1.1k citations · 2.1k across the 27 of their papers we have counts for
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cs.AI2020★ 2 cited
Hierarchical clustering in particle physics through reinforcement learning
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo +1
Particle physics experiments often require the reconstruction of decay patterns through a hierarchical clustering of the observed final-state particles. We show that this task can…
hep-ph2020
Simulation-based inference methods for particle physics
Johann Brehmer, Kyle Cranmer
Our predictions for particle physics processes are realized in a chain of complex simulators. They allow us to generate high-fidelity simulated data, but they are not well-suited f…
stat.ML2020★ 33 cited
Flows for simultaneous manifold learning and density estimation
Johann Brehmer, Kyle Cranmer
We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that mani…