Publications

  1. OmniJet-${α_{ C}}$: Learning point cloud calorimeter simulations using generative transformers: Joschka Birk et al., arXiv, doi: 10.48550/arXiv.2501.05534
  2. Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows: Thorsten Buss et al., arXiv, doi: 10.48550/arXiv.2405.20407
  3. OmniJet-$α$: The first cross-task foundation model for particle physics: Joschka Birk et al., arXiv, doi: 10.48550/arXiv.2403.05618
  4. CaloPointFlow II Generating Calorimeter Showers as Point Clouds: Simon Schnake et al., arXiv, doi: 10.48550/arXiv.2403.15782
  5. Calibrating Bayesian Generative Machine Learning for Bayesiamplification: Sebastian Bieringer et al., arXiv, doi: 10.48550/arXiv.2408.00838
  6. CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation: Erik Buhmann et al., arXiv, doi: 10.48550/arXiv.2305.04847
  7. AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimization: Sebastian Bieringer et al., arXiv, doi: 10.48550/arXiv.2312.14027
  8. L2LFlows: Generating High-Fidelity 3D Calorimeter Images: Sascha Diefenbacher et al., arXiv, doi: 10.48550/arXiv.2302.11594
  9. Full Phase Space Resonant Anomaly Detection: Erik Buhmann et al., arXiv, doi: 10.48550/arXiv.2310.06897
  10. Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information: Joschka Birk et al., arXiv, doi: 10.48550/arXiv.2312.00123
  11. EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion: Erik Buhmann et al., arXiv, doi: 10.48550/arXiv.2310.00049
  12. EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets: Erik Buhmann et al., arXiv, doi: 10.48550/arXiv.2301.08128
  13. DeepTreeGAN: Fast Generation of High Dimensional Point Clouds: Moritz Alfons Wilhelm Scham et al., arXiv, doi: 10.48550/arXiv.2311.12616
  14. Attention to Mean-Fields for Particle Cloud Generation: Benno Käch et al., arXiv, doi: 10.48550/arXiv.2305.15254
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed: Erik Buhmann et al., Comput Softw Big Sci, doi: 10.1007/s41781-021-00056-0
  21. A deep neural network to search for new long-lived particles decaying to jets: cms-collaboration et al., Mach. Learn.: Sci. Technol., doi: 10.1088/2632-2153/ab9023
  22. Dijet resonance search with weak supervision using √s=13 TeV pp collisions in the ATLAS detector: ATLAS Collaboration et al., Phys. Rev. Lett., doi: 10.1103/PhysRevLett.125.131801
  23. Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques: The CMS Collaboration et al., J. Inst., doi: 10.1088/1748-0221/15/06/P06005
  24. Teaching machine learning with an application in collider particle physics: L. Benato et al., J. Inst., doi: 10.1088/1748-0221/15/09/C09011
  25. Cosmic ray spectrum from 250 TeV to 10 PeV using IceTop: The IceCube Collaboration et al., Phys. Rev. D, doi: 10.1103/PhysRevD.102.122001
  26. Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks: R. D. Parsons et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-020-7953-3
  27. Higgs self-coupling measurements using deep learning in the bb¯bb¯ final state: Jacob Amacker et al., J. High Energ. Phys., doi: 10.1007/JHEP12(2020)115
  28. Russian–German Astroparticle Data Life Cycle Initiative: Igor Bychkov et al., Data, doi: 10.3390/data3040056
  29. Evidence for associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying τ leptons at √s= 13 TeV: CMS Collaboration et al., J. High Energ. Phys., doi: 10.1007/JHEP08(2018)066
  30. Measurement of the νµ energy spectrum with IceCube-79: M. G. Aartsen et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-017-5261-3
  31. Improved γ/hadron separation for the detection of faint γ -ray sources using boosted decision trees: Maria Krause et al., Astroparticle Physics, doi: 10.1016/j.astropartphys.2017.01.004
  32. Performance and optimization of support vector machines in high-energy physics classification problems: M.Ö. Sahin et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, doi: 10.1016/j.nima.2016.09.017