1

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

2

The Challenge of Trigger at LHC

Bunch crossing 40 MHz σ total 70 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-2

Level-1

Offline Analyses

Mass Storage

Need to have Trigger of high performance ~6 order of rate reduction Complex event and 140 M channels

3

Brief Introduction to the ATLAS Trigger System

LVL1: Hardware Trigger EM, TAU, JET calo. clusters µ trigger chambers tracks Total and missing energy

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

H

L

T

100 kHz

3 kHz

200 Hz

40 MHz

RoI data

LVL1 Acc.

ROD ROD ROD

LVL1 2.5 sCalorimeter

TriggerMuon

Trigger

Event Builder

EB

3 GB/s

ROS

ROB ROB ROB

120 GB/s

Calo MuTrigDet Other detectors

1 PB/s

Event Filter

EFPEFP

EFP EFN

300 MB/s

LVL2 ~40ms

L2P

L2SV

L2NL2PL2P

ROIB

LVL2 Acc.

RoI’s

Event Size ~1.5 MB

CTP

Pipelines2.5 s

EF Acc.

(Region of Interest)

RoI reques

ts

~4s

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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)

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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

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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

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Energy correction - Effects 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

Used to give the best energy resolution Get the best efficiency

On set of parameters per position

From Olivier

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S-shape correction studyFunction 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 :

43210 )arctan()(

)(

uuuuf

ufcorr 11 u

1)arctan(

)1()arctan()arctan(

)arctan()arctan()(

1

1

2

11

110

Z

uZ

Zuu

uf

Only 3 parameters left tabulated as function of energy An interpolation in energy is done on the parameters

. Before correction

. After correction

0.025<<0.05

From Olivier

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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

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Steps of Online Integration

Simulated Online Environment

1) Test offline – RDO input– Raw (BS) input

Offline Environment

athena

Steering Controller

Algorithms

2) Test with athenaMT– simulate online– BS input– use TDAQ release

3) Test at Point 1– actual DAQ– BS input

(through ROS)

athenaMT/PT

Steering Controller

Algorithms

DAQ Data Flow

L2PU

Steering Controller

Algorithms

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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

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LVL2 Timing for Rejected Events

mean = 5.3mean = 6.0 ms

Data requests per event

Data collection time per event

mean = 31.5 ms

Total time per event Processing time per event

mean = 25.7 ms

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Express Stream (Szymon)

ATLAS data streams

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

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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).

From Szymon

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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

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LVL2 Egamma Reconstruction 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

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Calorimeter Timing Results from the May TR

mean 27ms / RoI mean 65ms /RoI

TrigCaloCellMaker

TrigCaloTowerMakerTrigCaloClusterMaker

mean 16ms / RoI

T2CaloEgamma

mean 6.2ms / RoI