Charalampos C. Moustakidis

One of the outstanding challenges in nuclear physics is achieving a comprehensive understanding of the equation of state that governs dense nuclear matter, and, by extension, the internal composition and structure of compact astrophysical objects such as neutron stars and quark stars. The primary approach to addressing this problem has thus far been predominantly theoretical, involving the construction of equations of state based on plausible nuclear and particle physics theories. These theoretical predictions are subsequently evaluated against empirical results, which are obtained primarily from heavy-ion collision experiments and, increasingly, from astrophysical observations of compact objects. In recent years, several alternative analytical methodologies have been developed that integrate the aforementioned theoretical and empirical approaches. These methods incorporate advanced data analysis and processing techniques, including Bayesian inference, and machine-learning and deep-learning frameworks, to enhance the interpretation and constraining of the equation of state for dense nuclear matter. In this study, the aforementioned techniques, particularly machine learning and deep learning, are employed with the objective of enhancing our understanding of the equation of state by leveraging information derived from mass-radius diagrams of compact objects. More specifically, this study utilizes a large dataset of equations of state constructed using multimodal formulations of neutron stars, along with a corresponding dataset for quark stars. These equations of state are consistent with known physical constraints and have been developed to reflect the highest degree of physical plausibility. Through the application of machine-learning and deep-learning techniques, it becomes possible to infer or reconstruct the underlying equations of state based on input from mass-radius diagram data.

Neutron stars, white dwarfs and black holes are the after death remnants of massive stars. However, according to the most recent observations, the neutron stars’ maximum mass is between 2.0–2.5 [ ] while black holes of less than 5 [ ] have not yet been observed. The region between the most massive neutron star and the least massive black hole is called the mass gap. If indeed its existence is confirmed by future observations, that indicates a gap in our understanding which seeks for explanation. In addition, the existence of compact objects within the mass gap should also be supported with the help of possible new theoretical scenarios. In this study, we propose a possible explanation for the existence of compact objects within the mass gap region. Specifically, we propose that the mass gap region could be bridged by the existence of a hybrid compact object, composed of hadronic and self interacting and nonannihilating fermionic dark matter, considering that the interaction between these two fluids its only gravitational. Fundamental questions about how these objects form and how they can be detected are also addressed.

Recent experimental observation suggests that neutron decay is always accompanied by emission of electron while in 1% of cases proton is not emitted. We develop a scenario kinematically compatible with experimental observation, where neutron decay results in production of two dark matter particles of about half the mass of neutron and test properties of neutron stars with admixture of such particles. Constraints on mass and coupling to vector dark boson are obtained. The structure of the compact object is modified to a dark star with a shell of nucleonic matter around the nuclear saturation density.