The ATLAS Trigger: HighThe ATLAS Trigger: High-Level ...
Transcript of The ATLAS Trigger: HighThe ATLAS Trigger: High-Level ...
The ATLAS Trigger: High Level TriggerThe ATLAS Trigger: High-Level Trigger Commissioning and Operation During Early
Data TakingData Taking
Ricardo Gonçalo, Royal Holloway University of LondonRoyal Holloway University of London
On behalf of the ATLAS TDAQ High Level Trigger groupOn behalf of the ATLAS TDAQ High-Level Trigger group
EPS HEP2007 – Manchester, 18-25 July 2007
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Outline
The ATLAS High-Level Triggerg ggOverall system designSelection algorithms and steering
Trigger strategy for initial runningTrigger algorithm organisationTrigger strategy for initial runningTrigger strategy for initial runningStatus
High-Level Trigger Commissioning Technical runs Cosmic-ray runsy
Summary and outlook
Ricardo Goncalo, Royal Holloway University of London2ATLAS HLT Operation in Early Running
The ATLAS High-Level Trigger
Ricardo Goncalo, Royal Holloway University of London3ATLAS HLT Operation in Early Running
Calo MuTrChOth d t t
Trigger DAQThree trigger levels:Level 1:
40 MHz
LVL1 2.5 μsCalorimeter Muon
MuTrChOther detectors
1 PB/sPipelines2.5 μs
Hardware basedCalorimeter and muons onlyLatency 2.5 μsOutput rate ~75 kHz
75 kHzLVL1 Acc.
ROD ROD ROD
CalorimeterTrigger
MuonTrigger
120 GB/sRoI’s
CTP
(Region of Interest)
Output rate ~75 kHz
Level 2: ~500 farm nodes(*)Only detector ”Regions of
ROB ROB ROB
120 GB/s
LVL2 ~40msL2SVROIB
RoI’s (Region of Interest)
RoI reques
ts
Only detector Regions of Interest” (RoI) processed -Seeded by level 1Fast reconstructionAverage execution time ~40 ms(*)
H
L2 kHz
RoI data
Event Builder3 GB/s
ROSL2PL2N
L2PL2P LVL2 Acc.
Average execution time 40 ms( )Output rate up to ~2 kHz
Event Builder: ~100 farm nodes(*)
T2 kHz
EBEvent Filter
EFPEFP
EFP
~4sec
EFNEF Acc.
( )
Event Filter (EF):~1600 farm nodes(*)Seeded by level 2
200 HzEFN
300 MB/sEvent Size ~1.5 MB
Potential full event accessOffline algorithmsAverage execution time ~4 s(*)Output rate up to ~200 Hz
Ricardo Goncalo, Royal Holloway University of London4ATLAS HLT Operation in Early Running
300 MB/sOutput rate up to 200 Hz
(*) 8CPU (four-core dual-socket farm nodes at ~2GHz
Selection method EMROILevel1 Region of Interest is found and position in EM
l i i dL2 calorim.
cluster?
calorimeter is passed to Level 2Event rejection possible at each step
Electromagnetic
L2 trackingLevel 2 seeded by Level 1Fast reconstruction
Electromagneticclusters
match?
track?Fast reconstruction algorithms Reconstruction within RoI
E.F.calorim.
E F t kiE.F.tracking
track?Ev.Filter seeded by Level 2Offline reconstruction l ithalgorithms
Refined alignment and calibration
e/γ reconst.
Ricardo Goncalo, Royal Holloway University of London5ATLAS HLT Operation in Early Running
e/γ OK?
Steering EMROI
Algorithm execution managed by Steering Based on static trigger configuration
L2 calorim.
cluster?
L2 calorim.
cluster?And dynamic event data (RoIs, thresholds)
Step-wise processing and early rejectionL2 tracking
p p g y jChains stopped as soon as a step failsReconstruction step done only if earlier step successful match?
track?
Event passes if at least one chain is successful E.F.calorim.
E F t ki
E.F.calorim.
Prescale (1 in N successful events allowed to pass) applied at end of each level
E.F.tracking
track?
Specialized algorithm classes for all situations
Topological: e g 2 μ with m ~ m
e/γ reconst.e/γ reconst.
Ricardo Goncalo, Royal Holloway University of London6ATLAS HLT Operation in Early Running
Topological: e.g. 2 μ with mμμ ~ mZ
Multi-objects: e.g. 4-jet trigger, etc……e± OK?γ OK?
Trigger algorithms
High-Level Trigger algorithms organised in groups (“slices”):Minimum bias, e/γ, τ, μ, jets, B physics, B tagging, ET
miss, cosmics, plus combined-slice algorithmsslice algorithms
For commissioningCosmics slice used to exercise trigger – already started!
