Fuτure - dataset for studies, development, and training of algorithms for reconstructing and identifying hadronically decaying tau leptons

Data description MC Simulation The Fuτure dataset is intended for studies, development, and training of algorithms for reconstructing and identifying hadronically decaying tau leptons. The v3 of this dataset is generated with Pythia8, with the full detector simulation being performed by Geant4 using the FTFP_BERT physics list with the CLIC Like Detector (CLD) detector setup (CLD_o2_v07). Events are reconstructed using the Marlin reconstruction framework and interfaced with Key4HEP (release 2025-05-29). Particle candidates in the reconstructed events are reconstructed using the PandoraPF algorithm. In this version of the dataset no γγ -> hadrons background is included. Samples This dataset contains e+e- samples with Z->ττ, and Z->qq events. The following processes e+e- were simulated with Pythia 8 at sqrt(s) = 91 GeV: p8_ee_Z_tautau_ecm91 [Z -> ττ events] p8_ee_Z_qq_ecm91 [Z -> qq events] The .root files from the MC simulation chain are eventually processed by the software found in Github in order to create flat ntuples as the final product. Features The basis of the ntuples are the particle flow (PF) candidates from PandoraPF. Each PF candidate has four momenta, charge and particle label (electron / muon / photon / charged hadron / neutral hadron). The PF candidates in a given event are clustered into jets using generalized kt algorithm for ee collisions, with parameters p=-1 and R=0.4. The minimum pT is set to be 0 GeV for both generator level jets and reconstructed jets. The dataset contains the four momenta of the jets, with the PF candidates in the jets with the above listed properties. Additionally, a set of variables describing the tau lifetime are calculated. As tau lifetime is very short, these variables are sensitive to true tau decays. In the calculation of these lifetime variables, we use a linear approximation. In summary, the features found in the flat ntuples are: Name Description reco_cand_p4s 4-momenta per particle in the reco jet. reco_cand_charges Charge per particle in the jet. reco_cand_pdgs PDGid per particle in the jet. reco_jet_p4s RecoJet 4-momenta. reco_cand_dz Longitudinal impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated. reco_cand_dz_err Uncertainty of the longitudinal impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated. reco_cand_dxy Transverse impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated. reco_cand_dxy_err Uncertainty of the transverse impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated. gen_jet_p4s GenJet 4-momenta. Matched with RecoJet within a cone of radius dR < 0.3. gen_jet_tau_decaymode Decay mode of the associated genTau. Jets that have associated leptonically decaying taus are removed, so there are no DM=16 jets. If no GenTau can be matched to GenJet within dR < 0.4, a fill value is used. gen_jet_tau_charge Charge of the genTau. gen_jet_tau_p4s Visible 4-momenta of the genTau. If no GenTau can be matched to GenJet within dR<0.4, a fill value is used. cls_weight Per-jet classification weight used to balance signal and background contributions across bins in jet θ–p space during classifier training. The ground truth is based on stable particles at the generator level, before detector simulation. These particles are clustered into generator-level jets and are matched to generator-level τ leptons as well as reconstructed jets. In order for a generator-level jet to be matched to generator-level τ lepton, the τ lepton needs to be inside a cone of dR = 0.4. The same applies for the reconstructed jet, with the requirement on dR being set to dR = 0.4. Contents: qq_test.parquet qq_train.parquet z_test.parquet z_train.parquet Dataset characteristics File # Jets Size z_test.parquet 593 540 139 MB z_train.parquet 5 331 858 1.3 GB qq_test.parquet 3 928 385 749 MB qq_train.parquet 35 355 456 6.6 GB The dataset consists of 4 files of 8.7 GB in total. How can you use these data? The .parquet files can be directly loaded with the Awkward Array Python library.