ATLAS analysis workflows using the EventIndex and the Event Picking Server for massive event picking and enhanced processing

The ATLAS detector produces a wealth of information for each recorded event. Standard calibration and reconstruction procedures reduce this information to physics objects that can be used as input to most analyses; nevertheless, there are very specific analyses that need full information from some of the ATLAS subdetectors, or enhanced calibration and/or reconstruction algorithms. For these use cases, a novel workflow has been developed that involves the selection of events satisfying some basic criteria, their extraction in RAW data format using the EventIndex data catalogue and the Event Picking Server, and their specialised processing. This workflow allows us to commission and use new calibration and reconstruction techniques before launching the next full reprocessing (important given the longer and longer expected time between full reprocessing campaigns), to use algorithms and tools that are too CPU or disk intensive if run over all recorded events, and in the future to apply AI/ML methods that start from low-level information and could profit from rapid development/use cycles. This paper describes the tools involved, the procedures followed and the current operational performance.

Authors

Institutions

Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701347
Primary Topic
Particle Detector Development and Performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ATLAS analysis workflows using the EventIndex and the Event Picking Server for massive event picking and enhanced processing

Igor Alexandrov, D. Barberis, E. Alexandrov, Simone Pagan Griso et al.
EPJ Web of Conferences
Particle Detector Development and Performance
article

ATLAS analysis workflows using the EventIndex and the Event Picking Server for massive event picking and enhanced processing

Igor Alexandrov, D. Barberis, E. Alexandrov, Simone Pagan Griso, C. Dimitriadi, Alexander Iakovlev, Semen Turchikhin
article en

Abstract

The ATLAS detector produces a wealth of information for each recorded event. Standard calibration and reconstruction procedures reduce this information to physics objects that can be used as input to most analyses; nevertheless, there are very specific analyses that need full information from some of the ATLAS subdetectors, or enhanced calibration and/or reconstruction algorithms. For these use cases, a novel workflow has been developed that involves the selection of events satisfying some basic criteria, their extraction in RAW data format using the EventIndex data catalogue and the Event Picking Server, and their specialised processing. This workflow allows us to commission and use new calibration and reconstruction techniques before launching the next full reprocessing (important given the longer and longer expected time between full reprocessing campaigns), to use algorithms and tools that are too CPU or disk intensive if run over all recorded events, and in the future to apply AI/ML methods that start from low-level information and could profit from rapid development/use cycles. This paper describes the tools involved, the procedures followed and the current operational performance.

EPJ Web of ConferencesVol. 337
Lawrence Berkeley National Laboratory (US), University of Birmingham (GB), University of Genoa (IT), University of California, Berkeley (US)
Openalex Percentile: Top 87%
Particle Detector Development and Performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.