Learning to Identify Semi-Visible Jets

Abstract

We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-$p_{\text{T}}$ jet constituents.

9 pages, for submission

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Cited by in corpus (8)

• Dark Sector Glueballs at the LHC
• Equivariant, Safe and Sensitive -- Graph Networks for New Physics
• Search for new physics in final states with semi-visible jets or anomalous signatures using the ATLAS detector
• Dark Sector Showers in the Lund Jet Plane
• TopicFlow: Disentangling quark and gluon jets with normalizing flows
• Semi-visible jets + X: Illuminating Dark Showers with Radiation
• 2B or not 2B, a study of bottom-quark-philic semi-visible jets
• Jet Rotational Metrics