Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format

Derived from https://zenodo.org/record/8260741, prepared in a machine-learning friendly TFDS format, ready to be used with https://zenodo.org/record/8397954. clic_edm_ttbar_pf.tar: ee -> ttbar, center of mass energy at 380 GeV clic_edm_qq_pf.tar: ee -> Z* -> qqbar, center of mass energy at 380 GeV clic_edm_ww_fullhad_pf.tar: ee -> WW -> W decaying hadronically, center of mass energy at 380 GeV clic_edm_zh_tautau_pf.tar: ee -> ZH -> Higgs decaying to tau leptons, center of mass energy at 380 GeV Contents Each .tar file contains the dataset in the tensorflow-datasets (minimum version v4.9.1), array_record format. Dataset semantics Each dataset consists of events that can be iterated over using the tensorflow-datasets library in either tensorflow or pytorch. Each event has the following information available: X: the reconstruction input features, i.e. tracks and clusters ygen: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v1.6/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py.