806 citations · 4.7k across the 88 of their papers we have counts for
90 papers
More QCD Masterclass Lectures on Jet Physics and Machine Learning
Andrew J. Larkoski
These lectures were presented at the 2026 QCD Masterclass in Saint-Jacut-de-la-mer, France. They supplement the published notes from the 2024 school with presentation of a few new…
Generative models on phase space
Zachary Bogorad, Ibrahim Elsharkawy, Yonatan Kahn +2
Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are partic…
Flavor-Changing Non-Global Logarithms
Andrew J. Larkoski
Non-global logarithms are low energy correlations between the substructure of a jet and the event in which it is immersed. We study the leading non-global logarithms that arise fro…
Factorization for Collider Dataspace Correlators
Andrew J. Larkoski
A metric on the space of collider physics data enables analysis of its geometrical properties, like dimensionality or curvature, as well as quantifying the density with which a fin…
Non-Gaussianities in Collider Metric Binning
Andrew J. Larkoski
Metrics for rigorously defining a distance between two events have been used to study the properties of the dataspace manifold of particle collider physics. The probability distrib…
A Step Toward Interpretability: Smearing the Likelihood
Andrew J. Larkoski
The problem of interpretability of machine learning architecture in particle physics has no agreed-upon definition, much less any proposed solution. We present a first modest step…