Generalized Numerical Inversion: A Neural Network Approach to Jet Calibration

Jets that are reconstructed by the ATLAS detector are corrected to ensure that the reported energy is an unbiased measurement of the particle-level jet energy. This jet energy scale correction consists of multiple steps where features of the reconstructed jet are used se- quentially, in order to improve the resolution and reduce the differences between quark and gluon jets (flavor dependence). This study reports on a new method based on multivari- ate regression, demonstrated with neural networks, that generalizes the current (iterated) one-dimensional technique for performing jet energy scale corrections (numerical inversion), called generalized numerical inversion. The new method remains an unbiased measurement of the particle-level energy, but allows for simultaneously using multiple features, such as the number of tracks inside jets and the average track radius, in order to account for correlations in the dependencies between features and with the jet energy. This new procedure can further improve the jet energy resolution and flavor dependence beyond a sequential approach and can be systematically improved by exploiting more variables and their interdependence with the jet energy response.