| Medium effects on charmonium production at ultrarelativistic energies available at the CERN Large Hadron Collider K Zhou, N Xu, Z Xu, P Zhuang Phys. Rev. C 89 (2014), no.5, 054911, 2014 | 287 | 2014 |
| An equation-of-state-meter of quantum chromodynamics transition from deep learning LG Pang, K Zhou, N Su, H Petersen, H Stöcker, XN Wang Nature communications 9 (2018), no.1, 210, 2018 | 179 | 2018 |
| Regressive and generative neural networks for scalar field theory K Zhou, G Endrődi, LG Pang Phys. Rev. D 100 (2019), 011501(R), 2018 | 110 | 2018 |
| Heavy flavors under extreme conditions in high energy nuclear collisions J Zhao, K Zhou, S Chen, P Zhuang Progress in Particle and Nuclear Physics 114 (2020) 103801, 2020 | 99 | 2020 |
| Heavy ions at the Future Circular Collider A Dainese, UA Wiedemann, N Armesto, D d'Enterria, JM Jowett, ... CERN-TH-2016-107, 2016 | 89 | 2016 |
| Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning YL Du, K Zhou, J Steinheimer, LG Pang, A Motornenko, HS Zong, ... The European Physical Journal C 80 (2020), 516, 2019 | 80 | 2019 |
| A machine learning study to identify spinodal clumping in high energy nuclear collisions J Steinheimer, LG Pang, K Zhou, V Koch, J Randrup, H Stoecker JHEP 1912 (2019), 122, 2019 | 67 | 2019 |
| High energy nuclear physics meets Machine Learning WB He, YG Ma, LG Pang, H Song, K Zhou Nucl. Sci. Tech 34 (88), 2023 | 63 | 2023 |
| Exploring QCD matter in extreme conditions with Machine Learning K Zhou, L Wang, LG Pang, S Shi Progress in Particle and Nuclear Physics 135, 104084, 2024 | 60 | 2024 |
| Heavy quark potential in the quark-gluon plasma: Deep neural network meets lattice quantum chromodynamics S Shi, K Zhou, J Zhao, S Mukherjee, P Zhuang Physical Review D 105 (1), 014017, 2022 | 55 | 2022 |
| Thermalization of gluons with Bose-Einstein condensation Zhe Xu, Kai Zhou, Pengfei Zhuang, Carsten Greiner Physical Review Letters 114 (2015), no.18, 182301, 2015 | 55* | 2015 |
| Continuum-extrapolated NNLO Valence PDF of Pion at the Physical Point X Gao, A D. Hanlon, N Karthik, S Mukherjee, P Petreczky, P Scior, S Shi, ... Physics Review D 106 (114510), 2022 | 47 | 2022 |
| Unsupervised outlier detection in heavy-ion collisions P Thaprasop, K Zhou, J Steinheimer, C Herold Physica Scripta 96 (6), 064003, 2021 | 47 | 2021 |
| Neural network reconstruction of the dense matter equation of state from neutron star observables S Soma, L Wang, S Shi, H Stoecker, K Zhou Journal of Cosmology and Astroparticle Physics 8 (2022), 071, 2022 | 44 | 2022 |
| Nonequilibrium photon production in partonic transport simulations M Greif, F Senzel, H Kremer, K Zhou, C Greiner, Z Xu Phys. Rev. C 95 (2017), no.5, 054903, 2016 | 42 | 2016 |
| ψ^\prime Production and B Decay in Heavy Ion Collisions at LHC Baoyi Chen, Yunpeng Liu, Kai Zhou, Pengfei Zhuang Phys. Lett. B 726 (4-5), 725–728, 2013 | 42* | 2013 |
| Reconstructing spectral functions via automatic differentiation L Wang, S Shi, K Zhou Physical Review D 106 (5), L051502, 2022 | 40 | 2022 |
| Reconstructing the neutron star equation of state from observational data via automatic differentiation S Soma, L Wang, S Shi, H Stöcker, K Zhou Physical Review D 107 (8), 083028, 2023 | 39 | 2023 |
| A fast centrality-meter for heavy-ion collisions at the CBM experiment MO Kuttan, J Steinheimer, K Zhou, A Redelbach, H Stoecker Physics Letters B 811, 135872, 2020 | 38 | 2020 |
| Thermal Charm and Charmonium Production in Quark Gluon Plasma Kai Zhou, Zhengyu Chen, Carsten Greiner, Pengfei Zhuang Phys. Lett. B 758 (2016), 434-439, 2016 | 38 | 2016 |
Horst StoeckerFiAS & ITP Johann Wolfgang Goethe Universitaet Frankfurt am Main and GSI Helmholtzzentrum fueruni-frankfurt.de üzerinde doğrulanmış e-posta adresine sahip