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May 8 – 12, 2023
Norfolk Waterside Marriott
US/Eastern timezone

DUNE Database Development

May 9, 2023, 3:15 PM
Hampton Roads Ballroom VI (Norfolk Waterside Marriott)

Hampton Roads Ballroom VI

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 3 - Offline Computing Track 3 - Offline Computing


Dr Vizcaya Hernandez, Ana Paula (Colorado State University)


The Deep Underground Neutrino Experiment (DUNE) is a long-baseline experiment which aims to study neutrino oscillation and astroparticle physics. It will produce vast amounts of metadata, which describe the data coming from the read-out of the primary DUNE detectors. Various databases will make up the overall DB architecture for this metadata. ProtoDUNE at CERN is the largest existing prototype for DUNE and serves as a testing ground for - among other things - possible database solutions for DUNE.
The subset of all metadata that is accessed during offline data reconstruction and analysis is referred to as ‘conditions data’ and it is stored in a dedicated database. As offline data reconstruction and analysis will be deployed on HTC and HPC resources, conditions data is expected to be accessed at very high rates. It is therefore crucial to store it in a granularity that matches the expected access patterns allowing for extensive caching. This requires a good understanding of the sources and use cases of conditions data. This contribution will briefly summarize the database architecture deployed at ProtoDUNE and explain the various sources of conditions data. We will present how the conditions data is retrieved from the run conditions and beam database; and how, together with the conditions data from the Detector Control System (Slow Controls) and those needed for the calibration of a LArTPC, are put in a format to match the expected access patterns.

Consider for long presentation No

Primary authors

Gerlach, Lino (Brookhaven National Laboratory) Dr Vizcaya Hernandez, Ana Paula (Colorado State University)

Presentation materials

Peer reviewing