Machine Learning and generative modeling in particle physics analysis and simulation |
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To get in touch with the particles team, please contact Frank Gaede
Team Members
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Recent publications
- Machine learning the trilinear and light-quark Yukawa couplings from Higgs pair kinematic shapes: Lina Alasfar et al., arXiv:2207.13760 [cond-mat.mtrl-sci], doi: 10.48550/arXiv.2207.13760
- Measurement of the CP-violating phase ϕs in the Bs0→J/ψϕ(1020)→μ+μ−K+K− channel in proton-proton collisions at s=13TeV: A.M. Sirunyan et al., Physics Letters B, doi: 10.1016/j.physletb.2021.136188
- Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models: Kim A. Nicoli et al., Phys. Rev. Lett., doi: 10.1103/PhysRevLett.126.032001
- Measurement of the Higgs boson production rate in association with top quarks in final states with electrons, muons, and hadronically decaying tau leptons at √s= 13 TeV: A. M. Sirunyan et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-021-09014-x
- Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations: Florian Rehm et al., EPJ Web Conf., doi: 10.1051/epjconf/202125103042