A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

cms-l1t-offline/cms-l1t-analysis: Alpha v0.1.0

Include Shane's macros as 'legacy' for comparison Read L1TNtuples in python Transfer 1 Macro to python (makeJetResolutions) Benchmark legacy vs new Add histogram collections for easier creation & handling Added multidimensional dictionary based on defaultdict Added HistogramByPileUpCollection Automatic selection of PU bin based on pileup value. E.g. histograms[11] will fill the 2nd bin if pileupBins=[0,10,20,30,999] Added ResolutionCollection Specialisation of HistogramByPileUpCollection Automatic selection of detector region based on cmsl1t.geometry Implement Ben's MET turnons to check if the package is going the right way Explore ways to recalculate MET Implement TurnOnCollection EfficiencyCollectio

BristolTopGroup/DailyPythonScripts: Last release before becoming a real python package

Everything changed after thi

delphes/delphes: Delphes-3.5.1pre10

<ul> <li>added vertexing with neutrals (thanks to @fbedesch)</li> <li>added Scenario II FCC-hh card (nominal)</li> <li>added CLD card</li> </ul&gt

Second Analysis Ecosystem Workshop Report

International audienceThe 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

Second Analysis Ecosystem Workshop Report

International audienceThe 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

Machine Learning in High Energy Physics Community White Paper

peer reviewedMachine learning is an important research area in particle physics, 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 in machine learning in particle physics with 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