Conveners
Taggers: 1
- Chris Malena Delitzsch (Technische Universitaet Dortmund (DE))
Taggers: 2
- Jennifer Roloff (Brookhaven National Laboratory (US))
The ability to differentiate between hadronically decaying massive particles is increasingly important to the LHC physics program. A variety of tagging algorithms for large-radius jets, reconstructed from unified-flow-objects (UFOs), are presented to identify jets containing the hadronic decay of W/Z bosons and top quarks, including both cut-based taggers and machine learning discriminants....
We describe a new jet clustering algorithm (SIFT: Scale-Invariant Filter Tree) that does not impose a fixed cone size or associated scale on the event. This construction maintains excellent object discrimination for very collimated partonic systems, tracks accrued mass, and asymptotically recovers favorable behaviors of both the standard KT and anti-KT algorithms. It is intrinsically suitable...
Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement. In this work, we present JetClass, a new comprehensive dataset for jet tagging. The JetClass dataset consists of 100 M jets, about two orders of magnitude...
The Heavy Object Tagger with Variable R (HOTVR) is an algorithm for the clustering and identification of boosted, hadronically decaying, heavy particles. The central feature of the HOTVR algorithm is a vetoed jet clustering with variable distance parameter R, that decreases with increasing transverse momentum of the jet. In this talk, we present improvements to the HOTVR algorithm, replacing...
The study of substructure of hadronic jets is key to unlocking further understanding of the physics underlying collisions at the LHC. In the context of precision Standard Model physics, we discuss how substructure variables sensitive to colour flow, such as the Lund Jet Plane, Jet Angularities and Jet Pull projections can be used to develop taggers highly sensitive to the radiation pattern...
Identifying highly boosted resonances, including top quarks, electroweak bosons, and new particles, has become a core topic of research at the LHC. Advances in machine learning have further accelerated interest in boosted resonance identification. However, as machine learning algorithms become more powerful, so too have the correlations of the algorithms with jet kinematics, like mass and...
As classic WIMP-based signatures for dark matter at the LHC have found no compelling evidence, several phenomenological studies have raised the possibility of accessing a strongly-interacting dark sector through new collider-event topologies. If dark mesons exist, their evolution and hadronization procedure are currently little constrained. They could decay promptly and result in QCD-like jet...
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...
Several flavor tagging algorithms exist in ATLAS for jets containing two b-hadrons. These double-b tagger algorithms focus on high transverse-momentum jets, usually above 200 GeV. This work describes the development of a new double-b tagger for jets below 200~GeV. The algorithm relies on large radius track-jets which can be reconstructed at low transverse momenta and implements a neural...
Deep learning has transformed jet tagging, in bringing a leap to tagging performance and hence substantially improving the sensitivity of physics searches at the LHC. In seek of further enhancement, recent interests fall in experimenting with more advanced neural network architectures, or injecting physics knowledge into the design of the network. This talk focuses on the latter, with a...
