Speaker
Description
Estimating the size and shape of backgrounds is a key part in the vast majority of HEP analyses. The ABCD method provides a reliable way to extract this information directly from the experimental data, thus avoiding reliance on simulation. It relies on the assumption that two, statistically independent variables can be constructed, in order to compute the expected background contribution in the signal region of the analysis. This task is however often unfeasible in practice, since even small linear or non-linear correlation can spoil the validity of the ABCD method. A novel Machine Learning approach, known as ABCDisCoTEC, is presented here replacing the two variables with decorrelated neural network scores. The engineering of optimal features for the ABCD method is enforced during the training of the neural networks by minimising a differentiable dedicated loss function. In addition, to deal with such complex minimisation problem involving multiple constraints on different loss terms, a more robust technique, based on the Modified Differential Method for Multipliers, has been employed showing an improvement in the stability and robustness of the method.