arXiv subjects

Luke Kreczko

Publications and source records attributed to Luke Kreczko.

3 recordsLinked to original sources

arXiv2026

FAST-HEP: Compiling Declarative Analysis Workflows for High-Energy Physics and Beyond

High-energy physics analyses increasingly rely on complex software workflows whose scientific lifetime often exceeds that of the underlying software ecosystem. Maintaining reproducibility while accommodating evolving analysis software, data formats, and execution environments therefore remains a significant challenge. These challenges are not unique to high-energy physics and are shared by many data-intensive scientific analyses. We present FAST-HEP and its workflow engine, Flow, which combines a declarative workflow language, compiler, and runtime. Flow separates the scientific description of a workflow from its implementation and execution, and compiles workflows into backend-independent execution plans through normalization, graph construction, dependency analysis, and execution planning. A common runtime then orchestrates the resulting plan using replaceable capabilities. This architecture enables static validation and modular replacement of analysis operations, execution backends, and storage technologies, while recording provenance throughout compilation and execution. Although developed for the requirements of high-energy physics analysis, Flow's workflow model and orchestration layer are domain-independent. By applying compiler techniques to scientific analysis workflows, FAST-HEP provides a foundation for workflows that are transparent, extensible, portable, and reproducible, allowing scientific analyses and their supporting software ecosystems to evolve independently.

hep-ex↗

arXiv2022

Second Analysis Ecosystem Workshop Report

The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. The workshop was themed around six particular topics, which were felt to capture key questions, opportunities and challenges. Each topic arranged a plenary session introduction, often with speakers summarising the state-of-the art and the next steps for analysis. This was then followed by parallel sessions, which were much more discussion focused, and where attendees could grapple with the challenges and propose solutions that could be tried. Where there was significant overlap between topics, a joint discussion between them was arranged. In the weeks following the workshop the session conveners wrote this document, which is a summary of the main discussions, the key points raised and the conclusions and outcomes. The document was circulated amongst the participants for comments before being finalised here.

hep-ex↗

arXiv2019

Machine Learning in High Energy Physics Community White Paper

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

physics.comp-ph↗