ATLAS multi-process simulation for ML-based jet flavour tagging (JetSet2)

<p>Flavour tagging, the identification of jets originating from bottom and charm quarks and from hadronically decaying tau leptons, is essential for many physics analyses at the ATLAS experiment. This dataset, released for public use, can be used to train and evaluate machine learning models for jet flavour tagging. It is the second ATLAS flavour-tagging open dataset (JetSet2), the successor to the JetSet dataset (<a href="opendata.cern.ch/record/93940">record 93940</a>, <a href="arxiv.org/abs/2505.19689">arXiv:2505.19689</a>), and is the data on which the tagger scaling study <a href="cds.cern.ch/search?p=reportnumber%3A%22ATL-SOFT-PUB-2026-002%22">ATL-SOFT-PUB-2026-002</a> is based.</p> <p>The dataset contains approximately 11 billion fully labelled jets from 29 simulated samples covering 7 physics processes: top quark pair production, QCD dijet production (including <i>b</i>-filtered samples), <i>Z'</i> production, associated <i>VH</i> production with <i>H</i> to <i>bb</i>, <i>cc</i> and <i>tau tau</i>, and <i>gamma*</i> to <i>tau tau</i>, from the ATLAS Run 2 (13 TeV) and Run 3 (13.6 TeV) simulation campaigns.

Each jet carries its kinematics, truth labels, up to 40 inner-detector tracks, 10 electrons, 50 particle-flow objects and 5 truth heavy-flavour hadrons, the scores of the ATLAS GN2v01 and GN3EPCLV01 taggers, and a sample identifier (DSID). Files are in HDF5 format with one structured dataset per object type.</p> <p>Unlike JetSet, the files are released after preprocessing: every jet is kept exactly once and the training files carry a per-jet weight that balances the flavour classes and their kinematics, so no resampling step is needed.