Transferability of deep learning models in searches for new physics at colliders

Abstract

In this work we assess the transferability of deep learning models to detect beyond the standard model signals. For this we trained deep neural networks on three different signal models: tZ production via a flavor changing neutral current, pair production of vectorlike T-quarks via standard model gluon fusion and via a heavy gluon decay in a grid of three mass points: 1, 1.2 and 1.4 TeV. These networks were trained with t¯t, Z+jets and dibosons as the main backgrounds. Limits were derived for each signal benchmark using the inference of networks trained on each signal independently, so that we can quantify the degradation of their discriminative power across different signal processes. We determine that the limits are compatible within uncertainties for all networks trained on signals with vectorlike T-quarks, whether they are produced via heavy gluon decay or standard model gluon fusion. The network trained on flavor changing neutral current signal, while struggling the most on the other signals, still produces reasonable limits. These results indicate that deep learning models are capable of providing sensitivity in the search for new physics even if it manifests itself in models not assumed during training.