Theo Heimel, Gregor Kasieczka, Tilman Plehn, Jennifer M Thompson
SciPost Phys. 6, 030 (2019) ·
published 8 March 2019
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.
Anke Biekoetter, Fabian Keilbach, Rhea Moutafis, Tilman Plehn, Jennifer M. Thompson
SciPost Phys. 4, 035 (2018) ·
published 22 June 2018
Searches for invisible Higgs decays in weak boson fusion are a well-known
laboratory for jets and QCD studies. We present a series of results on tagging
jets and central jet activity. First, precision analyses of the central jet
activity require full control of single top production in some analyses.
Second, the rate dependence on the size of the tagging jets is not limited to
weak boson fusion. For the first time, we show how subjet information on the
tagging jets and on the additional jet activity can be used to extract the
Higgs signal. The additional observables relieve some of the pressure on other,
critical observables. Finally, we compare the performance of weak boson fusion
and associated Higgs production.
by Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn, Jennifer M. Thompson
Version 1 (current version)
Submitted 2019-03-04 to SciPost Physics
· latest activity: 2019-04-18 13:45
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