Energy calibration via correlation using an adaptive mesh refinement

Abstract

Energy calibration via correlation has proven to be a powerful calibration method especially in the case of calibration data sets with low counting statistics. This work reports on an evolved version of this approach that uses an adaptive mesh refinement to sample the parameter space which results in the same calibration performance but in a considerably reduced computation time.