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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)

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Introduction to Geneva ATLAS High Level Trigger Activities

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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


The Challenge of Trigger at LHC

  • Bunch crossing40 MHz

  • σ total70 mb

  • Event rate~1 GHz

  • Number of event/BC~25

  • Number of part./event~1500

  • Event size~1.5MB

  • Mass storage rate~200Hz

Event rate 

Level-1 

Level-2 

Mass Storage 

Offline Analyses

  • Need to have Trigger of high performance

    • ~6 order of rate reduction

    • Complex event and 140 M channels


ROD

ROD

ROD

RoI’s

ROB

ROB

ROB

ROIB

L2SV

L2P

L2P

L2P

EB

EFN

EFP

EFP

EFP

Brief Introduction to the ATLAS Trigger System

Calo MuTrigDet

Other detectors

LVL1: Hardware Trigger

  • EM, TAU, JET calo. clusters

  • µ trigger chambers tracks

  • Total and missing energy

40 MHz

1 PB/s

Pipelines

2.5 ms

LVL1

2.5s

Muon

Trigger

Calorimeter

Trigger

LVL1 Acc.

100 kHz

CTP

HLT: PC farms

  • LVL2: special fast algorithms

    • Access data directly from the ROS system

    • Partial reconstruction seeded with L1 Regions of Interest (RoIs)

  • EF: offline reco. algorithms

    • Access to fully built event

    • Seeded with LVL2 objects (full event reconst. possible)

    • Up to date calibrations

120 GB/s

(Region of Interest)

H

L

T

RoI

requests

~40ms

ROS

LVL2

RoI data

L2N

3 GB/s

LVL2 Acc.

Event Builder

3 kHz

Event Filter

~4s

EF Acc.

200 Hz

Event Size ~1.5 MB

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)


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


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


Energy correction - Effects

From Olivier

  • Used to give the best energy resolution  Get the best efficiency

  • On set of parameters per  position

  • Energy calibration based on offline calibration:

    •  global factor (lateral leakage)

    • off : offset

    • wi: weights on pre-sampler and layer 3 energy

  • MZ reconstructed from electron pairs

    • - With energy correction

    • - Without energy correction


.Before correction

.After correction

S-shape correction study

From Olivier

Function proposed for this correction : Where

With

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 :

0.025<<0.05

  • Only 3 parameters left tabulated as

  • function of energy

  • An interpolation in energy is done

  • on the parameters


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


Steps of Online Integration

Offline Environment

Simulated Online Environment

DAQ Data Flow

athena

athenaMT/PT

L2PU

Steering Controller

Steering Controller

Steering Controller

Algorithms

Algorithms

Algorithms

1) Test offline

  • RDO input

  • Raw (BS) input

  • 2) Test with athenaMT

    • simulate online

    • BS input

    • use TDAQ release

  • 3) Test at Point 1

    • actual DAQ

    • BS input (through ROS)


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

  • 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 requests per event

Data collection time per event

mean = 5.3

mean = 6.0 ms


Express Stream (Szymon)

  • ATLAS data streams

Calibration streams contain incomplete events.

Complete physics events used for calibration are in the Express.


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).


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


g

p0

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

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

EtHad=Hadronic Energy (Tile or HEC)


Calorimeter Timing Results from the May TR

TrigCaloCellMaker

T2CaloEgamma

mean 16ms / RoI

mean 6.2ms / RoI

TrigCaloTowerMaker

TrigCaloClusterMaker

mean 27ms / RoI

mean 65ms /RoI


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