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					Introduction to Geneva ATLAS High
        Level Trigger Activities
                         Xin Wu

   Journée de réflexion du DPNC, 11 septembre, 2007

                         Participants
   Assitant(e)s: Gauthier Alexandre, Francesca Bucci,
                 Till Eifert, Clemencia Mora
   MA:           Olivier Gaumer, Andrew Hamilton,
                 Phillip Urquijo (20/09/07)
   Physiciens: Szymon Gadomski, Xin Wu                  1
The Challenge of Trigger at LHC
                                Bunch crossing              40 MHz
                                σ total                     70 mb
             Event rate 
                                Event rate                  ~1 GHz
                                Number of event/BC          ~25
                                Number of part./event       ~1500
                Level-1        Event size                  ~1.5MB
                                Mass storage rate           ~200Hz
               Level-2 

         Mass Storage 




       Offline Analyses




                             Need to have Trigger of high performance
                                ~6 order of rate reduction
                                Complex event and 140 M channels
                                                                2
    Brief Introduction to the ATLAS Trigger System
                                                                           Calo
                                                                         MuTrigDet Other detectors
  LVL1: Hardware Trigger                                        40 MHz
                                                                                              1 PB/s
  EM, TAU, JET calo. clusters                                   Pipelines
                                      LVL1              2.5 s    2.5 s
  µ trigger chambers tracks          Calorimeter      Muon
                                        Trigger       Trigger    LVL1 Acc.
  Total and missing energy
                                                CTP
                                                                 100 kHz     ROD   ROD       ROD


  HLT: PC farms                          RoI’s (Region of Interest)                       120 GB/s

 LVL2: special fast algorithms         LVL2       ~40ms     RoI
                                                           requests
                                                                             ROB    ROB      ROB
    Access data directly from             ROIB     L2SV

      the ROS system                                                         ROS
                                     H L2P                RoI data
    Partial reconstruction               L2P        L2N
                                                                                          3 GB/s
                                            L2P
      seeded with L1 Regions of L                         LVL2 Acc.
                                                                             Event Builder
      Interest (RoIs)                                     3 kHz
                                     T Event Filter
                                                                                    EB
 EF: offline reco. algorithms                        ~4s
                                           EFP
    Access to fully built event             EFP
                                               EFP
                                                            EF Acc.
                                                                                    EFN
    Seeded with LVL2 objects
                                                          200 Hz
      (full event reconst. possible)
    Up to date calibrations           Event Size ~1.5 MB                               3
                                                                                   300 MB/s
Geneva’s Participation in High Level Trigger
 Calorimeter Trigger Software (Gauthier, Olivier, Xin)
    Overall coordination
    LVL2 calorimeter cluster correction
 HLT Steering Controller (Till)
    Control the complex algorithm scheduling for ROI based
     reconstruction and Stepwise processing for early rejection
     (see Till’s talk)
 Online integration of the HLT algorithms (Xin)
    Integrate the HLT algorithms developed offline into the DAQ
     online running environment
 Trigger Event Data Model (Andrew, Francesca)
    Manage trigger objects stored in data (see Andrew’s talk)
 EF tracking software (Andrew, Francesca)
    Adapt offline track reconstruction for EF (see Andrew’s talk)
 Express stream (Syzmon)
    Special data stream for fast reconstruction
 ATLAS Trigger Coordination (Xin)                           4
Calorimeter Trigger Software
 Collaborative effort of many people
    Common first steps for all the “slices”: electron, photon,
     jet, tau, missing energy
 LVL1 hardware simulation
 Calorimeter RegionSelector
    Mapping between detector elements and - region for
     using Region of Interest
 Calorimeter data preparation
    Fast raw data unpacking
 LVL2 calorimeter reconstruction
    Specific fast clustering algorithms
 LVL2 cluster calibration
    Energy correction, position correction, crack correction,…
 Event Filter calorimeter reconstruction
    Adapt offline algorithms for EF
 Overall coordination                                        5
L2 EM Cluster Corrections (Olivier, Gauthier)
 Lateral energy correction
    Better Energy evaluation (10% effect)
 S-shape correction (sampling 2)
    Better position reconstruction
 Longitudinal energy correction : Material and leakage
    Better energy resolution
 Energy  correction and  correction + accordion modulations
  for different clusters
 Crack corrections (local correction)
     = 0.8 : crack between the two electrodes of the barrel
     = 1.4 : crack between barrel and end-cap
 Currently first 2 corrections implemented using offline
  constants
    Study effect on trigger in progress

                                                           6
                                                             From Olivier

          Energy correction - Effects
    Energy calibration based on offline                     Used to give the best
     calibration:                                             energy resolution  Get
                                                              the best efficiency
         global factor (lateral leakage)                   On set of parameters per 
        off : offset                                         position
        wi: weights on pre-sampler and layer 3 energy




    MZ reconstructed from electron pairs
        - With energy correction
        - Without energy correction




                                                                                   7
                                                                            From Olivier

           S-shape correction study
Function proposed for this correction :                      corr    f (u )   Where   1  u  1
                                                    With                       
                                                            f (u )   0 arctan( 1u )   2 u   3 u   4
This function is actually modified to ensure the continuity at |u|=1
The variables are redefined to remove correlations between them
At the end the actual function used is :
                                                       
             u arctan( )  arctan( u )               
f (u )  0            1            1
                                            2 (1  u )                0.025<<0.05
             Z arctan( )  arctan(Z )                 
             1          1                             
                                                       
