Autoencoders for Semivisible Jet Detection

TL;DR

This paper introduces an autoencoder-based anomaly detection method to identify semivisible jets, a potential signature of dark matter, in collider experiments, improving detection of complex, non-standard jet topologies.

Contribution

The paper presents a novel, signal-agnostic autoencoder approach utilizing jet substructure variables for detecting semivisible jets in collider data.

Findings

Autoencoder effectively distinguishes semivisible jets from ordinary jets.

Method demonstrates robustness against detector inefficiencies.

Applicable to various new physics models predicting anomalous jets.

Abstract

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.