a fast level 2 tracking algorithm for the atlas detector mark sutton university college london 7 th...

A Fast Level 2 Tracking Algorithm for the ATLAS Detector

Mark SuttonUniversity College London

7th October 2005

TIME 2005 - 7th October, Zurich

M.Sutton - A Fast Level 2 Tracking Algorithm for ATLAS 2

Physics rates at the LHC LHC pp colider, collision energy

14 TeV Bunch crossing every 25ns - 40MHz

rate Data storage capability ~200Hz

Reduction of ~200000 : 1 needed!

Peak luminosity: 2x1033 cm-2s-1 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 LVL2 and Event Filter farms



LVL1

LVL1

LVL2

LVL2

EF

EF

> Latency: 2.5s (max)

> Hardware based (FPGA, ASIC)

> Calo/Muon (coarse granularity)

> Latency: ~10 ms (average)

> Software (specialised algs)

> All sub-dets, full granularity

> Match different sub-det info

> Work in Regions of Interest

> Latency: few sec (average)

> Offline-type algorithms

> Full calibration/alignment info

> Access to full event possible

ATLAS Trigger-DAQ overview



The ATLAS Detector

Calorimeter

Muon Detector

Inner Detector



The ATLAS Inner Detector

TRT

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 lumi) - 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 … Pixel only using Lookup tables - 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



One bunch crossing

One pp interaction



Dealing witrh “Pileup” events Exploit differences between interesting

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

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

The interesting pp collision should have more high-pT tracks, at least inside the RoI 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 Z Finder to find event vertex - histogramming algorithm Hit Filter for hits compatible with this z - histogramming Find hit combinations consistent with single tracks. Track fitting with hits from previous stages - Kalman Filter Fitter, extrapolate to the TRT

(See talk by Dmity Emelyanov)

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 , (in bins of 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



Use narrow phi slices (0.2-0.3 degrees) improves selection of high pT tracks and significantly reduces combinatorial multiplicity.

ppTT ~ ~ 20 GeV

~ 0.3 degrees

pT ~ 1 GeV

~ 5 degrees

Curvature in the transverse plane



From total ~200 hits, only ~7 good electron hits

Single electron RoI (0.2x0.2)



ZFinder – Jet RoI

Jet RoI fromWH event



Zfinder Performance - Single electrons

Resolution for single 25 GeV electron events (with no pile-up)

~200 m, varies with Efficiency approaches 100%

In low luminosity events (with pile up) efficiency approaches 95-97%



The HitFilter All Space Points on a track originating from a given z0 have the same when

calculated with respect to z0 …

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 lumi, ~95% of hits are rejected!)

Limits number of combinations

Latency behaviour, approximately linear with number of hits.



- z view

x-y

view

- histogram

zz

Pattern Recognition in Pile-up events

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



Group Cleaner A group from the Hit Filter may contain hits from more than one track, and maybe

some random hits

In Group Cleaner, we exploit the (pT,0) information to select final track candidates Similar to Hit Filter: make a 2d-histogram in 1/pT and 0

Select triplets of Space Points, calculate (1/pT,0), 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)

(d0=0, zV, 1/pT, 0) are good starting parameters for the Kalman fitter

Fitter also performs some outlier removal and can extrapolate tracks into the TRT.



Performance

Single pT = 40 GeV electron RoI at high Luminosity

Mean number of space points ~ 200

Mean execution time ~ 1ms1

ZFinder resolution ~ 200m Efficiency ~95%

B physics (low Luminosity), full Silicon Tracker reconstruction

Mean execution time ~10ms

1 CPU speed of 1GHz

Exe

cuti

on T

ime

(ms) 30

0 2000 4000 6000 8000 10000

Number of space-points

20

10

Linear scaling with occupancy

0



Performance - Monte Carlo data

25 GeV electrons, design (high) luminosity with pileup.

Vertex residual for u- and b-jet events.



Resonance reconstruction

Fully reconstructed mesons from the Ds channel.



ATLAS Combined Test Beam

Transition RadiationTracker

First MuonChambers

HadronicCalorimeter

ElectromagneticCalorimeter

Beam Line



Test beam performance

40 GeV muons in magnetic field, 100A solenoid current.

Vertex residual with respect to offline kalman filter algorithm.

Full alignment proceedure still in development stage,

resolution around 200 microns

Efficiencies approaching 100%



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 environment.

Reconstructing the primary interaction coordinate in z to aid subsequent pattern recognition works well …

Latency performance seems acceptable, Performance in high luminosity, high occupancy data seems acceptable.

Level 2 tracking algorithms successfully operational in test beam First look at 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 in 2007 … Detector and Trigger well on target for readiness within this challenging

schedule.



And finally …

A big THANK YOU to the organising committee for the excellent choice of venue for the conference dinner.

a fast level 2 tracking algorithm for the atlas detector mark sutton university college london 7 th...

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