For initial running:Crucial to have minimum bias, e/γ, τ, μ, jetsB physics will take advantage of initial low-lumi conditions (not bandwidth-critical)B physics will take advantage of initial low-lumi conditions (not bandwidth-critical)
Lower event rate allow low transverse momentum thresholds needed for B physics
ETmiss and B-jet tagging will require significant understanding of the detector
Will need to understand trigger efficiencies and rates using real dataZero bias triggers (passthrough)Minimum bias: 1 Select good offline Z→μμ/eeMinimum bias:
Coincidence in scintillators placed in front of calo.Counting inner-detector hits
Prescaled loose triggers“T d b ” th d t
1. Select good offline Z→μμ/ee 2. Randomly select “tag” lepton;
if triggered, use second lepton as “probe”
Ricardo Goncalo, Royal Holloway University of London8ATLAS HLT Operation in Early Running
“Tag-and-probe” method, etc 3. ε = #(triggered probes)/#(all)
Trigger strategy for initial running
Major effort ongoing to design a complete trigger list (“menu”) for initial runningCommissioning of detector and trigger; early physicsCommissioning of detector and trigger; early physicsStart with L=1031 cm-2s-1 benchmark and scale accordingly
Many sources of uncertainty:Background rate (dijet cross section uncertainty up to factor ~2)Beam-related backgroundsNew detector: alignment, calibration, noise, Level 1 performance (calo isolation?), etcEvent occupancyEvent occupancy
Must be conservative and be prepared to face much higher rates than expected
Need many “handles” to understand the trigger:Many low-threshold, prescaled triggers, several High Level triggers will run in “pass-through” mode (take the event even if trigger rejects it)Monitoring framework (embedded in algorithms, flexible and with small overheads)Monitoring framework (embedded in algorithms, flexible and with small overheads)Redundant triggers
e.g. minimum bias selection with inner detector and with min.bias scintillators
E t th t l idl i ll it f l d t
Ricardo Goncalo, Royal Holloway University of London9ATLAS HLT Operation in Early Running
Expect the menu to evolve rapidly, especially once it faces real data
Status
Trigger information routinely available in simulated dataTrigger decision and reconstructed objects easily accessible in simulated dataG t d h k d f db k f h iGenerated much work and feedback from physics groups
Trigger decision can be re-played with different thresholds on already reconstructed data: important for optimisation of selectionreconstructed data: important for optimisation of selection
Tools being developed for trigger optimisationEstimate efficiency, rate and overlapsy pNeed to be able to react quickly to changing luminosity conditions
A draft menu exists with some 90 triggersM h k i d i i i d i i h d di iMuch work is under way to optimise it and test it against the expected conditions
Rates, efficiencies and overlap between selections being studied for the menuIncluding misaligned detector in simulationIncluding misaligned detector in simulationIncluding overlapped events per bunch crossingIncluding natural cavern radiation (for muons)
Ricardo Goncalo, Royal Holloway University of London10ATLAS HLT Operation in Early Running
Technical runs
A subset of the final High-Level Trigger CPU farm and DAQ system were exercised in “technical runs”
Simulated (Level 1 triggered) Monte Carlo events in raw data format preloaded into DAQ readout buffers and distributed to farm nodes
Realistic trigger list used (e/γ, jets, τ, B physics, ETmiss, cosmics)
HLT algorithms, steering, monitoring infrastructure, configuration d t bdatabase
Measure/exercise:
Data: 4
•Mean 31.5 ms/event•98% rejection @ L2•Average 40ms/ev available
Event latenciesAlgorithm execution timeMonitoring frameworkC fi ti d t b
40% W
/Z +
Configuration databaseNetwork configurationRun-control
+ 60% di-jmean 94.3 ms/event
Accepted events only
Ricardo Goncalo, Royal Holloway University of London12ATLAS HLT Operation in Early Running
ets
Cosmics runs
A section of the detector was used in cosmics runs (see previous talk) including:including:
Muon spectrometer Tile (hadronic) calorimeterLAr (electromagnetic) calorimeterInner detector
The High-level was exercised successfully on real data in test cosmic runs
Ricardo Goncalo, Royal Holloway University of London13ATLAS HLT Operation in Early Running
runs.
Conclusions and outlook
The ATLAS High-Level Trigger is getting ready to face LHC datag g y
The final High-Level Trigger system was successfully exercised in technicalwas successfully exercised in technical runs on simulated data and was shown to be stable
High-Level Trigger algorithms and machines took part in cosmics test runs
Trigger information now routinely available in simulated data
U d f t i ti i tiUsed for trigger optimisation
Looking forward to triggering on LHC
Ricardo Goncalo, Royal Holloway University of London15ATLAS HLT Operation in Early Running
g gg gdata next year!
