You can find all my publications on InspireHEP.
My research explores the thermodynamic properties of dense, strongly interacting matter. These properties are encoded in the QCD equation of state (EOS). Studying the EOS in extreme environments — such as supernovae, neutron stars and their mergers, and relativistic collisions of heavy nuclei — helps address key questions about strongly-interacting systems:
- Does QCD matter undergo a first-order phase transition at finite baryon density?
- How does the isospin dependence of the EOS evolve with density and temperature?
- How do nuclear matter degrees of freedom and their properties change as the thermodynamic variables are varied?
To investigate these questions, I model relativistic heavy-ion collisions using dynamic microscopic transport simulations. A lot of my research focuses on developing flexible EOS meta-models which constitute an adjustable input to the simulations. Constraining the QCD EOS and uncovering the QCD phase diagram relies on comparisons to experimental data, which in turn require extensive use of high-performance computing. Recently, to address the high computational cost of such analyses, I have begun incorporating machine learning techniques into my work.
Currently, I am working on the following projects:
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Locating the critical point for the hadron to quark-gluon plasma phase transition from finite-size scaling of proton cumulants in heavy-ion collisions
In this first analysis of experimental data to locate the QCD critical point in a range consistent with modern theoretical predictions, we study fluctuations of the number of protons produced in collisions of gold nuclei. By adjusting the portion of the collision we analyze (using proton fluctuations measured in different rapidity bin widths), we test how fluctuations scale with system size. At collision energies above \( \sqrt{s_{\rm{NN}}} = 7.7~\rm{GeV}\), we observe that the data follows Taylor’s law, which suggests scale-free behavior: something expected near a critical point. Our analysis indicates a critical point near a baryon chemical potential of \( \mu_B = 580 \pm 30~\rm{MeV} \).
You can read this work here.
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Volume dependence of second-order baryon cumulant ratio in hadronic transport simulations
Using hadronic transport simulations of symmetric nuclear matter with mean-field interactions in a box with periodic boundary conditions, I study the dependence of the ratio of variance to mean on the considered subvolume and technical parameters of the simulation such as the number of test particles per particle, mean-field lattice spacing, and smearing radius for density calculation. I find that the results do not depend on the number of test particles (as long as it's large enough to warrant a reliable density calculation) or the smearing radius. Further, I show that the dependence on the microscopic scale -- the lattice spacing -- is suppressed as compared to the dependence on the subvolume, which follows a power law. Finally, I show that in the limit of an infinite subvolume and infinitesimal lattice spacing, the simulation results approach the value expected from the underlying mean-field EOS.
The manuscript is currently in preparation.
