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|As Contributors:||Tilman Plehn · Jennifer Thompson|
|Submitted by:||Thompson, Jennifer|
|Submitted to:||SciPost Physics|
|Domain(s):||Exp. & Theor.|
|Subject area:||High-Energy Physics - Phenomenology|
Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets from decays of arbitrary heavy resonances. To control the backgrounds and the underlying systematics we can de-correlate the jet mass using an adversarial network. Such an adversarial autoencoder allows for a general and at the same time easily controllable search for new physics. Ideally, it can be trained and applied to data in the same phase space region, allowing us to efficiently search for new physics using un-supervised learning.
Puts forward an innovative new technique for a high priority new area - search for BSM effects in jet substructure at the LHC. Makes a convincing attempt to reduce the model dependence of such an approach.
1 - the model neglects underlying event and pile up effects, which are typically reduced by "grooming" jets using one or another technique. The authors do not comment or show whether their method works on groomed jets, but this would be relatively simple to do using the tools they have at hand, I think.
I think this should be accepted, if my questions can be addressed/answered.
1 - show the MC statistics are high enough to support the conclusions, or generate more
2 - show or discuss how well the method should work on groomed (pile-up suppressed) jets.
3 - show or discuss impact of the detector simulation used
4 - address questions/requests for clarification in the attached PDF (which include the above as the most significant) (I also so highlighted some bits of text which look like typos or may need rephrasing)