Abstract
We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing analysis computational tasks. The analysis process can be structured in the form of a Directed Acyclic Graph (DAG), where each graph vertex represents a unit of computation with its inputs and outputs, and the graph edges describe the interconnection of various computational steps. We have developed REANA, a platform for reproducible data analyses, that supports several such DAG workflow specifications. The REANA platform parses the analysis workflow and dispatches its computational steps to various supported computing backends (Kubernetes, HTCondor, Slurm). The focus on declarative rather than imperative programming enables researchers to concentrate on the problem domain at hand without having to think about implementation details such as scalable job orchestration. The declarative programming approach is further exemplified by a multi-level job cascading paradigm that was implemented in the Yadage workflow specification language. We present two recent LHC particle physics analyses, ATLAS searches for dark matter and CMS jet energy correction pipelines, where the declarative approach was successfully applied. We argue that the declarative approach to data analyses, combined with recent advancements in container technology, facilitates the portability of computational data analyses to various compute backends, enhancing the reproducibility and the knowledge preservation behind particle physics data analyses.
Highlights
Data analysis in experimental particle physics involves studying the result of particle collisions in detectors and comparing experimental results with theoretical models
The computations in data analysis workflows can be represented as Directed Acyclic Graphs (DAG), where each graph vertex represents a unit of computation with its inputs and outputs and the graph edges describe the interconnection of various computational steps
The workflows are preserved in such a way that the simulated events that would be produced at the LHC and measured by the ATLAS detector according to the model(s) of new physics used to interpret the original search can be trivially replaced in the workflow with events generated using an alternative model
Summary
Data analysis in experimental particle physics involves studying the result of particle collisions in detectors and comparing experimental results with theoretical models. In the data-taking stage, the data are filtered by selecting events of interest using a multi-tiered trigger system reconstructing physics objects with increasing precision. In the following processing stage, the collision data are fully reconstructed, in many cases re-processed to profit from later improvements, and subsequently reduced into a format suitable for studying individual event signatures. Comparison to theoretical models is performed by generating events using Monte Carlo generator programs and simulating interaction with the detector.
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