A5015294647- Pulsars and Gravitational Waves Research
- Gamma-ray bursts and supernovae
- Geophysics and Gravity Measurements
- Astrophysical Phenomena and Observations
- Cosmology and Gravitation Theories
- High-pressure geophysics and materials
- Astrophysics and Cosmic Phenomena
- Atomic and Subatomic Physics Research
- Statistical and numerical algorithms
- Radio Astronomy Observations and Technology
- Magnetic confinement fusion research
- Astronomical Observations and Instrumentation
- Biomedical Text Mining and Ontologies
- Coastal and Marine Dynamics
- Meteorological Phenomena and Simulations
- Machine Learning in Healthcare
- Black Holes and Theoretical Physics
- Superconducting Materials and Applications
- Ocean Waves and Remote Sensing
- Bayesian Methods and Mixture Models
- Seismic Waves and Analysis
- Cold Atom Physics and Bose-Einstein Condensates
Australian Regenerative Medicine Institute
2024
Monash University
2018-2024
ARC Centre of Excellence for Gravitational Wave Discovery
2018-2023
Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. It method by which data used to infer sources' astrophysical properties. We introduce a user-friendly inference library for astronomy, Bilby. This python code provides expert-level infrastructure with straightforward syntax and tools that facilitate use beginners. allows users perform accurate reliable on both real, freely-available from LIGO/Virgo, simulated data. provide suite examples analysis...
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine techniques the analysis ground-based gravitational-wave detector data. Examples include improving sensitivity Advanced LIGO Virgo searches, methods fast measurements astrophysical parameters sources, algorithms reduction characterization non-astrophysical noise. These demonstrate how may be harnessed to enhance science that is possible with current future detectors.
Abstract Gravitational waves from coalescing neutron stars encode information about nuclear matter at extreme densities, inaccessible by laboratory experiments. The late inspiral is influenced the presence of tides, which depend on star equation state. Neutron mergers are expected to often produce rapidly rotating remnant that emit gravitational waves. These will provide clues extremely hot post-merger environment. This signature in contains most 2–4 kHz frequency band, outside sensitive...
Gravitational waves have been detected from the inspiral of a binary neutron-star, GW170817, which allowed constraints to be placed on neutron star equation state. The state can further constrained if gravitational postmerger remnant are detected. Postmerger waveforms currently generated by numerical-relativity simulations, computationally expensive. Here we introduce hierarchical model trained generate reliable spectra in fraction second. Our mean fitting factors 0.95, compares factor 0.93...
Detection and parameter estimation of binary neutron star merger remnants can shed light on the physics hot matter at supranuclear densities. Here we develop a fast, simple model that generate gravitational waveforms, show it be used for both detection postmerger remnants. The consists three exponentially damped sinusoids with linear frequency-drift term. We test against nine equal-mass numerical-relativity simulations selected emission waves $\ensuremath{\gtrsim}25\text{ }\text{...
Measuring the collapse time of a binary neutron star merger remnant can inform physics extreme matter and improve modelling short gamma-ray bursts associated kilonova. The lifetime post-merger directly impacts mechanisms available for jet launch bursts. We develop test method to measure remnants. show that GW170817-like event at $\sim\!40\,$Mpc, network Einstein Telescope with Cosmic Explorer is required detect times $\sim\!10\,$ms. For two-detector A+ design sensitivity, remnants...