1. X. Wu, March 2006 1 ATLAS Egamma Trigger Overview Xin Wu University of Geneva

2. X. Wu, March 2006 2 Outline  Introduction  LVL1 EM Trigger  LVL2 EM Trigger  EF EM Trigger  Overall Performance  Online Integration  Conclusion

3. X. Wu, March 2006 3 Introduction  Egamma Trigger: online selection of electrons and photons –LVL1: hardware processors to reconstruct (isolated) EM cluster –LVL2: Seeded fast Athena clustering and tracking algorithms –EF: (seeded) offline clustering and tracking algorithms  Responsible for a large fraction of data for ATLAS physics –Inclusive electron, dielectron (e25i, 2e15i) Main triggers for W, Z, dibosons, top, Higgs, SUSY, Exotics –Inclusive photon, diphoton (  60i, 2  20i) Main triggers for direct photon, H , Exotics –Exclusive (combination and topological) triggers  Dominant contributor to the trigger rate –~65% of LVL1 rate at L=2E33 Total LVL1: 25 KHz; EM25I: 12 kHz; 2EM15I: 4 kHz –~35% of EF rate at L=2E33 Total EF: 200 Hz; e25i+2e15i: 41 Hz;  60i+2  20i: 27 Hz TDAQ TDR

4. X. Wu, March 2006 4 LVL1 Calorimeter Trigger System Rx   Calorimeters (LAr, Tile) 0.2x0.2 0.1x0.1 400 Mb/s analogue ~75m 0.1x0.1 RoI Builder L1 CTP PreProcessor Timing alignment 10-bit FADC FIR filter BCID LUT Sum 2x2 BC-MUX PreProcessor Timing alignment 10-bit FADC FIR filter BCID LUT Sum 2x2 BC-MUX Jet/Energy Processor Sum Em+Had Jet/Energy Processor Sum Em+Had Jet identification Threshold count E T  Ex, Ey  E T, E T Cluster Processor RoI identification e/  /  classification Threshold count Cluster Processor RoI identification e/  /  classification Threshold count DAQ

5. X. Wu, March 2006 5 LVL1 EM RoI Reconstruction  RoI EM Core: a 0.2x0.2 local EM Et maximum  EM Cluster: most energetic of the four 2-tower EM clusters in th RoI Cluster –Et : LVL1 EM cluster Et  EM isolation –Total Et of the 12 EM towers around the RoI Cluster  Hadronic core isolation –Total Et of the 4 hadronic towers behind the RoI Core  Hadronic ring isolation –Total Et of the 12 hadronic towers around the RoI Core RoI Core Em Cluster EM Isolation TriggerTower 0.1x0.1 HAD core Isolation HAD ring Isolation

6. X. Wu, March 2006 6 LVL1 Calorimeter Simulation Software  Analog tower sum simulation –Need to be run at digitization stage –LArL1Sim : make LArTTL1 objects from hits (Fabienne Ledroit) –TileHitToTTL1 : make TileTTL1 from hits  TrigT1Calo : trigger tower digitization and RoI building –Use either TTL1 or Cells as input –Can be run at digitization or reconstruction stage –Make TriggerTower, EmTauROI, JetROI, EnergyRoI objects –Provide simulated input (RoI’s) to HLT Starting point for all efficiency/rate numbers !  CTPsim : make L1 decisions for a given L1 menu  EDM in ESD/AOD –TriggerTowers –L1EMTauObjectContainer: collection of LVL1 EM clusters –LVL1_ROI: collection of LVL1 RoIs ( , , threshold passed)

7. X. Wu, March 2006 7 LVL1 Egamma Performance  Benchmark numbers frequently updated with MC production and reconstruction releases –Eg. EM25i (M. Wielers) Rome data: eff=96.7%, rate 5.6 kHz (L=1E33) CSC validation: eff=96.5%, rate 6.0 kHz (L=1E33)  Detailed studies will be done with CSC data –Efficiency turn-on, noise effects, algorithm bias, dependence of isolation on event topology, …  Full characterization of LVL1 with data has high priority at the beginning of data taking –Tower noise threshold: 250 MeV steps –Isolation cut: HAD core, HAD ring, EM ring –Energy scale: 1 GeV or 500 MeV or 250 MeV –Efficiency turn-on –Clustering algorithm tuning, …

8. X. Wu, March 2006 8 L2 Egamma Calorimeter Algorithm  00 Rcore= E 3x7 /E 7X7 in EM Sampling 2 Eratio=(E1-E2)/(E1+E2) in EM Sampling 1 EtEm=Total EM Energy (add sampling 0 and 3) EtHad=Hadronic Energy (Tile or HEC) 4 Processing steps of T2CaloEgamma at each step data request is made and accept/reject decision is possible

9. X. Wu, March 2006 9 L2 Egamma Cluster Reconstruction  Samp2Fex : in sampling 2 –Find seed cell: hottest cell in the 0.2x0.2 window around LVL1 RoI –sum E in 3*7 and 7*7 cells windows around seed  Rcore –Cluster center = E weighted eta, phi in a 3x7 window around seed –Cluster is a 3x7 window around the new cluster center  Samp1Fex: in sampling 1 (strips) –Update cluster energy –Find max E and second max E strips in a window of 0.125x0.196 around cluster center  Eratio  SamEnEmFex –Update cluster energy with sampling 0 and 3 cells –Energy correction applied  EtEm  SamEnHadFex –Calculate sum E of HEC or Tile in 0.1*0.1 window around cluster center  EtHad

