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| Title: | Estimating the thermal and electrical conductivities in a Hadron resonance gas with van der Waals interaction |
| Authors: | Kumar Pradhan, Kshitish Sahu, Dushmanta Scaria, Ronald Sahoo, Raghunath |
| Issue Date: | 2025 |
| Publisher: | Elsevier B.V. |
| Citation: | Kumar Pradhan, K., Sahu, D., Scaria, R., & Sahoo, R. (2025). Estimating the thermal and electrical conductivities in a Hadron resonance gas with van der Waals interaction. Journal of Subatomic Particles and Cosmology, 4. https://doi.org/10.1016/j.jspc.2025.100204 |
| Abstract: | Transport properties act as crucial probes in describing the dynamical evolution of the strongly interacting matter produced in ultra-relativistic heavy-ion collisions. Their dependencies on parameters like temperature (T[jls-end-space/]) and chemical potential (μB[jls-end-space/]) can help us locate the phase boundary in the QCD phase diagram. Transport coefficients like electrical conductivity (σel[jls-end-space/]) can be used to quantify the rate of decay of the magnetic field produced in the initial non-central heavy-ion collisions, whereas thermal conductivity (κth[jls-end-space/]), which involves the relative flow of energy, is an important parameter to be studied in highly baryon-rich environments. In this work, we perform a study of the thermal and electrical conductivities in a hadron resonance gas along with the van der Waals interaction. Both the attractive and repulsive interactions between the meson-meson and (anti)baryon-(anti)baryon have been taken care of within the model. The conductivities have been calculated by using the Boltzmann Transport Equation (BTE) under the Relaxation Time Approximation (RTA), and then the temperature and baryochemical potential dependencies of these quantities are studied within the model. Both conductivities were found to decrease with T and μB[jls-end-space/]. The effect of van der Waals interaction is clearly observed towards higher baryon chemical potentials. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. |
| URI: | https://dx.doi.org/10.1016/j.jspc.2025.100204 https://dspace.iiti.ac.in/handle/123456789/19211 |
| ISSN: | 3050-4805 |
| Type of Material: | Journal Article |
| Appears in Collections: | Department of Physics |
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