Yadage and Packtivity - analysis preservation using parametrized workflows

Abstract

Preserving data analyses produced by the collaborations at LHC in a parametrized fashion is crucial in order to maintain reproducibility and re-usability. We argue for a declarative description in terms of individual processing steps - packtivities - linked through a dynamic directed acyclic graph (DAG) and present an initial set of JSON schemas for such a description and an implementation - yadage - capable of executing workflows of analysis preserved via Linux containers.

9 pages

References in corpus (3)

• Summary of the ATLAS experiment's sensitivity to supersymmetry after LHC Run 1 - interpreted in the phenomenological MSSM
• RECAST: Extending the Impact of Existing Analyses
• Dark matter interpretations of ATLAS searches for the electroweak production of supersymmetric particles in $\sqrt{s}=8$ TeV proton-proton collisions

Cited by in corpus (2)

• Deep Learning and its Application to LHC Physics
• Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider