Johann Brehmer
Johann Brehmer
Verified email at nyu.edu - Homepage
TitleCited byYear
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
8792016
Pushing Higgs effective theory to its limits
J Brehmer, A Freitas, D Lopez-Val, T Plehn
Physical Review D 93 (7), 075014, 2016
942016
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
872015
Constraining effective field theories with machine learning
J Brehmer, K Cranmer, G Louppe, J Pavez
Physical review letters 121 (11), 111801, 2018
422018
Extending the limits of Higgs effective theory
A Biekötter, J Brehmer, T Plehn
Physical Review D 94 (5), 055032, 2016
42*2016
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
382018
Better Higgs boson measurements through information geometry
J Brehmer, K Cranmer, F Kling, T Plehn
Physical Review D 95 (7), 073002, 2017
372017
Mining gold from implicit models to improve likelihood-free inference
J Brehmer, G Louppe, J Pavez, K Cranmer
arXiv preprint arXiv:1805.12244, 2018
232018
Better Higgs- tests through information geometry
J Brehmer, F Kling, T Plehn, TMP Tait
Physical Review D 97 (9), 095017, 2018
202018
Polarized W W scattering on the Higgs pole
J Brehmer, J Jaeckel, T Plehn
Physical Review D 90 (5), 054023, 2014
152014
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
Neural Message Passing for Jet Physics
I Henrion, J Brehmer, J Bruna, K Cho, K Cranmer, G Louppe, G Rochette
122017
Likelihood-free inference with an improved cross-entropy estimator
M Stoye, J Brehmer, G Louppe, J Pavez, K Cranmer
arXiv preprint arXiv:1808.00973, 2018
82018
Effective LHC measurements with matrix elements and machine learning
J Brehmer, K Cranmer, I Espejo, F Kling, G Louppe, J Pavez
arXiv preprint arXiv:1906.01578, 2019
42019
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
arXiv preprint arXiv:1909.02005, 2019
3*2019
New Ideas for Effective Higgs Measurements
J Brehmer
32017
Towards testing a two-Higgs-doublet model with maximal CP symmetry at the LHC: Monte Carlo event generator implementation
J Brehmer, V Lendermann, M Maniatis, O Nachtmann, HC Schultz-Coulon, ...
The European Physical Journal C 73 (4), 2380, 2013
32013
MadMiner: Machine learning-based inference for particle physics
J Brehmer, F Kling, I Espejo, K Cranmer
arXiv preprint arXiv:1907.10621, 2019
22019
Polarised WW Scattering at the LHC
J Brehmer
22012
Monte-Carlo event generation for a two-Higgs-doublet model with maximal CP symmetry
J Brehmer
arXiv preprint arXiv:1206.7044, 2012
12012
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