Estimation of Unmodeled Gravitational Wave Transients with Spline Regression and Particle Swarm Optimization

Detecting and estimating unmodeled transient gravitational wave (GW) signals in noisy data is a major challenge in GW data analysis. This paper explores a solution that combines spline based regression with Particle Swarm Optimization for knot placement and directional parameter estimation. First, the estimation of binary black hole merger signals in data from a single GW detector is used as a testbed problem to quantify the relative performance of several algorithmic design choices. The method resulting from this study is then adapted to the case of data from a network of geographically distributed GW detectors. Simulation results show fairly good directional estimates for black hole mergers, with reasonable fidelity in the reconstruction of both GW polarization waveforms, at a signal to noise ratio capped at 15 for any single detector in the network. This promising performance suggests that the method should be developed further and applied to other types of GW transients.