X. Wu, March 20061

ATLAS Egamma Trigger Overview

Xin WuUniversity of Geneva

X. Wu, March 20062

Outline

Introduction LVL1 EM Trigger LVL2 EM Trigger EF EM Trigger Overall Performance Online Integration Conclusion

X. Wu, March 20063

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, 220i)

• 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+220i: 27 Hz

TDAQ TDR

X. Wu, March 20064

LVL1 Calorimeter Trigger System

RxRx

Calorimeters

(LAr, Tile)Calorimeters

(LAr, Tile)

0.2x0.2

0.1x0.1400 Mb/s

analogue~75m

0.1x0.1RoI

Builder

L1 CTP

PreProcessorTiming alignment

10-bit FADCFIR filter

BCIDLUT

Sum 2x2 BC-MUX

PreProcessorTiming alignment

10-bit FADCFIR filter

BCIDLUT

Sum 2x2 BC-MUX

Jet/Energy ProcessorSum Em+Had

Jet/Energy ProcessorSum Em+Had

Jet identificationThreshold count

ET Ex, Ey

ET, ET

Cluster ProcessorRoI identification

e// classificationThreshold count

Cluster ProcessorRoI identification

e// classificationThreshold count

DAQ

X. Wu, March 20065

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

X. Wu, March 20066

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)

X. Wu, March 20067

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, …

X. Wu, March 20068

L2 Egamma Calorimeter Algorithm

0

Rcore= E3x7/E7X7 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 T2CaloEgammaat each step data request is made and

accept/reject decision is possible

X. Wu, March 20069

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

X. Wu, March 200610

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

X. Wu, March 200611

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 Conversion Fast Conversion

X. Wu, March 200612

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)

X. Wu, March 200613

EF Egamma Calorimeter Reconstruction

Wrap offline tools to EF environment (Cibran Santamarina) – Seeded approach, interface to trigger steering

TrigCaloRec

X. Wu, March 200614

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

X. Wu, March 200615

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

Step Eff (%) RateLVL1 96.7 5.6 kHzLVL1+LVL2+EF 80.3 42 HzLVL1+LVL2+EF+offline

73.5 34 Hz

LVL1+offline 76.1 73 HzStep Eff (%) RateLVL1 96.5 6 kHzoffline 83.9 180 HzLVL1+offline 81.8 78 HzLVL1+LVL2+offline 80.7 52 HzLVL1+LVL2+EF+offline

79.2 33 Hz

LVL1+EF 81.7 59 HzLVL1+LVL2+EF 80.7 40 Hz

Rome data

CSC validation data

Offline = isEM = 78%

X. Wu, March 200616

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

X. Wu, March 200617

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 SimOnline

Algorithms

Offline

GAUDI

ByteStream File (RDO)ByteStream File or Pool(RIO) File

ROS

X. Wu, March 200618

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