Public Documentation for US ATLAS Analysis Facilities

US ATLAS hosts three shared Tier 3 computing spaces at BNL, SLAC, and UChicago, also known as Analysis Facilities (AF). These three facilities are available to all US ATLAS physicists and computer scientists. They are organized and managed to support US ATLAS users' need for computing resources including login, run interactive and batch jobs, access ATLAS data, store private data, etc.

The AFs also support a wide variety of tools specific for analysis, including ATLAS/CERN software in CVMFS, Grid middleware, Rucio clients, Machine Learning packages, MPI, Jupyter Lab with PyROOT, Xcache with auto data discovery, GPUs, etc.

The three facilities are backed by staff to support software environments, unix systems and storage.

Need help? Have questions or comments?, Visit our ATLAS AF Discourse Forum (do not confuse with Discord 👾) for user support, contact, friendly discussion, newsletter and more! We'd love to help you have a smooth experience while working at our analysis facilities!

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This documentation includes the following:

• User Onboarding: details the process of applying for user accounts at BNL, SLAC, and UChicago
• Quickstart Guides: walkthroughs for accessing the AFs
• Machine Learning Containers: information and use of ML containers
• Data Storing, Accessing, and Sharing: explains the ways users can use their ATLAS data at AFs
• Jupyter at Analysis Facilities: highlights the different aspects of Jupyter and how to use it at AFs
• Data Analysis Tutorials: step-by-step tutorials on using AFs for analyses
• Containers: detailed information on container-based data processing and how to use them at AFs
• Using VSCode: detailed guide on using Visual Studio Code.
• FAQ: answers to frequently asked questions