The ZEUS ong term data preservation project

Verbytskyi, A.

doi:10.22323/1.265.0264

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…The relations between the MC generated events and the description of the simulated processes is documented in dedicated internal ZEUS web pages and the lists of files belonging to each generated MC sample are also stored in a standalone sqlite3 database. The generation of small additional simulated data sets including detector simulation (level 4, through an encapsulated and/or containerized approach) is possible [60] and has been used successfully. Dedicated Code needed for the 'zevis' event display and for automated handling of the 'cninfo' data base (both very useful but not crucial) are available in github and are kept updated by MPP.…”

Section: Zeusmentioning

confidence: 99%

Data Preservation in High Energy Physics (DPHEP Global Report 2022)

Basaglia¹

2023

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This document summarizes the status of data preservation in high energy physics. The paradigms and the methodological advances are discussed from a perspective of more than ten years of experience with a structured effort at international level. The status and the scientific return related to the preservation of data accumulated at large collider experiments are presented, together with an account of ongoing efforts to ensure long-term analysis capabilities for ongoing and future experiments. Transverse projects aimed at generic solutions, most of which are specifically inspired by open science and FAIR principles, are presented as well. A prospective and an action plan are also indicated.

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Section: Zeusmentioning

confidence: 99%

Data Preservation in High Energy Physics (DPHEP Global Report 2022)

Basaglia¹

2023

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Deeply learning deep inelastic scattering kinematics

et al. 2022

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We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables $$Q^2$$ Q 2 and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection of a training set, the neural networks sufficiently surpass all classical reconstruction methods on most of the kinematic range considered. Rapid access to large samples of simulated data and the ability of neural networks to effectively extract information from large data sets, both suggest that deep learning techniques to reconstruct DIS kinematics can serve as a rigorous method to combine and outperform the classical reconstruction methods.

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Data preservation in high energy physics

Basaglia,

Bellis,

Blomer

et al. 2023

Eur. Phys. J. C

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Data preservation is a mandatory specification for any present and future experimental facility and it is a cost-effective way of doing fundamental research by exploiting unique data sets in the light of the continuously increasing theoretical understanding. This document summarizes the status of data preservation in high energy physics. The paradigms and the methodological advances are discussed from a perspective of more than ten years of experience with a structured effort at international level. The status and the scientific return related to the preservation of data accumulated at large collider experiments are presented, together with an account of ongoing efforts to ensure long-term analysis capabilities for ongoing and future experiments. Transverse projects aimed at generic solutions, most of which are specifically inspired by open science and FAIR principles, are presented as well. A prospective and an action plan are also indicated.

show abstract

The ZEUS ong term data preservation project

Cited by 4 publications

References 17 publications

Data Preservation in High Energy Physics (DPHEP Global Report 2022)

Data Preservation in High Energy Physics (DPHEP Global Report 2022)

Deeply learning deep inelastic scattering kinematics

Data preservation in high energy physics

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