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Publication
Transferability of Deep Learning Models in Searches for New Physics at Colliders
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Abstract(s)
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 flavour changing neutral
current, pair-production of vector-like $T$-quarks via standard model gluon
fusion and via a heavy gluon decay in a grid of 3 mass points: 1, 1.2 and 1.4
TeV. These networks were trained with $t\bar{t}$, $Z$+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 vector-like $T$-quarks, whether they are
produced via heavy gluon decay or standard model gluon fusion. The network
trained on flavour changing neutral current signal, while struggling the most
on the other signals, still produce 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.