2.5k citations · 4.5k across the 14 of their papers we have counts for
6 papers · 1 filter
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…
MadMiner: Machine learning-based inference for particle physics
Johann Brehmer, Felix Kling, Irina Espejo +1
Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recent…
Effective LHC measurements with matrix elements and machine learning
Johann Brehmer, Kyle Cranmer, Irina Espejo +3
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response ha…
A Guide to Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics p…
Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, Gilles Louppe +1
We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra informatio…
QCD-Aware Recursive Neural Networks for Jet Physics
Gilles Louppe, Kyunghyun Cho, Cyril Becot +1
Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neu…