Datasets of QCD jets used for studying unfolding in OmniFold: A Method to Simultaneously Unfold All Observables. Four different datasets are present: Herwig 7.1.5 with the default tune Pythia 8.243 with tune 21 (ATLAS A14 central tune with NNPDF2.3LO) Pythia 8.243 with tune 25 (ATLAS A14 variation 2+ of tune 21) Pythia 8.243 with tune 26 (ATLAS A14 variation 2- of tune 21) \(Z \) + jet events (with the \(Z \) set to be stable) were generated for each of the above generator/tune pairs with the \(Z\) boson \(\hat p_{T}^\text{min}>150\,\text{GeV} \) and \(\sqrt{s}=14\,\text{TeV}\). Events were then passed through the Delphes 3.4.2 fast detector simulation of the CMS detector. Jets with radius parameter \(R=0.4\) were found with the anti-\(k_T\) algorithm at both particle level ("gen"), where all non-neutrino, non-\(Z\) particle are used, and detector level ("sim"), where reconstructed energy flow objects (tracks, electromagnetic calorimeter cells, and hadronic calorimeter cells) are used. Only jets with transverse momentum greater than \(10\,\text{GeV}\) are kept (note that sim jets have a simple jet energy correction applied by Delphes). The hardest jet from events with a \(Z\) boson with a final transverse momentum of \(200\,\text{GeV}\) or greater are kept, yielding approximately 1.6 million jets at both gen-level and sim-level for each generator/tune pair. Each zipped NumPy file consists of several arrays, the names of which begin with either 'gen_' or 'sim_' depending on which set of jets they correspond to. The name of each array ends in a key word indicating what it contains. With the exception of 'gen_Zs' (which contains the \((p_T,\,y,\,\phi)\) of the final \(Z\) boson), there is both a gen and sim version of each array. The included arrays are (listed by their key words): 'jets' - The jet axis four vector, as \((p_T^\text{jet},\,y^\text{jet},\,\phi^\text{jet},\,m^\text{jet})\) where \(y^\text{jet}\) is the jet rapidity, \(\phi^\text{jet}\) is the jet azimuthal angle, and \(m^\text{jet}\) is the jet mass. 'particles' - The (rescaled, translated) constituents of the jets as \((p_T/100,\,y-y^\text{jet},\,\phi-\phi^\text{jet},\,f_\text{PID})\) where \(f_\text{PID}\) is a small float corresponding to the PDG ID of the particle. The PIDs are remapped according to \(22\to0.0,\,211\to0.1,\,-211\to0.2,\) \(130\to0.3,\,11\to0.4,\,-11\to0.5,\,13\to0.6,\,-13\to0.7,\,321\to0.8,\,-321\to0.9,\) \(2212\to1.0,\,-2212\to1.1,\,2112\to1.2,\,-2112\to1.3\). Note that ECAL cells are treated as photons (id 22) and HCAL cells are treated as \(K_L^0\) (id 130). 'mults' - The constituent multiplicity of the jet. 'lhas' - The Les Houches (\(\beta=1/2\)) angularity. 'widths' - The jet width (\(\beta=1\) angularity). 'ang2s' - The \(\beta=2\) angularity (note that this is very similar to the jet mass, but does not depend on particle masses). 'tau2s' - The 2-subjettiness with \(\beta=1\). 'sdms' - The groomed mass with Soft Drop parameters \(z_\text{cut}=0.1\) and \(\beta=0\). 'zgs' - The groomed momentum fraction (same Soft Drop parameters as above). If you use this dataset, please cite this Zenodo record as well as the corresponding paper: A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables, arXiv:1911.09107. The datasets can be downloaded and read into python automatically using the EnergyFlow Python package.

{"references": ["P. T. Komiske, E. M. Metodiev, EnergyFlow, https://energyflow.network."]}