10. X. Wu, March 2006 10 L2 Egamma Calo. Data Preparation  RegionSelector –Return list of cells and ROB’s in the RoI window Initialization from LAr/Tile Geometry (F. Ledroit)  Retrieve ROB data –2 GB/s link ROS  LVL2  ByteStream data conversion (the main bottle beck) –Coupled tightly to ROD data format, DSP processing Continuous optimization (B. Laforge, D. Fournier, …) –Dedicated LVL2 ByteStream conversion (D. Damazio) Cell memory allocated and geometry initialized during initialization Organize cells in TT (Trigger Tower) Modified decoding method –Factor of 6 faster than offline BS conversion  Not yet investigated –Handle dead/noise cells and timing information –Performance study with respect to zero suppression

11. X. Wu, March 2006 11 L2 Egamma Calo. Timing Performance  Fast conversion will become default for release 12 and 11.0.6 –Validation with physics performance  Further improvements –exploit the new ROD data format (B. Laforge) fixed length block structure, hot cell index,... –use of faster/smaller LArCell (D. Damazio)  A LVL2 Egamma Calo. code review is being planned for May-July D. Damazio Offline ConversionFast Conversion

12. X. Wu, March 2006 12 LVL2 Tracking Algorithms  Seeded with LVL2 calo clusters –Search window 0.2x0.2 (could be narrowed by better Z position from T2Calo using strips)  2 independent tacking algorithms with Pixel and SCT –IDScan: histogram method for pattern recognition; Kalman filter for track fitting Total execution time ~4.1 ms (DataPrep ~3.5ms) –SiTrack: LUT method for finding triplet track segments straight line (R/Z) and circle (R/Phi) track fitting  Tool for track extension to TRT: TrigTRT_TrackExtensionTool –Use Probabilistic Data Association Filter ~ 1 ms/track + DataPrep  TRT standalone and full Inner Detector tracking –TRTxK: wrapper for the offline tool Xkalman Total TRT execution time ~4.6 ms (DataPrep ~2ms)

13. X. Wu, March 2006 13 EF Egamma Calorimeter Reconstruction  Wrap offline tools to EF environment (Cibran Santamarina) –Seeded approach, interface to trigger steering TrigCaloRec

14. X. Wu, March 2006 14 EF Egamma Tracking Reconstruction  Wrap offline newTracking tools (I. Grabowska-Bold) –All EF ID algorithms available since release 11.0.0  The full Egamma slice is running on BS input with 11.0.5 nightlies

15. X. Wu, March 2006 15 Overall Egamma Performance  Many studies and optimizations have been done with Rome data and are being repeated for CSC data –Eg. e25i for 1E33 from M. Wielers, crack region excluded StepEff (%)Rate LVL196.75.6 kHz LVL1+LVL2+EF80.342 Hz LVL1+LVL2+EF+offline73.534 Hz LVL1+offline76.173 Hz StepEff (%)Rate LVL196.56 kHz offline83.9180 Hz LVL1+offline81.878 Hz LVL1+LVL2+offline80.752 Hz LVL1+LVL2+EF+offline79.233 Hz LVL1+EF81.759 Hz LVL1+LVL2+EF80.740 Hz Rome data CSC validation data Offline = isEM = 78%

16. X. Wu, March 2006 16 Comment on Overall Performance  Performance numbers are only indicative due the fast evolution of software (trigger and offline)  Studies need to couple tightly with offline Egamma reconstruction (not always easy!)  Equally important and more challenging is to understand all individual variables –Geometrical, physical and topological bias –robustness against noise –efficiency calculation with data –Simplicity from the point of view of MC simulation, offline reconstruction and real data verification –correction and calibration  The final optimization can only be done with data –Get tools ready

17. X. Wu, March 2006 17 ATHENA Environment HLT integration: Online vs. Online Simulaton vs. Offline DAQ Data Flow L2PU/EFPT Steering Controller Algorithms GAUDI with support for multiple threads ATHENA Environment athenaMT/PT Steering Controller Algorithms Online Sim Online Algorithms Offline GAUDI ByteStream File (RDO) ByteStream File or Pool(RIO) File ROS

18. X. Wu, March 2006 18 Conclusions  Full HLT Egamma slice has been implemented –Basic functionalities and performance satisfactory –Great progresses have been made on more technical areas LVL2 data preparation, EDM, EF wrappers, athenaMT, …  Next –Validation and performance studies with CSC samples –Integration on HLT pre-series with 11.0.6 –Correction and calibration schemes; Monitoring –Algorithm reviews and improvements –Trigger menu for L=1E31 Benchmark physics channels (W, Z, top, DY, Diboson, direct , searches, …) –“Trigger-aware” analyses (physics groups) Startup scenario for Egamma slice Trigger/data sample/physics channel for Egamma verification, optimization and efficiency calculation –Tools for trigger commissioning with data