Cranmer, Kyle

The frontier of simulation-based inference

Cranmer, K., Brehmer, J., & Louppe, G. (2020) · Proceedings of the National Academy of Sciences

Markov Chains and Monte Carlo MethodsSimulation Techniques and Applications

Cited by 1,032

Deep learning and its application to LHC physics

Guest, D., Cranmer, K., & Whiteson, D. (2018) · Annual Review of Nuclear and Particle Science

Particle physics theoretical and experimental studiesParticle Detector Development and Performance

Cited by 417

QCD-aware recursive neural networks for jet physics

Louppe, G., Cho, K., Becot, C., & Cranmer, K. (2019) · Journal of High Energy Physics

Particle physics theoretical and experimental studiesHigh-Energy Particle Collisions Research

Cited by 123

Challenges of profile likelihood evaluation in multi-dimensional SUSY scans

Feroz, F., Cranmer, K., Hobson, M., Ruiz de Austri, R., & Trotta, R. (2011) · Journal of High Energy Physics

Particle physics theoretical and experimental studiesMarkov Chains and Monte Carlo Methods

Cited by 82

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning

Brehmer, J., Mishra-Sharma, S., Hermans, J., Louppe, G., & Cranmer, K. (2019) · The Astrophysical Journal

Galaxies: Formation, Evolution, PhenomenaDark Matter and Cosmic Phenomena

Cited by 80