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MadMiner: Machine Learning-Based Inference for Particle Physics

https://doi.org/10.1007/s41781-020-0035-2

Brehmer, Johann ; Kling, Felix ; Espejo, Irina ; Cranmer, Kyle ( December 2020 , Computing and Software for Big Science)

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The frontier of simulation-based inference

https://doi.org/10.1073/pnas.1912789117

Cranmer, Kyle ; Brehmer, Johann ; Louppe, Gilles ( May 2020 , Proceedings of the National Academy of Sciences)

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 inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving additional momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound influence these developments may have on science.

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Effective LHC measurements with matrix elements and machine learning

https://doi.org/10.1088/1742-6596/1525/1/012022

Brehmer, J. ; Cranmer, K. ; Espejo, I. ; Kling, F. ; Louppe, G. ; Pavez, J. ( April 2020 , Journal of Physics: Conference Series)

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A guide to constraining effective field theories with machine learning

https://doi.org/10.1103/PhysRevD.98.052004

Brehmer, Johann ; Cranmer, Kyle ; Louppe, Gilles ; Pavez, Juan ( September 2018 , Physical Review D)

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