Tracking at Level 2 for the ATLAS High Level Trigger Mark Sutton University College London 26 th...
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Transcript of 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.
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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
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The ATLAS Detector
Calorimeter
Muon Detector
Inner Detector
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The ATLAS Inner Detector Transition Radiation Tracker
Pixel Detector
SemiConductor Tracker
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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.
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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
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H
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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.
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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
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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
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Single electron RoI (0.2 x 0.2)
O(7) good electron hits
from O(200) hits
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ZFinder – Jet RoI
Jet RoI from WH event (120GeV)
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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
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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
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Zero suppressed scale
6 GeV muons
26 GeV muons
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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.
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- 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%.
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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.
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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 .
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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
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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
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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
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Performance - Monte Carlo data
mesons from Ds decay. K mesons efficiency the same. O(80%) at 1 GeV. Use for resonance reconstruction …
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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.
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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
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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
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Surface Cosmic Run – Barrel SCT + TRT
SCT
TRT
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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.
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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.
eff
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nc
ye
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eff
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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.