Johann Brehmer
Johann Brehmer
Verified email at nyu.edu - Homepage
Title
Cited by
Cited by
Year
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
12232016
Pushing Higgs effective theory to its limits
J Brehmer, A Freitas, D Lopez-Val, T Plehn
Physical Review D 93 (7), 075014, 2016
992016
Symmetry restored in dibosons at the LHC?
J Brehmer, JA Hewett, J Kopp, T Rizzo, J Tattersall
Journal of High Energy Physics 2015 (10), 182, 2015
892015
Constraining effective field theories with machine learning
J Brehmer, K Cranmer, G Louppe, J Pavez
Physical Review Letters 121 (11), 111801, 2018
752018
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
642018
Extending the limits of Higgs effective theory
A Biekötter, J Brehmer, T Plehn
Physical Review D 94 (5), 055032, 2016
46*2016
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
452018
Better Higgs boson measurements through information geometry
J Brehmer, K Cranmer, F Kling, T Plehn
Physical Review D 95 (7), 073002, 2017
412017
Better Higgs- tests through information geometry
J Brehmer, F Kling, T Plehn, TMP Tait
Physical Review D 97 (9), 095017, 2018
302018
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
252017
The frontier of simulation-based inference
K Cranmer, J Brehmer, G Louppe
Proceedings of the National Academy of Sciences, 2020
242020
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
152019
Polarized W W scattering on the Higgs pole
J Brehmer, J Jaeckel, T Plehn
Physical Review D 90 (5), 054023, 2014
152014
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
142018
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
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
122020
Benchmarking simplified template cross sections in W H production
J Brehmer, S Dawson, S Homiller, F Kling, T Plehn
Journal of High Energy Physics 2019 (11), 34, 2019
122019
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
92020
New ideas for effective higgs measurements
J Brehmer
42017
The diboson excess: experimental situation and classification of explanations
J Brehmer, G Brooijmans, G Cacciapaglia
A Les Houches pre-proceeding, 2015
42015
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