David Hendriks
Credit: David Hendriks
David Hendriks is a PhD student at the University of Surrey where he works under guidance of Dr. Robert Izzard in the Surrey Stars team. His current research focuses on the interaction between binary stars and accretion disks. Specifically the loss of material from accretion disks and the impact that this has on the orbital evolution.
He obtained his Bachelor and Master degree at the University of Amsterdam after doing his master thesis on synthetic models of black hole populations and the impact of (pulsational) pair instability supernovae on the observable gravitational waves from these black holes.
| Date | Talk at GAS meeting |
| 15 Apr 2021 | A Zoom Talk: The creation of gravitational waves and what the observations could tell us about the progenitor systems In this talk I will give an overview on the topic of gravitational waves. After a short introduction I will cover the theory of gravitational waves and show which systems and phenomena would emit gravitational waves.
I will then talk about show the history the different observation methods, the current (and future) detectors and the first observations of gravitational waves.
Gravitational wave observations give us a new way of observing the universe, and, as such, also new opportunities to narrow down the uncertainties we have within stellar evolution, or even confirm or reject different possible scenarios. In the last part of this talk I will zoom in on the science we do with the observations of gravitational waves, specifically the use of synthetic populations that are matched to the observations. Several assumptions for e.g. the supernova kick velocity or the common envelope ejection efficiency are used as input for these synthetic populations of stars, and it has been shown that some of these can significantly affect the merger rate. By evolving populations of stars we can estimate these merger rates for different types of binary systems, and compare that to the observed merger rate, allowing us to infer the input values that best match the observations.
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