Umami: A Python toolkit for jet flavour tagging

Flavour-tagging, the identification of collimated sprays of particles ("jets") originating from bottom and charm quarks, is a critically important technique in the data analysis of the ATLAS experiment (ATLAS Collaboration, 2008) at the Large Hadron Collider (Evans & Bryant, 2008).It is applied in precision measurements of the Standard Model of particle physics, which is the theory describing fundamental forces and classifying all known elementary particles, as well as in searches for yet unknown phenomena.The long lifetime, high mass, and large decay multiplicity of hadrons containing bottom and charm quarks provide distinct signatures in particle detectors which can be exploited by machine learning algorithms.Excellent knowledge of the detector and the physics processes at hand enables simulations to provide a high-quality training dataset representative of recorded ATLAS data.The Umami software toolkit provides a unified data pipeline, definition of the algorithms, training and performance evaluation with a high degree of automation.