Constraining the r-mode saturation amplitude from a hypothetical detection of r-mode gravitational waves from a newborn neutron star: Detection strategies and machine learning algorithms

Newborn neutron stars have been thought of as very promising sources of detectable gravitational waves since the discovery (1998) that r-mode oscillations of neutron stars are driven unstable by gravitational radiation. Important factors that increase the likelihood for such a detection are: high rotational velocities of newborn neutron stars, high saturation amplitudes of the r-mode oscillations and also validity of the r-mode gravitational radiation theories that claim to have explained the spin down of newborn neutron stars to the observed rotational velocities. My present work is focused on the experimental and computational part for the r-mode gravitational wave detection as well as the physical implications from such a detection. My contribution in the field consists of the following topics: (a) derive an expression for the moment of inertia (MOI) of neutron stars as a function of observables from a hypothetical r-mode gravitational wave detection, (b) demonstrate that by constraining the MOI of a neutron star from a hypothetical r-mode detection, we can set constraints on the equation of state of the matter of the neutron star, (c) for each candidate equation of state, present a method that sets an upper bound on the saturation amplitude of the r-mode oscillations, (d) perform a sensitivity study on the existing clustering algorithms using the stochastic pipeline of the LIGO collaboration and (e) demonstrate the validity of machine learning algorithms as decision making algorithms whose performance is at least as good as that of the existing clustering algorithms.