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Zoltan Juhasz (Pannon Egyetem)28/05/2026, 15:50Lecture
In this talk, we plan to report the results of our performance optimisation effort aimed at speeding up a GPU-accelerated 2D Particle-in-Cell plasma simulation code, following an international plasma simulation benchmarking effort of 19 leading plasma research groups. We studied the effects of memory management, data movements, the choice and implementation of the Poisson solver, the use of...
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Bálint Tóth (University of Pannonia)28/05/2026, 16:10Lecture
Particle-in-Cell (PIC) simulation is an important tool in plasma science, where certain properties and behaviour can only be examined by simulations. Due to the large number of particles and simulation cycles, these simulations are extremely time-consuming and can be executed in acceptable time only with parallel implementations. A crucial step in the simulation is solving the Poisson equation...
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Aswathy Menon K R (Indian Institute of Technology Indore)28/05/2026, 16:30Lecture
One of the major open problems in the collider physics community is understanding the onset of quark–gluon plasma (QGP) signatures. Collisions of Oxygen nuclei provide a golden opportunity to probe the emergence of collective phenomena in collider experiments. Additionally, $^{16}\rm O$ nuclei are theorized to possess a clustered nuclear structure, where $\alpha$-particles occupy the corners...
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209. Impact of nuclear geometry in symmetry plane correlations in OO and Ne–Ne collisions at the LHCSuraj Prasad (HUN-REN Wigner Research Centre for Physics)28/05/2026, 16:50Lecture
The correlations among different symmetry planes, otherwise known as the symmetry plane correlations (SPCs), in heavy-ion collisions are driven by the corresponding participant plane correlations and are sensitive to the transport properties of the system formed. The participant plane correlations can vary with the fluctuating nuclear geometry and can therefore be influenced by the nuclear...
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Anisa Khatun28/05/2026, 17:10Lecture
Modern high-energy physics analyses rely heavily on large-scale Monte Carlo (MC)
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simulations for machine-learning training, efficiency corrections, and systematic
studies. For rare-signal workflows, obtaining sufficiently large reconstructed-level
signal samples often require computationally expensive MC campaigns with large
CPU and storage demands.
This work explores the use of...
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