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The ATLAS Experiment's main offline software repository

The ATLAS Experiment's main JobOptions repository for MC production using Release 21.

Configuration and tests for the LHCb High Level Trigger application Moore. Documentation can be found here.

The LHCb project contains general purpose classes used throughout the LHCb software. It is built on top of the Gaudi framework. Documentation is available at http://cern.ch/lhcbdoc/lhcb

Full software HLT1 reconstruction sequence on GPU.

Yet Another Rapid Readout

This project is built on top of the Gaudi, LHCb and Lbcom projects. It contains components related to reconstruction. See documentation at http://cern.ch/lhcbdoc/rec/

The Gaudi project is an open project for providing the necessary interfaces and services for building HEP experiment frameworks in the domain of event data processing applications. The Gaudi framework is experiment independent.

This repository hosts the simulation software of the LHCb experiment.

Documentation can be found at https://lhcb-gauss.docs.cern.ch/master/ and for Sim10 at http://cern.ch/lhcbdoc/gauss/

A JIRA connected project is available at https://its.cern.ch/jira/projects/LHCBGAUSS

For general information on Simulation refer to https://lhcb-simulation.web.cern.ch/index.html

An AthAnalysis framework, focusing on physics analysis combining runs 2 and 3

ATLAS Experiment main repository for external projects

CMS Tracker Phase2 Acquisition & Control Framework: https://ph2acf.docs.cern.ch/

LHCb offline physics analysis software main project.

The documentation for run 3 is at https://lhcb-davinci.docs.cern.ch/

Release notes and links to all documentation, including that for legacy data, is at http://cern.ch/lhcbdoc/davinci/.

This is the FTAG (training) dataset dumpster (aka TDD). We use it to run away from the ATLAS EDM into the land of H5.

EOS project.

Sub-project part of the LHCb physics analysis software project DaVinci. See http://cern.ch/lhcbdoc/phys/ for details.

Description of the LHCb Upgrade detector using the DD4hep framework

Multimodal and Multiclass Machine Learning for High Energy Physics