Level3 trigger for ALICE

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Level-3 trigger forALICE

• Bergen
• Frankfurt
• Heidelberg
• Oslo

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Assumptions

• Need for an online rudimentary event
  reconstruction for monitoring
• Detector readout rate (i.e. TPC) gtgt DAQ
  bandwidth ? mass storage bandwidth
• Some physics observables require running
  detectors at maximum rate (e.g. quarkonium
  spectroscopy TPC/TRD
  dielectrons jets in pp TPC tracking)
• Online combination of different detectors can
  increase selectivity of triggers (e.g. jet
  quenching PHOS/TPC high-pT ? - jet events)

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Data volume and event rate
bandwidth
TPC detector data volume 300 Mbyte/event data
rate 200 Hz
60 Gbyte/sec
front-end electronics
15 Gbyte/sec
Level-3 system
lt 2 Gbyte/sec
DAQ event building
lt 1.2 Gbyte/sec
permanent storage system
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Dielectrons

• Dielectron measurement in TRD/TPC/ITS
• quarkonium spectroscopy needs high rates
• TPC must operate at gt100 Hz
• TPC data rate has to be significantly reduced
• TRD pre-trigger for TPC
• level-3 trigger system for TPC
• partial readout
• eeverification event rejection

level-3 trigger system
TRD _at_ 2kHz
TPC _at_ 200 Hz
Online track reconstruction 1) selection of
eepairs (ROI) 2) analysis of
eepairs (event rejection)
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Event flow
Event sizes and number of links TPC only
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Level-3 tasks

• Online (sub)-event reconstruction
• optimization and monitoring of detector
  performance
• monitoring of trigger selectivity
• fast check of physics program
• Data rate reduction
• data volume reduction
• regions-of-interest and partial readout
• data compression
• event rate reduction
• (sub)-event reconstruction and event rejection
• pp program
• pile-up removal
• charged particle jet trigger

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Online event reconstruction

• Optimization and monitoring of detector
  performance
• see STAR online tracking
• Monitoring of trigger selectivity
• see STAR event rejection by Level-3 vertex
  determination
• Fast check of physics program
• see STAR peripheral physics program

has to be up and running on day 1
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Data rate reduction

• Volume reduction
• regions-of-interest and partial readout
• data compression
• entropy coder
• vector quantization
• TPC-data modeling
• Rate reduction
• (sub)-event reconstruction and event rejection
  before event building

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TPC event(only about 1 is shown)
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Regions-of-interest and partial readout

• Example selection of TPC sector and ?-slice
  based on TRD track candidate

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Data compressionEntropy coder
Probability distribution of 8-bit TPC data

• Variable Length Coding
• short codes for long codes for
• frequent values infrequent values
• Results
• NA49 compressed event size 72
• ALICE 65
• (Arne Wiebalck, diploma thesis, Heidelberg)

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Data compressionVector quantization

• Sequence of ADC-values on a pad vector

compare
code book

• Vector quantization transformation of
  vectors into codebook entries
• Quantization error

Results NA49 compressed event size 29
ALICE 48-64 (Arne Wiebalck, diploma
thesis, Heidelberg)
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Data compression TPC-data modeling

• Fast local pattern recognition
  

simple local track model (e.g. helix)
track parameters

• Track and cluster modeling

comparison to raw data
local track parameters
analytical cluster model
quantization of deviations from track and
cluster model
Result NA49 compressed event size 7
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Fast pattern recognition

• Essential part of Level-3 system
• crude complete event reconstruction
• ? monitoring
• redundant local tracklet finder for cluster
  evaluation ? efficient data compression
• selection of (?,?,pT)-slices
• ? ROI
• high precision tracking for selected track
  candidates
• jets, dielectrons, ...

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Fast pattern recognition

• Sequential approach
• cluster finder, vertex finder and track follower
• STAR code adapted to ALICE TPC
• reconstruction efficiency
• timing results
• Iterative feature extraction
• tracklet finder on raw data and cluster
  evaluation
• Hough transform

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Fast cluster finder (1)

• timing 5ms per padrow

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Fast cluster finder (2)
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Fast cluster finder (3)

• Efficiency

• Offline efficiency

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Fast vertex finder

• Resolution

• Timing result
• 19 msec on ALPHA (667 MHz)

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Fast track finder

• Tracking efficiency

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Fast track finder

• Timing results

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Hough transform (1)

• Data flow

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Hough transform (2)

• ?-slices

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Hough transform (3)

• Transformation and maxima search

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Level-3 system architecture
TPC sector 1
TPC sector 36
TRD
ITS
XYZ
ROI
local processing (subsector/sector)
data compression
global processing I (2x18 sectors)
Level-3 trigger
jets
dielectron verification event rejection
global processing II (detector merging)
global processing III (event reconstruction)
monitoring
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Level-3 implementation scenariosA
B
Detectors
Detectors
(sub)detector 1 2 n
Level-3
DAQ-EVB
event 1 2 n
Level-3
DAQ-EVB

• simple architecture
• trivial parallel processing
• throughput always limited to 10-20 Hz due to
  bandwidth limitation
• cannot fulfill all Level-3 requirements

• minimized data transfer
• scalable distributed computing farm (500-1000
  nodes network) would do the job

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Conclusion

• Need for online (crude/partial/sub) event
  reconstruction and event rejection
• Essential task fast pattern recognition (TPC)
• Distributed computing farm (500-1000 nodes) close
  to the detector readout would do the job