TackTDEs SIGNED

Project "TackTDEs" data sheet

The following table provides information about the project.

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 Project objective

When a star gets too close to a supermassive black hole, it gets disrupted by the strong tidal forces of the compact object. A few dozen of these tidal disruption events (TDEs) have been discovered to date and a much larger number is expected from current and near-future missions. The observed signal contains rich information about the mechanisms and conditions at play around black holes in otherwise quiescent galaxies. A full exploitation of the huge predictive power of TDEs urgently requires to improve our understanding of their dynamics and the associated emission. Through a combination of analytical and numerical tools, the proposed research aims at studying the different phases of these events considering crucial physical processes missing from previous investigations. After the star has been disrupted, the debris evolves into an elongated gas stream that partially falls back towards the black hole. The researcher will determine how the geometry of this matter evolves under the influence of stellar magnetic field and rotation. As this stream comes back to pericentre, general-relativistic effects cause it to self-cross, which initiates the formation of an accretion disc and could participate to the emission of TDEs in the optical and UV bands. The researcher will determine the influence of the black hole spin on this process, focusing on the delay of stream-stream collision induced by Lense-Thirring precession. He will also investigate photon diffusion during disc formation to determine the evolution of the emerging shock-powered lightcurve. After the gas has settled, viscous torques drive the accretion of the stellar matter onto the black hole, producing X-ray radiation. The researcher will study this ultimate phase to evaluate the energy output and the origin of angular momentum transport in TDEs. This work will result in great advances in this research field on the theoretical side that will be used to better exploit observational data.