Speakers
Description
We investigate the interplay between saturation dynamics and fluctuating hotspots on midrapidity flow observables in Pb+Pb collisions at the LHC energies using the new Monte Carlo EKRT (MC-EKRT) initial-state event generator [1,2]. We demonstrate that this type of analysis can be efficiently and accurately performed using pre-trained neural networks [3] to predict flow observables directly from the initial energy-density profiles, despite the networks having no prior information about the MC-EKRT initial state, or hotspots, during the training phase. We show that saturation intensity significantly affects the ratio between the flow coefficients $v_3$ and $v_2$. Incorporating fluctuating hotspots into the nucleon structure enhances saturation effects, and improves the agreement with the experimental data. Moreover, the collision-energy dependence of the flow coefficients obtained using the MC-EKRT initial states with hotspots is improved in comparison with the earlier event-by-event EKRT model. Additionally, we show that the minijet-multiplicity originating fluctuations of the saturation scale present in MC-EKRT, along with the inclusion of hotspots, are essential for accurately describing the large-multiplicity tail of the measured charged-hadron distributions.
[1] H. Hirvonen, M. Kuha, J. Auvinen, K. J. Eskola, Y. Kanakubo, H. Niemi, Phys.Rev.C 110 (2024) 3, 034911
[2] M. Kuha, J. Auvinen, K. J. Eskola, H. Hirvonen, Y. Kanakubo, H. Niemi, arXiv:2406.17592 [hep-ph]
[3] H. Hirvonen, K. J. Eskola, H. Niemi, Phys.Rev.C 108 (2023) 3, 034905
| Category | Theory |
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