Simulated datasets for detector and particle flow reconstruction: CLIC detector, hit-based data, 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_hits_pf10k.tar: ee -> ttbar, center of mass energy at 380 GeV, 10k events
• clic_edm_qq_hits_pf10k.tar: ee -> Z* -> qqbar, center of mass energy at 380 GeV, 10k events

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 calorimeter hits
• 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_hits/utils_edm.py.