Michail Chabanov

Since September 2019 I am a Ph.D. student in the Relativistic Astrophysics/ Numerical Relativity group of Prof. Dr. Luciano Rezzolla at the Institute for Theoretical Physics in Frankfurt am Main, Germany. Previously, I have obtained my Master’s degree also under the supervision of Prof. Dr. Rezzolla where I have been working on “3+1 Formulations of Relativistic Dissipative Hydrodynamics and Numerical Implementations”. A preprint version of the corresponding paper can be found here: https://arxiv.org/abs/2102.10419.

Research Interests

My research interest is on the evolution and dynamics of binary neutron star mergers. More specifically, I am interested in the application of dissipative physics in numerical simulations of binary neutron star mergers. Dissipative effects which for example include the resistance of a liquid to shearing motion through viscosity (think of honey!) are hypothesized to play an important role in the evolution of binary neutron star mergers.

On the one hand, they can stem from the microphysics of the underlying matter involved in the merger. This offers the opportunity to study the corresponding microphysical processes through the combination of numerical modelling and astronomical observations.

On the other hand, “effective” dissipative forces can arise from the complex turbulent motion observed in numerical simulations of the post-merger remnant. In fact, it is hypothesized that these “effective” dissipative forces lead to a large contribution to the matter outflow from binary neutron star mergers. The outflowing material undergoes rapid nuclear reactions and subsequent decay creating heavy elements in our galaxy as well as transient thermal emission. This transient astronomical event is termed “kilonova” and was for the first time directly associated to a binary neutron star merger in 2017 by the event AT 2017gfo (GW170817). Thus, the modelling of matter ejection from remnants of binary neutron star mergers constitutes an important piece in the interpretation and understanding of observational signals.