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In this work we assess the transferability of deep learning models to detectbeyond the standard model signals. For this we trained Deep Neural Networks onthree different signal models: $tZ$ production via a flavour changing neutralcurrent, pair-production of vector-like $T$-quarks via standard model gluonfusion and via a heavy gluon decay in a grid of 3 mass points: 1, 1.2 and 1.4TeV. These networks were trained with $t\bar{t}$, $Z$+jets and dibosons as themain backgrounds. Limits were derived for each signal benchmark using theinference of networks trained on each signal independently, so that we canquantify the degradation of their discriminative power across different signalprocesses. We determine that the limits are compatible within uncertainties forall networks trained on signals with vector-like $T$-quarks, whether they areproduced via heavy gluon decay or standard model gluon fusion. The networktrained on flavour changing neutral current signal, while struggling the moston the other signals, still produce reasonable limits. These results indicatethat deep learning models are capable of providing sensitivity in the searchfor new physics even if it manifests itself in models not assumed duringtraining.

Transferability of Deep Learning Models in Searches for New Physics at Colliders