Speaker
Dr
Zhenbin Wu
(University of Illinois at Chicago)
Description
The Large Hadron Collider has an enormous potential of discovering physics
beyond the Standard Model, given the unprecedented collision energy and the
large variety of production mechanisms that proton-proton collisions can probe.
Unfortunately, only a small fraction of the produced events can be studied,
while the majority of the events are rejected by the online filtering system.
One is then forced to decide upfront what to search and miss a new physics that
might hide in unexplored "corners" of the search region. We propose a
model-independent anomaly detection technique, based on deep autoencoders, to
identify new physics events as outliers of the standard event distribution in
some latent space. We discuss how this algorithm could be designed, trained,
and operated within the tight latency of the first trigger level of a typical
general-purpose LHC experiment.
Primary author
Dr
Zhenbin Wu
(University of Illinois at Chicago)
