9 papers
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…
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…
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…
SPECTER: Efficient Evaluation of the Spectral EMD
Rikab Gambhir, Andrew J. Larkoski, Jesse Thaler
The Energy Mover's Distance (EMD) has seen use in collider physics as a metric between events and as a geometric method of defining infrared and collinear safe observables. Recentl…