Backup slides
Ricardo Goncalo, Royal Holloway University of London16ATLAS HLT Operation in Early Running
~ 500~1600
SDX1 dual-CPU nodesCERN computer 6 ~100
Trigger / DAQ architecture
SDX1
Second-leveltrigger
500
LVL2 farm
EventFilter(EF)
1600computer centre Event rate
~ 200 HzData storage
6Local
StorageSubFarmOutputs
(SFOs)
100 EventBuilderSubFarm
Inputs
(SFIs)
SDX1
et
LVL2S
Network switches
pROS
stores LVL2output
DataFlowManager
Network switches
USA15Gig
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Eth
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data
requ
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dat
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Super-visor
E t d t USA15
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Data of events acceptedb fi t l l t iUSA15 1600
Event data pulled:partial events @ ≤ 100 kHz,
Reg
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UX15
Read-Out
Dedicated linksVME
by first-level trigger
Read-OutSubsystems
USA15 1600Read-OutLinks
~150PCs
@ ,full events @ ~ 3 kHz
Drivers(RODs) First-
leveltrigger
Subsystems(ROSs)
RoIBuilder
Timing Trigger Control (TTC)
Ricardo Goncalo, Royal Holloway University of London17ATLAS HLT Operation in Early Running
Event data pushed @ ≤ 100 kHz, 1600 fragments of ~ 1 kByte each
Configuration
Trigger configuration:Active triggersTh i tTheir parametersPrescale factorsPassthrough fractionsConsistent over three trigger levelsgg
Needed for: Online running Event simulationEvent simulationOffline analysis
Relational Database (TriggerDB) for online runningonline running
User interface (TriggerTool) Browse trigger list (menu) through keyRead and write menu into XML formatMenu consistency checks
After run, configuration becomes conditions data (Conditions Database)
Ricardo Goncalo, Royal Holloway University of London18ATLAS HLT Operation in Early Running
conditions data (Conditions Database)For use in simulation & analysis
Single-e Tr. Eff. (from Z→e+e-)f ti f φ d Eas a function of η, φ and ET
Mi li d G tMisaligned Geometry
Wrt. offline:Loose electron
Ricardo Goncalo, Royal Holloway University of London19ATLAS HLT Operation in Early Running
Tight electron
Trigger efficiency from data
Electron trigger efficiency from real Z→e+e- data:
MZ (GeV)
real Z→e e data:1. Tag Z events with single
electron trigger (e.g. e25i) 2. Count events with a second
electron (2e25i) and mee ≅ mZ
N d d f dNo dependence found on background level (5%, 20%, 50% tried)
~3% statistical uncertainty after 30 mins at initial luminosity
Small estimated systematic uncertainty Method Z→e+e- counting
Level 2 efficiency 87 0 % 87 0 %
Ricardo Goncalo, Royal Holloway University of London20ATLAS HLT Operation in Early Running
Level 2 efficiency 87.0 % 87.0 %
Trigger pT threshold(*) Obs
Σ ET (jets) ? ?
Trigger pT threshold(*) Obs
Electron 5,10,15, Prescale
ETmiss 12, 20, 24, 32,
36, 44Prescale
ETmiss 52, 72 No presc
J/Ψ→ee Topological B-phys
Electron 20,25,100 No presc
Di-electron 5,10 Prescale
Di-electron 15 No prescJ/Ψ→ee Topological B phys
μ μ 4 B-phys
J/Ψ→ μ μ Topological B-phys
BsDsPhiPi Topological B-phys
Photon 10,15,20 Prescale
Photon 20 No presc
Di-photon 10 Prescale
Di photon 20 No presc p g p y
BγX B-phys
e + ETmiss 18+12 Prescale
μ + ETmiss 15+12 No presc
Di-photon 20 No presc
Jets 5,10,18,23,35,42,70 Prescale
Jets 100 No presc
3 Jets 10 18 B-tagJet + ET
miss 20+30 No presc
2 Jets + ETmiss 42+30 No presc
Jet+ ETmiss +e 42+32+15 No presc
3 Jets 10,18 B-tag
4 Jets 10, 18 B-tag
4 Jets 23 Express
τ 10, 15, 20, 35Jet+ ET
miss + μ 42+32+15 No presc
4 Jet + e 23+15 No presc
4 Jet + μ 23+15 No presc
τ 10, 15, 20, 35
Di- τ 10+15,10+20,10+25
Muon 4, 6, 10, 11, 15, 20, 40 Muon spectr
τ + ETmiss 15+32,25+32,
35+20,35+32
τ + e 10+10 Express
τ + μ 10+6 Express
spectr.
Muon 4, 6, 10, 11, 15, 20, 40 ID+Muon
Di-muon 4, 6, 10, 15, 20 Passtthr.
ΣET 100 200 304 prescale
Ricardo Goncalo, Royal Holloway University of London21ATLAS HLT Operation in Early Running
τ + μ 10+6 Express
2 τ + e 10+10 Express
ΣET 100, 200, 304 prescale
ΣET 380 No presc