           1
Z                  1
      arctan(1 )
                                                                                     . Before correction
 Only 3 parameters left tabulated as
 function of energy                                                                 . After correction
 An interpolation in energy is done
 on the parameters
                                                                                                              8
Online Integration of HLT Algorithms
 Integrate the HLT algorithms developed offline into the DAQ online
  running environment
 HLT algorithms developed in the offline framework because they use
  many offline reconstruction tools (more on EF, less on LVL2)
    Read MC pool RDO files and use transient BS
    Run together with Reconstruction
    Well suited (fast turn-around) for trigger performance studies
 Online running is quite different from offline
    Transition controlled by DataFlow software rather than Athena
    Read ByteStream raw data from ROS through DAQ
    Need to interface to online monitoring/error reporting tools
    Need to be thread-safe for multithreaded running
 Online integration involves many components of the HLT:
    Algorithms, trigger configuration, database, Steering Controller,
      Data Collection, …
    Follow through integration steps from offline, quasi-online
      (Athena MT/PT) tests all the way up till final online validation at
      point-1                                                           9
    Steps of Online Integration

       Offline             Simulated Online       DAQ Data Flow
     Environment             Environment

          athena            athenaMT/PT               L2PU

       Steering                Steering              Steering
      Controller              Controller            Controller

      Algorithms             Algorithms            Algorithms



1) Testoffline         2) Test with athenaMT  3) Test at Point 1
    – RDO input            – simulate online      – actual DAQ
    – Raw (BS) input       – BS input             – BS input
                           – use TDAQ release       (through ROS)

                                                                 10
DAQ/HLT Technical Runs
 Dedicated Technical Runs (1 week each) are used to test DAQ/HLT
  and HLT algorithm integration
    So far two in 2007 (March and May). Next in end of September
 Brief Summary of the May TR (21/5-25/5)
    ‘Final’ Hardware
        • ROIB (+ LVL1 emulator), 120 ROSs
        • 4 HLT racks (130 dual quad-core 1.8 GHz), ~5% final system
    tdaq-01-07-00, AtlasHLT 2.0.5-HLT, Offline 12.0.5-HLT-1
    All basic HLT slices integrated
        • e10, g10, mu6, tau10, jet20, cosmic, Bphysics, met
        • combined : e10+g10+mu6+tau10+jet20
    ~ 6k events (mixed physics processes, ~60% jets and ~40% W/Z)
 Main achievement :
    Validated TDAQ and HLT infrastructure with final hardware
    Measurements with dummy algorithm LVL2 and EF with final
     hardware
    Functionality test with combined algorithm
    Tested DBProxy and triggerDB configuration
                                                                 11
 Next Technical Run: Sept 24-30
LVL2 Timing for Rejected Events


   Total time per event           Processing time per event
    mean = 31.5 ms                 mean = 25.7 ms




Data collection time per event   Data requests per event
mean = 6.0 ms                        mean = 5.3




                                                           12
Express Stream (Szymon)
 ATLAS data streams




Calibration streams contain incomplete events.
Complete physics events used for calibration are in the Express.
                                                            13
                                      From Szymon

 Express Stream of ATLAS data
What is the Express Stream
• One of the data streams produced by ATLAS online,
  O(10%) of the physics data.
• To be reconstructed and looked at rapidly. Results in a
  few hours, before the reconstruction starts.
• Calibration, check of data quality, monitoring of the
  detector status, rapid alert on interesting events…
Role of Geneva
• S.Gadomski coordinates the work on the trigger menu.
• Trigger rates are calculated on Swiss ATLAS Grid
  resources, in collaboration with Bern (Sigve Haug).

                                                            14
Conclusion
 ATLAS HLT project is in good progress
    Trigger algorithm development in advanced stage
    Trigger menu for early data-taking being completed
    HLT being integrated online and performance being
     studied in Technical Runs
 Over the pas year Geneva expanded its effort in the ATLAS
  High Level Trigger and made many important contributions
 We are becoming key players in several areas
    Calorimeter Trigger Software, Steering, EDM, Online
     Integration, Express Stream, Trigger Coordination
    See Till and Andrew talks for some more details
 Expertise in HLT is a great advantage for the group to access
  and understand real data at the earliest stage



                                                            15
LVL2 Egamma Reconstruction Algorithm

                4 Processing steps of T2CaloEgamma
                at each step data request is made and
                   accept/reject decision is possible

                                   Rcore= E3x7/E7X7
                                  in EM Sampling 2



                               Eratio=(E1-E2)/(E1+E2)
                                  in EM Sampling 1

       p0            g
                               EtEm=Total EM Energy
                               (add sampling 0 and 3)


                               EtHad=Hadronic Energy
                                   (Tile or HEC)

                                                        16
Calorimeter Timing Results from the May TR

  T2CaloEgamma               TrigCaloCellMaker
                              mean 16ms / RoI
          mean 6.2ms / RoI




TrigCaloTowerMaker
                                 TrigCaloClusterMaker
  mean 27ms / RoI
                                     mean 65ms /RoI




                                                        17

				
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