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

@article{Romo2020TransferabilityOD,
  title={Transferability of deep learning models in searches for new physics at colliders},
  author={M. Crispim Rom{\~a}o and Nuno Filipe Castro and Rute Pedro and T. Dias Do Vale},
  journal={Physical Review D},
  year={2020}
}
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 $t\overline{t}$, $Z+\text{jets}$ and dibosons as the main backgrounds… 

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