144 citations · 573 across the 12 of their papers we have counts for
12 papers
Binary JUNIPR: an interpretable probabilistic model for discrimination
Anders Andreassen, Ilya Feige, Christopher Frye +1
JUNIPR is an approach to unsupervised learning in particle physics that scaffolds a probabilistic model for jets around their representation as binary trees. Separate JUNIPR models…
Infrared Finiteness and Forward Scattering
Christopher Frye, Holmfridur Hannesdottir, Nisarga Paul +2
Infrared divergences have long been heralded to cancel in sufficiently inclusive cross-sections, according to the famous Kinoshita-Lee-Nauenberg theorem which mandates an initial a…
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
Anders Andreassen, Ilya Feige, Christopher Frye +1
In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics. To this end, a powerful to…
Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
Christopher Frye, Andrew J. Larkoski, Jesse Thaler +1
Charged track multiplicity is among the most powerful observables for discriminating quark- from gluon-initiated jets. Despite its utility, it is not infrared and collinear (IRC) s…
Precision physics with pile-up insensitive observables
Christopher Frye, Andrew J. Larkoski, Matthew D. Schwartz +1
To deepen the search for beyond the Standard Model physics, the Large Hadron Collider is pushing to higher and higher luminosity. At high luminosity, precision physics becomes incr…
Factorization for groomed jet substructure beyond the next-to-leading logarithm
Christopher Frye, Andrew J. Larkoski, Matthew D. Schwartz +1
Jet grooming algorithms are widely used in experimental analyses at hadron colliders to remove contaminating radiation from within jets. While the algorithms perform a great servic…