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Johann Brehmer
Johann Brehmer
Qualcomm AI Research
Bestätigte E-Mail-Adresse bei nyu.edu - Startseite
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Zitiert von
Zitiert von
Jahr
Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector
D de Florian, C Grojean, F Maltoni, C Mariotti, A Nikitenko, M Pieri, ...
arXiv. org, 2016
13722016
The frontier of simulation-based inference
K Cranmer, J Brehmer, G Louppe
Proceedings of the National Academy of Sciences 117 (48), 30055-30062, 2020
3002020
Constraining effective field theories with machine learning
J Brehmer, K Cranmer, G Louppe, J Pavez
Physical review letters 121 (11), 111801, 2018
1522018
A guide to constraining effective field theories with machine learning
J Brehmer, K Cranmer, G Louppe, J Pavez
Physical Review D 98 (5), 052004, 2018
1282018
Mining gold from implicit models to improve likelihood-free inference
J Brehmer, G Louppe, J Pavez, K Cranmer
Proceedings of the National Academy of Sciences, 2020, 2018
1222018
Pushing Higgs effective theory to its limits
J Brehmer, A Freitas, D Lopez-Val, T Plehn
Physical Review D 93 (7), 075014, 2016
1142016
Symmetry Restored in Dibosons at the LHC?
J Brehmer, JA Hewett, J Kopp, T Rizzo, J Tattersall
Journal of High Energy Physics 2015 (10), 1-32, 2015
942015
MadMiner: Machine learning-based inference for particle physics
J Brehmer, F Kling, I Espejo, K Cranmer
Computing and Software for Big Science 4 (1), 1-25, 2020
792020
Flows for simultaneous manifold learning and density estimation
J Brehmer, K Cranmer
34th Annual Conference on Neural Information Processing Systems (NeurIPS 2020), 2020
672020
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
J Brehmer, S Mishra-Sharma, J Hermans, G Louppe, K Cranmer
The Astrophysical Journal 886 (1), 49, 2019
652019
Better Higgs- tests through information geometry
J Brehmer, F Kling, T Plehn, TMP Tait
Physical Review D 97 (9), 095017, 2018
632018
Extending the limits of Higgs effective theory
A Biekötter, J Brehmer, T Plehn
Physical Review D 94 (5), 055032, 2016
602016
Better Higgs boson measurements through information geometry
J Brehmer, K Cranmer, F Kling, T Plehn
Physical Review D 95 (7), 073002, 2017
582017
Likelihood-free inference with an improved cross-entropy estimator
M Stoye, J Brehmer, G Louppe, J Pavez, K Cranmer
NeurIPS Workshop on Machine Learning for the Physical Sciences 2019, 2018
452018
Neural Message Passing for Jet Physics
I Henrion, J Brehmer, J Bruna, K Cho, K Cranmer, G Louppe, G Rochette
NIPS Workshop on Deep Learning for the Physical Sciences 2017, 2017
392017
Benchmarking simplified template cross sections in WH production
J Brehmer, S Dawson, S Homiller, F Kling, T Plehn
Journal of High Energy Physics 2019 (11), 1-30, 2019
292019
Polarized W W scattering on the Higgs pole
J Brehmer, J Jaeckel, T Plehn
Physical Review D 90 (5), 054023, 2014
252014
Effective LHC measurements with matrix elements and machine learning
J Brehmer, K Cranmer, I Espejo, F Kling, G Louppe, J Pavez
Journal of Physics: Conference Series 1525 (1), 012022, 2020
192020
The diboson excess: experimental situation and classification of explanations; a Les Houches pre-proceeding
J Brehmer, G Brooijmans, G Cacciapaglia, A Carmona, SR Chivukula, ...
arXiv preprint arXiv:1512.04357, 2015
142015
Simulation-based inference in particle physics
J Brehmer
Nature Reviews Physics 3 (5), 305-305, 2021
72021
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