The limits we find are insensitive to the nature of the resonance i.e. with respect to their quantum numbers, and they are comparable to the limits found by ATLAS in Ref. [@Aaboud:2018mjh].[^3]

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              **Signal**              **Overall AUC**   **Upper-Lower Difference**   **Cross Section Limit \[pb\]**
  ---------------------------------- ----------------- ---------------------------- --------------------------------
    $Z^\prime_{w=10~\mathrm{GeV}}$         0.662                  0.009                          0.0101
   $Z^\prime_{w=89.6~\mathrm{GeV}}$        0.656                  0.009                          0.0098
   $Z^\prime_{w=200~\mathrm{GeV}}$         0.650                  0.009                          0.0105
                Scalar                     0.654                  0.010                          0.0104
                Octet                      0.659                  0.010                          0.0102

  : The overall AUC score, difference between the largest and smallest AUC scores and the cross section limits found from using the adversarial autoencoder trained only on background events and tested on the original $Z^\prime$ case and four other signals. []{data-label="tab:bsmResultsAE"}

Conclusions {#sec:conclusions}
===========

The ideal scenario for the usage of machine learning methods is when they can be applied directly on experimental data, without the requirement to train them on pseudo-data or without theoretically calculated inputs, e.g. as in the Matrix Element Method. In such circumstances neither theoretical uncertainties that challenge the robustness of the method, nor a theoretical bias regarding the features of the signal are introduced. Thus, the experimental data alone would be sufficient to identify anomalous events, which could be isolated and studied further to discover new physics. Such identification 