2.5k citations · 4.5k across the 14 of their papers we have counts for
5 papers · 1 filter
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, Gilles Louppe
Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for infer…
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans +2
The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark…
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…
Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer +5
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent…