presentation to the LHCC Comprehensive Review James R. Catmore - - PowerPoint PPT Presentation

Distributed physics analysis for ATLAS: presentation to the LHCC Comprehensive Review

James R. Catmore Research Associate, Lancaster University, UK ATLAS B-physics working group

ATLAS Analysis computing model 2

Detector

TIER 0 (CERN) and PRODUCTION SYSTEM (TIER 2 SITES)

ANALYSTS

ATLAS distributed analysis tools

• Distributed data management: DQ2
• Replica catalogue providing mappings to concrete

datasets on sites across the Grids

• Dataset: basic data unit for an analyst - may contain

thousands of files

• Tools (command line and web) for listing catalogue

content

• Metadata service: AMI
• Web interface giving information on file provenance
• Grid user interface: GANGA

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GANGA

• Grid user interface: GANGA
• Single tool for all Grid-based work (including analysis and small Monte Carlo

productions)

• Trivial switching between Grid running and local execution (for testing

purposes)

• Grid backends include LCG (EGEE), NorduGrid, OSG (Panda)
• Interface via a command-line or a GUI

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www.cern.ch/ganga

TAGs

• Event TAGs are the method by which reconstructed

events are selected for analysis

• Built from AOD according to offline analysis-style code
• Initially consists of files containing event metadata and a

pointer to the POOL file from which the tag was made

• Later loaded into a relational database for access by

physicists

• Typical information held in the TAG: temporal conditions,

quality & detector status, trigger, physics

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Analysts’ work-flow (data taking) 6

Prepare analysis code Prepare TAG selection Set up analysis job Submit to the Grid Retrieve results Merge results Inspect in ROOT

1 2 3 4 5 6 7

Analysts’ work-flow (now) 7

Prepare analysis code Locate dataset Set up analysis job Submit to the Grid Retrieve results Merge results Inspect in ROOT

1 2 3 4 5 6 7

Example for this presentation

• Submission of a simple Athena analysis job to the Grid using

GANGA

• Search for J/ψ→μμ decays in AOD using the Athena package

BPhysAnalysisTools

• Run over Monte Carlo AOD data sample produced by the

Production System

• Job goes to the data
• Physics analyses using exactly this method are in progress

now for commissioning preparation

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Step 1: prepare your analysis code 9

Step 2: Find your dataset in AMI (current) 10

Wildcards supported Dataset number Simulation chain step

☝

All official ATLAS MC data on the Grid uses a strict naming policy

AMI page: http://ami3.in2p3.fr:8080/opencms/opencms/AMI/www

Step 2: Find your dataset in AMI (current) 11

DQ2 link Dataset name Data type Athena release Status

Step 2: prepare TAG selection (data taking)

• Event Quantities
• Data Quality
• Trigger information
• Electron objects
• Photon objects
• Muon objects
• Tau-jet objects
• Jets
• Physics attributes

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Available for TAG selection

Developing the correct selection will be a major task and will probably involve several analysts or an entire physics group. Local testing will be required (local use

• f the TAGs is trivial)

Step 3: Set up analysis job.... GANGA window 13

Step 3: Set up analysis job.... GANGA window 14

Job control panel

New Open Submit Retrieve

• utput

Save Copy Kill Delete

Job details window

Step 3: Set up analysis job.... GANGA window 15

Job monitoring window Type Back-end ID Status

Step 3: Set up analysis job.... job builder 16

Athena Back-end control Datset/TAG Results

Where is the data.....? 17

Best sites are FZKDISK and LYONDISK: 592 files each

Where is the data? 18

Step 4: Submit your job.... 19

☝

Step 6: Monitor the job 20

after 5 minutes or so.....

Step 7: Retrieve and merge your results 21

☝

Retrieve Merge

Step 8: Collect results; analysis in ROOT 22

Producing Monte Carlo data in GANGA 23

• Physicists will often need to produce

quick small samples of Monte Carlo data (<10 000 events)

• GANGA provides a plug-in for

running the full Athena simulation chain

• Uses the standard ATLAS

JobTransformation mechanism used in the main production system to guarantee trustworthiness

• Naming abides by ATLAS conventions
• User-generated datasets saved on the

Grid and registered in DQ2 under

the user’s name

Monitoring 24

ATLAS dashboard - reached from GANGA web page

Conclusions and outlook

• ATLAS physicists are doing distributed physics analysis now on

Monte Carlo data

• TAGs are under preparation for data-taking
• Datasets are well documented in AMI but even replication across

the sites remains a problem

• The submission tools are sufficiently robust to handle this
• Heavy reliance on LXPLUS UI at the moment
• How will user support work when data-taking begins?
• ATLAS distributed computing will be ready for data

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