Michel Luchmann

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Tilman PlehnHeidelberg UniversityVerified email at uni-heidelberg.de
Manuel HaussmannSyddansk UniversitetVerified email at imada.sdu.dk
Gregor KasieczkaUniversität HamburgVerified email at cern.ch
Marco BellagenteStability AIVerified email at stability.ai
Emma GEOFFRAYTrainer and coach for early career researchers, Grow into your PhDVerified email at growintoyourphd.com
Sebastian BruggisserPostdoc, Heidelberg UniversityVerified email at thphys.uni-heidelberg.de
Florian OtterpohlUniversität HamburgVerified email at physik.uni-hamburg.de
Anja ButterLPNHE, Paris/ ITP, HeidelbergVerified email at thphys.uni-heidelberg.de
Simon BadgerTurin universityVerified email at cern.ch
Ilaria BrivioNiels Bohr Institute, University of Copenhagen

Michel Luchmann

University of Heidelberg

Verified email at thphys.uni-heidelberg.de

Particle physics Machine Learning


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Deep-learning jets with uncertainties and more S Bollweg, M Haußmann, G Kasieczka, M Luchmann, T Plehn, ... SciPost Physics 8 (1), 006, 2020	58	2020
Machine learning and LHC event generation A Butter, T Plehn, S Schumann, S Badger, S Caron, K Cranmer, ... SciPost Physics 14 (4), 079, 2023	53	2023
Understanding event-generation networks via uncertainties M Bellagente, M Haußmann, M Luchmann, T Plehn SciPost Physics 13 (1), 003, 2022	43	2022
Per-object systematics using deep-learned calibration G Kasieczka, M Luchmann, F Otterpohl, T Plehn SciPost Physics 9 (6), 089, 2020	36	2020
From models to SMEFT and back? I Brivio, S Bruggisser, E Geoffray, W Killian, M Krämer, M Luchmann, ... SciPost Physics 12 (1), 036, 2022	21	2022
Loop amplitudes from precision networks S Badger, A Butter, M Luchmann, S Pitz, T Plehn SciPost Physics Core 6 (2), 034, 2023	13	2023
Deep-learning jets with uncertainties and more, SciPost Phys S Bollweg, M Haußmann, G Kasieczka, M Luchmann, T Plehn, ... arXiv preprint arXiv:1904.10004, 1904	7	1904
Understanding Event-Generation Networks via Uncertainties (2021) M Bellagente, M Haußmann, M Luchmann, T Plehn arXiv preprint arXiv:2104.04543, 0	6
To profile or to marginalize-A SMEFT case study I Brivio, S Bruggisser, N Elmer, E Geoffray, M Luchmann, T Plehn SciPost Physics 16 (1), 035, 2024	5	2024
Making the most of LHC data-Bayesian neural networks and SMEFT global analysis MA Luchmann	1	2022
Department of Physics and Astronomy M Luchmann

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