Speaker
Description
In the next decade, new gravitational-wave detectors (like Einstein Telescope and Cosmic Explorer) will operate with a sensitivity gain of about one order of magnitude compared to current instruments. This improvement will enable the detection of binary black hole coalescences up to redshift z∼10, as well as hundreds of merger events per month. Such capabilities will make these detectors powerful instruments for cosmology. The main challenge lies in data analysis: Bayesian inference of binary black hole signals requires exploring a 15-dimensional parameter space with highly complex waveform models. One possible way to address this problem is to rely on approximate methods, such as the Fisher approach, which assumes that gravitational-wave posteriors are Gaussian. However, this approximation fails when the posteriors exhibit multimodality or strong non-Gaussian features. This limitation can be alleviated by including higher-order terms in the Taylor expansion of the log-likelihood. In this work, we demonstrate the effectiveness of the DALI (Derivative Approximation for LIkelihoods) approach, which provides reliable gravitational-wave posteriors while remaining computationally efficient.
