Probing criticality with deep learning in relativistic heavy-ion collisions
arXiv:2107.11828 · doi:10.1016/j.physletb.2022.137001
Abstract
Systems with different interactions could develop the same critical behaviour due to the underlying symmetry and universality. Using this principle of universality, we can embed critical correlations modeled on the 3D Ising model into the simulated data of heavy-ion collisions, hiding weak signals of a few inter-particle correlations within a large particle cloud. Employing a point cloud network with dynamical edge convolution, we are able to identify events with critical fluctuations through supervised learning, and pick out a large fraction of signal particles used for decision-making in each single event.
10 pages, 5 figures, version accepted by Physics Letters B
References in corpus (9)
- The QCD transition temperature: results with physical masses in the continuum limit II.
- Droplets in the cold and dense linear sigma model with quarks
- Density fluctuations in the presence of spinodal instabilities
- QCD phase transitions via a refined truncation of Dyson-Schwinger equations
- Critical Opalescence in Baryonic QCD Matter
- Equation of state dependence of directed flow in a microscopic transport model
- Nuclear liquid-gas phase transition with machine learning
- Multiplicity Scaling of Light Nuclei Production in Relativistic Heavy-Ion Collisions
- An equation-of-state-meter for CBM using PointNet
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