Conveners
New Observables
- Andrew Larkoski
- Javier Duarte
We study the effectiveness of theoretically-motivated high-level jet observables in the extreme context of jets with a large number of hard sub-jets (up to N=8). Previous studies indicate that high-level observables are powerful, interpretable tools to probe jet substructure for N≤3 hard sub-jets, but that deep neural networks trained on low-level jet constituents match or slightly exceed...
We present a class of Neural Networks which extends the notion of Energy Flow Networks (EFNs) to higher-order particle correlations. The structure of these networks is inspired by the Energy-Energy Correlators of QFT, which are particularly robust against non-perturbative corrections. By studying the response of our models to the presence and absence of non-perturbative hadronization, we can...
In this talk, we present a new proposal on how to measure quark/gluon jet properties at the Large Hadron Collider (LHC). The main advantage of this approach is that our construction of an observable allows a single set of experimental cuts to be used to select jets, keeping all detector parameters unchanged, and in this way, reducing many systematic uncertainties. We will discuss the details...