Modeling accreting black holes

I study accreting supermassive black holes by combining general relativistic magnetohydrodynamical (fluid plasma) simulations with a general relativist ray tracing code (RAPTOR). I am one of the core developers of RAPTOR. I also use this code to generate Virtual Reality visualization that are used for public outreach and education.

My main focus is the modeling of the accreting black holes in the centre of our own Milky Way, called Sagittarius A*, and the black hole in centre of the galaxy Messier 87. These two sources are the primary targets of the Event Horizon Telescope. In this work I focus on the effect of electron acceleration in the jet.

Kinetic plasma simulations

Black holes launch relativistic jets, these jets are observered over the entire electromagnetic spectrum. The emission is highly non-thermal. They was jets accelerated the electrons is highly uncertain. To unravel this mystery, I perform kinetic plasma simulations, the first-principle simulations teach us how magnetic energy can dissipated and particles get accelerated.

Machine Learning

The extraction black hole parameters, such as mass, spin, and accretion rate from potential VLBI images of black holes is of vital importance to accurately test our understand of General Relativity and plasma physics. Convolutional neural network are known to accurately predict patterns. I am involved in the development of a bayesian neural network. The network is trained on state-of-the art simulations of accretion black holes.