For the complete, up-to-date list of my publications, see my INSPIRE-HEP author profile.
Published in arXiv preprint arXiv:2607.28265, 2026
We introduce a slice-within-Gibbs sampler within Jim, and show how it can infer binary neutron star properties at unprecedented sampling speeds.
Published in arXiv preprint arXiv:2607.28438, 2026
We use jester to provide an extensive projection study on multi-messenger inference on binary neutron star mergers observed in future detectors.
Published in arXiv preprint arXiv:2607.03045, 2026
We provide updates on the NMMA software, re-analyze the multi-messenger GW170817 merger and provide projections for future detectors.
Published in arXiv preprint arXiv:2606.19201, 2026
We assess the impact of a realistic duty cycle on parameter estimation of gravitational waves from binary black hole mergers.
Published in arXiv preprint arXiv:2606.06376, 2026
We show that neutron star resonance modes leave detectable imprints in the gravitational wave signal.
Published in Phys.Rev.D 114, 2025
We introduce data-driven priors for gravitational wave inference that are informed by existing knowledge on the neutron star equation of state.
Published in arXiv preprint arXiv:2510.24620, 2025
We perform multi-messenger analyses of an SSM candidate reported by the LVK, and using the follow-up from telescopes.
Published in arXiv preprint arXiv:2510.22290, 2025
We perform follow-up multi-messenger investigations of a candidate binary neutron star merger in LVK's O4a observing run, and provide projections of a similar source with future instruments.
Published in Astron.Astrophys. 704, 2025
We introduce fiesta, a GPU-accelerated pipeline for inference on kilonovae and gamma ray burst afterglows, with new surrogates for the latest models.
Published in Phys.Rev.D 113, 2025
We extend jester to account for pressure anisotropies in neutron stars and infer them with real data.
Published in Phys.Rev.D 112, 2025
Methods paper introducing jester, a GPU-accelerated pipeline for equation of state inference.
Published in Phys.Rev.D 112, 2025
We use electromagnetic follow-up observations on neutron star-black hole merger candidates to provide limits on the ejecta masses of their kilonovae.
Published in Eur.Phys.J.A 61, 2025
We develop machine learning methods to accelerate the conservative-to-primitive transformation, a costly step in binary neutron star mergers.
Published in Phys.Rev.D 112, 2024
We show how a triangular ET, using its null stream, has unique capabilities in mitigating noise transients in the detector, which would otherwise bias parameter estimation.
Published in Phys.Rev.D 111, 2024
We extend the treatment of systemtic uncertainties in kilonova light-curve inference, and demonstrate its usefulness when models are misspecified.
Published in arXiv preprint arXiv:2410.21076, 2024
We run jim, a fast inference pipeline for gravitational waves, together with harmonic, which provides fast Bayesian evidence estimates from a set of samples.
Published in Phys.Rev.D 110, 2024
We assess recent observations in light of our knowledge of the nuclear equation of state.
Published in Phys.Rev.D 110, 2024
We develop GPU-accelerated waveform approximants for binary neutron star mergers, and demonstrate fast inference for this type of GW signals.
Published in Mon.Not.Roy.Astron.Soc. 530, 2023
Running Bayesian inference on a GRB candidate, performing model selection on the type of transient with the NMMA software.