz

Lat

ency

(m

s)

Number of tracks



Performance - Monte Carlo data

mesons from Ds decay. K mesons efficiency the same. O(80%) at 1 GeV. Use for resonance reconstruction …



LVL2 Vertex Algorithm Fast iterative algorithm based on the Kalman filter.

Geometrical vertex fit (without mass constraints) – equivalent to Billoir’s “full” vertex fit. Linear transformation of track parameters reduces track covariance to a block-diagonal

form (2x2 + 3x3) allows reduction size of measurement vector in the Kalman filter and reduction in matrix size for inversions during iterations and significant speed-up in calculations.

Produces estimated vertex position (x,y,z) and estimated track parameters at vertex. Full covariance matrix for the vertexed tracks.

Invariant mass calculation, evaluation of invariant mass variance using the full covariance matrix.



Invariant mass resolution

LVL2 vertex fitter (0.06 ms)

MJ / 3097 +/- 2.4

J / 44.1 +/- 2.4

Offline vertex fitter (0.16 ms)

MJ / 3097 +/- 2.3

J / 43.4 +/- 2.2

Without TRT tracking J / is 63 MeV



Resonance reconstruction - Ds →()

Fully reconstructed Ds mesons from Bs decay. Search for pairs of opposite sign tracks with |m(KK) –m(φ)| < 3 σ Add additional tracks to form Ds and apply mass cuts |m(KKπ) –m(Ds)| < 3 σ Combinatorial background seems acceptable.

M(KK) (GeV)

True KK(π)combinations



Surface Cosmic Run – Barrel SCT + TRT

SCT

TRT



Cosmic trigger reconstruction

Surface cosmic run with no B field – no pT measurement. Cosmics - large impact parameter with respect to nominal beamline.

Tracking optimised for tracks from the beamline.

Implement a naïve shift of the space points … Test performance of LVL2 tracking without retuning, Efficiency of tracking and shift.



Tracking performance in surface cosmic data

Imperfectly aligned detector.

No magnetic field - large multiple scattering at low pT.

Shift fails for large hit occupancies. Better shift algorithm will

improve efficiency.

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ye

ffic

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eff

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y

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Summary and outlook Tracking in the ATLAS Trigger is essential to achieve the physics goals of the LHC,

yet must function in a very demanding, busy environment.

Pattern recognition, track fitting and vertex finding seem to work well. Latency performance seems acceptable Performance in high luminosity, high occupancy data seems acceptable.

Level 2 tracking algorithms successfully operational with test beam and cosmic data Online tracking performance with real data very encouraging.

Work is always ongoing to improve the Level 2 Tracking.

ATLAS will see its first collisions in 2007 and with full energy in 2008… Detector and Trigger well on target for readiness within this challenging

schedule.

tracking at level 2 for the atlas high level trigger mark sutton university college london 26 th...

Documents

tracking detectors

tracking sitrack

combinatorial tracking

interesting highpt

highpt tracks

lvl2 processing

layer pixel detector

outer detector calorimeter