  We propose a robust method to identify anomalous jets by vetoing QCD-jets.
The robustness of this method ensures that the distribution of the proposed
discriminating variable (which allows us to veto QCD-jets) remains unaffected
by the phase space of QCD-jets, even if they were different from the region on
which the model was trained. This suggests that our method can be used to look
for anomalous jets in high m/p T bins by simply training on jets from low m/p T
bins, where sufficient background-enriched data is available. The robustness
follows from combining an autoencoder with a novel way of pre-processing jets.
We use momentum rescaling followed by a Lorentz boost to find the frame of
reference where any given jet is characterized by predetermined mass and
energy. In this frame we generate jet images by constructing a set of
orthonormal basis vectors using the Gram-Schmidt method to span the plane
transverse to the jet axis. Due to our preprocessing, the autoencoder loss
function does not depend on the initial jet mass, momentum or orientation while
still offering remarkable performance. We also explore the application of this
loss function combined (using supervised learning techniques like boosted
decision trees) with few other jet observables like the mass and Nsubjettiness
for the purpose of top tagging. This exercise shows that our method performs
almost as well as existing top taggers which use a large amount of physics
information associated with top decays while also reinforcing the fact that the
loss function is mostly independent of the additional jet observables.
