Farr, Benjamin

Nortwestern University

Monday October 17, 2011


Bayesian Parameter Estimation from Gravitational Wave Signals



Once LIGO has made its first gravitational wave detection, it can finally begin operation as a true observatory.  For the first time astronomical observations can be performed outside of the electromagnetic spectrum, and we can learn a great deal more about compact objects (e.g. neutron stars, black holes) than possible using light alone.  To be able to make these statements however, there must be a framework in place that is capable of characterizing the signals to the greatest extent that the data will allow.  Bayesian parameter estimation methods satisfy this need by providing a means to determine the full multidimensional probability density function for the parameters of the system.  In this talk I will give an introduction to gravitational waves, their sources, and characterization of these sources using Bayesian methods, with particular emphasis on Markov Chain Monte Carlo. 


Friday October 21, 2011


Gravitational Wave Science in the High School Classroom. 



Gravitational waves have the potential to bring astronomy into the next era by providing an entirely new means of observing astronomical phenomena.  By measuring fluctuations down to the sub-attometer scale, scientists are hoping to measure the gravitational effects of extreme cosmic events happening millions of parsecs away.  This widely multidisciplinary work encompasses fields ranging from astrophysics to quantum optics.  I will discuss a few examples of how I integrated gravitational wave science into a high school astronomy curriculum, where students learned about a variety of topics in the field, with particular focus on astrophysical sources, detector technology, and data analysis techniques.



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Center for Computational Relativity and Gravitation, School of Mathematical Sciences,
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