The publicly released 13 TeV Open Datasets can be used for educational purposes with different levels of task difficulty.
At a beginner level, one could visualise the content of the datasets and produce simple distributions. An intermediate-level task would consist of making histograms with collision data after some basic selection. Advanced-level tasks would allow for a deeper look into the ATLAS data, with possibilities of measuring real event properties and physical quantities.
A non-exhaustive list of possible tasks with the proposed datasets include:
comparisons of several distributions of event variables for simulated signal and background events;
finding variables that are able to separate signal from background (jet multiplicity, transverse momenta of jets and leptons, lepton isolation, b-tagging, missing transverse energy, angular distributions);
development and modification of cuts on these variables in order to enrich the signal-over-background separation;
optimisation of the signal-over-background ratio and estimation of the purity based on simulation only;
comparisons of the selection efficiency between data and simulation.
Advanced-level tasks might include:
derivation of production cross sections and masses of objects;
reconstruction of the objects (quarks or bosons) by assigning the detector physics objects (jets, leptons, missing energy) to the hypothetical decay trees;
estimation of the impact of other sources of systematic uncertainties (luminosity uncertainty, b-tagging efficiency, background modelling) by adding approximate and conservative values.
a test-bed for new data-analysis techniques, e.g. kinematic fitting procedures, multivariate discrimination of signal from background and other machine learning tasks.
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