Publications and talks


  • T. Bister, M. Erdmann, U. Köthe, J. Schulte, Inference of cosmic-ray source properties by conditional invertible neural networks, arXiv:2110.09493
  • M. Erdmann, J. Glombitza, G. Kasieczka, U. Klemradt, Deep Learning for Physics Research, World Scientific, ISBN: 978-981-123-745-4,
  • The Pierre Auger Collaboration, Deep-Learning based Reconstruction of the Shower Maximum Xmax using the Water-Cherenkov Detectors of the Pierre Auger Observatory, 2021 JINST 16 P07019
  • M. O.K, K. Zhou, J. Steinheimer, A. Redelbach and H. Stoecker, An equation-of-state-meter for CBM using PointNet, JHEP 10(2021)184
  • S. Shi, L. Wang and K. Zhou, Rethinking the ill-posedness of the spectral function reconstruction – why is it fundamentally hard and how Artificial Neural Networks can help, arXiv:2201.02564
  • S. Soma, L. Wang, S. Shi and K. Zhou, Neural network reconstruction of the dense matter equation of state from neutron star observables, arXiv:2201.01756
  • L. Wang, S. Shi and K. Zhou, Automatic differentiation approach for reconstructing spectral functions with neural networks, accepted at NeurIPS2021 workshop, arXiv:2112.06206
  • L. Wang, S. Shi and K. Zhou, Reconstructing spectral functions via automatic differentiation, arXiv:2111.14760
  • Y.S. Zhao, L. Wang, K. Zhou and X.G. Huang, Detecting Chiral Magnetic Effect via Deep Learning, arXiv:2105.13761
  • Y. Wang, F. Li, Q. Li, H. Lue and K. Zhou, Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning, Phys. Lett. B 822(2021)136669
  • S. Shi, K. Zhou, J. Zhao, S. Mukherjee and P. Zhuang, Heavy Quark potential in quark-gluon plasma: deep neural networks meets lattice quantum chromodynamics, Phys. Rev. D 105(2022), 014017
  • L. Jiang, L. Wang and K. Zhou, Deep learning stochastic processes with QCD phase transition, Phy. Rev. D103(2021)11,116023
  • K. Taradiy, K. Zhou, J. Steinheimer, R. V.Poberezhnyuk, V. Vovchenko and H. Stoecker, Machine learning based approach to fluid dynamics, arXiv:2106.02841
  • M. O.K, J. Steinheimer, K. Zhou, A. Redelbach and H. Stoecker, Deep learning based impact parameter determination for the CBM experiment, Particles 4(2021)1, 47-52
  • Max Fischer, Eileen Kuehn, Manuel Giffels, Matthias Schnepf, Stefan Kroboth and Oliver Freyermuth COBalD - The opportunistic balancing daemon, 10.5281/zenodo.1887872, April 2020
  • Manuel Giffels, Matthias Schnepf, Eileen Kuehn, Stefan Kroboth, Rene Caspart, Ralf Florian von Cube, Max Fischer and Peter Wieneman TARDIS - Transparent Adaptive Resource Dynamic Integration System, 10.5281/zenodo.2240605, Juni 2020
  • M. O.K, J. Steinheimer, K. Zhou, A. Redelbach and H. Stoecker, A fast centrality-meter for heavy-ion collisions at the CBM experiment, Phys. Lett. B 811(2020) 135872
  • P. Thaprasop, K. Zhou, J. Steinheimer and C. Herold, Unsupervised Outlier detection in heavy-ion collisions, Phys. Scripta 96(2021)6, 064003
  • L. Benato, E. Buhmann, M. Erdmann, P. Fackeldey, J. Glombitza, N. Hartmann, G. Kasieczka, W. Korcari, T. Kuhr, J. Steinheimer, H. Stöcker, T. Plehn, K. Zhou, Shared Data and Algorithms for Deep Learning in Fundamental Physics, arXiv:2107.00656
  • E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger, Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network, arXiv:2102.12491
  • E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger, Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed, arXiv:2005.05334
  • Y. L. Du, K. Zhou, J. Steinheimer, L. G. Pang, A. Motornenko, H. S. Zong, X. N. Wang and H. Stöcker, Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning, arXiv:1910.11530 [hep-ph]
  • F. Bührer, F. Fischer, G. Fleig, A. Gamel, M. Giffels, T. Hauth, M. Janczyk, K. Meier, G. Quast, B. Roland, U. Schnoor, M. Schumacher, D. von Suchodoletz and B. Wiebelt, Dynamic Virtualized Deployment of Particle Physics Environments on a High Performance Computing Cluster, Comput Softw Big Sci 3, 9 (2019), arXiv:1812.11044
  • J. Steinheimer, L. Pang, K. Zhou, V. Koch, J. Randrup and H. Stoecker, A machine learning study to identify spinodal clumping in high energy nuclear collisions, JHEP 1912, 122 (2019)
  • R. Lenkiewicz, A. Meistrenko, H. van Hees, K. Zhou, Z. Xu and C. Greiner, Kinetic approach to a relativistic BEC with inelastic processes, Phys. Rev. D100, no. 9, 091501 (2019)
  • L. Pang, K. Zhou and X. Wang, Interpretable deep learning for nuclear deformation in heavy ion collisions, arXiv:1906.06429 [nucl-th]
  • T. Bister, M. Erdmann, J. Glombitza, N. Langner, J. Schulte, M. Wirtz, Identification of Patterns in Cosmic-Ray Arrival Directions using Dynamic Graph Convolutional Neural Networks, arXiv:2003.13038
  • M. Erdmann, F. Schlueter, R. Smida, Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning, JINST 14 (2019) P04005, arXiv:1901.04079
  • M. Erdmann, E. Geiser, Y. Rath, M. Rieger, Lorentz Boost Networks: Autonomous Physics-Inspired Feature Engineering, JINST 14 (2019) P06006, arXiv:1812.09722