Fluctuating temperature and baryon chemical potential in heavy-ion collisions and the position of the critical end point in the effective QCD phase diagram

Abstract

We use the linear sigma model with quarks to locate the critical end point in the effective QCD phase diagram accounting for fluctuations in temperature and quark chemical potential. For this purpose, we use the non-equilibrium formalism provided by the superstatistics framework. We compute the effective potential in the high-and low-temperature approximations up to sixth order and include the contribution of ring diagrams to account for plasma screening effects. We fix the model parameters from relations between the thermal sigma and pion masses imposing a first order phase transition at zero temperature and a finite critical value for the baryon chemical potential that we take of order of the nucleon mass. We find that the CEP displacement due to fluctuations in temperature and/or quark chemical potential is almost negligible.

Impact of a thermal medium on newly observed $Z_{cs}(3985)$ resonance and its $ b $-partner

“…Meanwhile, the features of matter under extreme conditions of high temperatures and/or densities have attracted the curiosity of high energy physicists [28][29][30][31][32]. QCD, the theory of strong interactions, expects that nuclear matter undergoes a phase transition from a state of deconfined quarks and gluons forming a new state of matter, named as the quark-gluon plasma (QGP), at a critical temperature T c ∼ = 155 MeV (∼ 10 12 K) [33][34][35][36] which is in excellent agreement with the freeze-out temperature for hadrons measured by the ALICE collaboration at LHC producing 4 He and 4 He nuclei in Pb-Pb collisions at √ s N N = 2.76 TeV in the rapidity range |y| < 1 [37,38].…”

The 3d O(4) model as an effective approach to the QCD phase diagram

“…Many such models have been studied. Examples, and corresponding references, include the Nambu-Jona-Lasinio model [7], and more specifically the Polyakov-Nambu-Jona-Lasinio model [8], the linear σ-model [9], holographic approaches to QCD [10], the Polyakov quark meson model [11], as well as methods like the Dyson-Schwinger equation [12], the mean-field approximation [13] and finite-size scaling [14].…”

Fluctuations of water quality time series in rivers follow superstatistics

“…These models generate heavy-tailed non-Gaussian distributions by a simple mechanism, namely the superposition of simpler distributions whose relevant parameters are random variables, fluctuating on a much larger timescale. Originating in turbulence modeling (Beck, 2007), superstatistics has been applied to many physical systems, such as plasma physics (Livadiotis, 2017;Davis et al, 2019), Ising systems (Cheraghalizadeh et al, 2021), cosmic ray physics (Yalcin and Beck, 2018;Smolla et al, 2020), self-gravitating systems (Ourabah, 2020), solar wind (Livadiotis et al, 2018), high energy scattering processes (Beck, 2009;Sevilla et al, 2019;Ayala et al, 2020), ultracold gases (Rouse and Willitsch, 2017), and non-Gaussian diffusion processes in small complex systems (Chechkin et al, 2017;Itto and Beck, 2021). Furthermore, the framework has successfully been applied to completely different areas, such as modeling the power grid frequency (Schä fer et al, 2018), wind statistics (Weber et al, 2019), air pollution (Williams et al, 2020), bacterial DNA (Bogachev et al, 2017), financial time series (Gidea and Katz, 2018;Uchiyama and Kadoya, 2019), rainfall statistics (De Michele and Avanzi, 2018), or train delays (Briggs and Beck, 2007).…”