UCD Team Enhances Black Hole Analysis with Novel Wave Method

Researchers from University College Dublin, University of Portsmouth, and University of Southampton have developed a novel analytical method for gravitational-wave data, published in Nature Astronomy, which incorporates multiple gravitational-wave models into a single algorithm to refine parameter estimation of black hole characteristics. This approach allows for the collective use of individual models, prioritising those with greater accuracy, to constrain properties such as black hole mass and spin, derived from observations of their behaviour and interactions with surrounding matter and energy. Since the initial detection of gravitational waves in 2015 – a discovery recognised with the Nobel Prize – this technique promises to enhance ground-based gravitational wave astronomy by providing a more accurate interpretation of spacetime ripples generated by accelerating massive objects, offering a fundamentally different observational modality compared to traditional electromagnetic radiation-based astronomy. Dr Sarp Akcay, of the UCD School of Mathematics and Statistics, notes that the method enables the incorporation of numerous state-of-the-art gravitational wave models into a single parameter estimation run, effectively combining results based on model accuracy.

Gravitational Wave Analysis

The research, recently published in Nature Astronomy, centres on an algorithm capable of integrating multiple gravitational wave models into a single parameter estimation process, effectively leveraging the strengths of each individual model to constrain black hole properties. This contrasts with conventional approaches, which typically rely on a single, often simplified, waveform model for analysis, potentially introducing systematic errors in the derived parameters.

The core innovation lies in the algorithm’s ability to prioritise more accurate models within the ensemble, weighting their contributions based on performance against observational data and theoretical expectations. As co-author Dr Sarp Akcay, of the UCD School of Mathematics and Statistics, explains, “Our method enables the incorporation of many state-of-the-art gravitational wave models into one parameter estimation run. ”

The algorithm then combines the results by prioritising the more accurate models. This prioritisation is achieved through Bayesian model selection, a statistical technique used to evaluate the relative plausibility of different models given the observed data, thereby enhancing the robustness and reliability of the parameter estimates.

Gravitational wave astronomy, inaugurated by the landmark detection of observatories including the Laser Interferometer Gravitational-Wave Observatory (LIGO), Virgo, and KAGRA, is enhancing their capacity to unravel the mysteries of the cosmos.