Autoencoders for Semivisible Jet Detection
This addresses the challenge of identifying dark matter signatures in particle collider experiments for physicists, but it appears incremental as it applies existing autoencoder methods to a new domain.
The paper tackled the problem of detecting semivisible jets, which are collimated sprays of dark hadrons with missing momentum, by proposing a signal-agnostic strategy using anomaly detection techniques. The result showed that a deep neural autoencoder with jet substructure variables is highly useful for identifying these jets, though no concrete numbers were provided.
The production of dark matter particles from confining dark sectors may lead to many novel experimental signatures. Depending on the details of the theory, dark quark production in proton-proton collisions could result in semivisible jets of particles: collimated sprays of dark hadrons of which only some are detectable by particle collider experiments. The experimental signature is characterised by the presence of reconstructed missing momentum collinear with the visible components of the jets. This complex topology is sensitive to detector inefficiencies and mis-reconstruction that generate artificial missing momentum. With this work, we propose a signal-agnostic strategy to reject ordinary jets and identify semivisible jets via anomaly detection techniques. A deep neural autoencoder network with jet substructure variables as input proves highly useful for analyzing anomalous jets. The study focuses on the semivisible jet signature; however, the technique can apply to any new physics model that predicts signatures with anomalous jets from non-SM particles.