Statistical host identification as a test of dark energy
The past four years have witnessed dramatic discoveries surrounding the birth of gravitational wave astronomy. By their nature, gravitational waves are ideal probes with which to test the laws of gravity – something currently under scrutiny due to unresolved questions about the dark sector of the universe.  In this proposal I lay out an ambitious campaign to determine the behaviour of gravity over cosmological distances, using the upcoming surge of gravitational wave data. I will achieve this by developing the burgeoning technique of `Statistical Host Identification’ of gravitational wave sources. This new tool will enable me to test gravity using hundreds of future detections of binary black holes at high redshifts, even without direct redshift information – thus removing a major obstacle for gravitational wave cosmology.  I will phrase my constraints in terms of model-independent parameters that quantify physically viable deviations from General Relativity, making my results applicable to virtually any dark energy or extended gravity model. In this way, I can validate or eliminate the space of theories in current literature.  To model the distribution of gravitational wave events and their host galaxies, I will construct an approximate simulation that operates with generalised, model-independent gravitational laws – the first ever simulation to do this. This tool enables me to additionally use information about gravity from non-linear scales of cosmological structure. This regime is virtually untouched by current comparable work, and is a prime target for the next generation of galaxy surveys. My key objectives are: i) To develop the calculations and software tools needed to apply gravitational wave Statistical Host Identification, in theories of gravity beyond General Relativity; ii) To use these tools to obtain powerful new constraints on extended gravity models, thereby confirming or ruling out a leading candidate explanation for the nature of dark energy.