It has now been two decades since Bayesian parameter estimation routines were first introduced for studies in gravitational waves and cosmology. Bayesian approaches have become extremely important for these disciplines, their use is ubiquitous and helped to significantly push our knowledge of the universe and its history.

On 14 September 2015, Advanced LIGO made the breakthrough first detection of gravitational waves - ripples in the fabric of spacetime caused by accelerating massive objects, predicted by Einstein in 1916 as a consequence of general relativity theory. Since then, Advanced LIGO/Virgo have announced the detection of many more binary star mergers.

Professor Meyer will review the development of the statistical methods that enabled the estimation of the waveform parameters, such as the masses of the black holes and their distance to earth. A major focus of current research has been on a more robust nonparametric noise characterization aiming at a simultaneous estimation of the noise spectral density and gravitational wave parameters. This is critical now for LIGO/Virgo but it might prove to be even more important for the Laser Interferometer Space Antenna (LISA) mission to be launched in 2034. If robust methods can be developed to handle LISA instrument and galactic confusion noise, LISA will let us go further than any alternative towards the detection of a primordial gravitational wave background, the remnant of the Big Bang.

Renate Meyer is a Professor in the Department of Statistics of the University of Auckland. After obtaining an MSc and PhD in Mathematics and Statistics from the University of Aachen, Germany, she took up a lectureship at the University of Auckland in 1994. In 2018, she was awarded a James Cook Research Fellowship by the Royal Society of NZ Te Apārangi for research on noise characterization studies for laser-interferometric gravitational wave observatories and she is co-chair of the NZ Astrostatistics and General Relativity Group of the International LISA Consortium.

Renate has wide research interests in applied Bayesian inference and MCMC methods. She has been collaborating for many years with astrophysicists on the analysis of gravitational wave data. Her research areas comprise time series analysis with applications in astrophysics, state-space modelling in ecology, multivariate modelling using copulas, survival analysis in medical statistics, and stochastic volatility models for financial time series.

Refreshments will be served in the basement foyer of building 303 (303-B00L2) from 5:45pm, prior to the lecture.

Image courtesy of NASA