6 citations · 11 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
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…
Removing phase-space restrictions in factorized cross sections
Ilya Feige, Matthew D. Schwartz, Kai Yan
Factorization in gauge theories holds at the amplitude or amplitude-squared level for states of given soft or collinear momenta. When performing phase-space integrals over such sta…