ATLAS Open Data Derivation Framework

Welcome to the ATLAS Open Data Derivation Tool! Physlitetoopendata is a C++ framework devoted to skimming the PHYSLITE files used for research to create flat ROOT NTuples used for educational purposes.

📌 What is this framework for?

Physlitetoopendata is designed to:

• Process datasets in the PHYSLITE format.
• Produce ROOT NTuples that retain essential information for analysis.
• Execute workflows either on the Worldwide LHC Computing Grid or locally.

Useful Features

• Accessible: You can install the framework locally or use our Docker images.
• Versatile: Useful for both educational and professional research purposes.

🔍 What does the framework do?

This framework automates the skimming of PHYSLITE datasets into ROOT NTuples, including the following objects in the output datasets:

• Lepton data: Some of the stored branches for leptons would be the number of leptons (lep_n), transverse momentums (lep_pt), pseudo-rapidities (lep_eta), azimuthal angles (lep_phi), charges (lep_charge) and their energies (lep_e).
• Tau data: Some of the stored branches for tau-leptons would be the number of taus (tau_n), transverse momentums (tau_pt), pseudo-rapidities (tau_eta), azimuthal angles (tau_phi), charges (tau_charge) and their energies (tau_e).
• Jets data: Some of the stored branches for jets would be the number of jets (jet_n), transverse momentums (jet_pt), pseudo-rapidities (jet_eta), azimuthal angles (jet_phi) and their energies (jet_e).
• Photons data: Some of the stored branches for photons would be the number of photons (photon_n), transverse momentums (photon_pt), pseudo-rapidities (photon_eta), azimuthal angles (photon_phi) and their energies (photon_e).
• Relevant Scale Factors: Scale factors that account for the efficiencies of many objects, such as electrons (ScaleFactor_ELE), muons (ScaleFactor_MUON), taus (ScaleFactor_TAU), photons (ScaleFactor_PHOTON), or different operating efficiencies for b-tagging (ScaleFactor_BTAG), lepton triggers (ScaleFactor_lepTRIGGER), the pileup (ScaleFactor_PILEUP) and the jvt (ScaleFactor_JVT).
• Missing transverse energy (MET): The missing transverse energy per event (met).
• Truth information: related to the particles produced in the collision, like truth electrons number (truth_elec_n), transverse momentums (truth_elec_pt), pseudo-rapities (truth_elec_eta), azimuthal angles (truth_elec_phi), truth muons number (truth_muon_n), transverse momentums (truth_muon_pt), pseudo-rapities (truth_muon_eta), azimuthal angles (truth_muon_phi), truth jets number (truth_jet_n), transverse momentums (truth_jet_pt), pseudo-rapities (truth_jet_eta), azimuthal angles (truth_jet_phi), etc.
• Example systematic uncertainty variations that can be used to better understand how systematic uncertainties are applied in real analyses, for instance jet_pt_jer1 or jet_pt_jer2 (transverse momentum of the jet after applying a specific systematic uncertainty from the jet energy resolution calibration).
• A carefully selected set of information sufficient to get started exploring the Run 2 proton-proton collisions collected by ATLAS (detailed list of variables).

⚙️ How does it work?

Getting started with the framework is straightforward:

1. Access the Repository: Visit the main GitLab repository.
   • For the 2025 beta release of Outreach and Education Open Data, please check out this tag
   • In general, the main branch should work and be up to date with the latest changes, but it is not guaranteed to be stable at all times
2. Follow the Documentation: The repository includes a comprehensive README file with step-by-step instructions to:
   • Install the framework (a docker image is available).
   • Run the framework locally.
   • Convert PHYSLITE samples to ROOT NTuples.
3. Get the ROOT NTuples for your educational purposes.