Search Results for author:
Found 7 papers, 4 papers with code
Eryn : A multi-purpose sampler for Bayesian inference
In recent years, methods for Bayesian inference have been widely used in many different problems in physics where detection and characterization are necessary.
MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes.
Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling
In several cases, small improvements can be observed, but the most significant improvement still comes from the addition of a second network that models the residual error.
Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling
Based on this finding, we design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients.
Frequency deviations in universal relations of isolated neutron stars and postmerger remnants
We relate the fundamental quadrupolar fluid mode of isolated non-rotating NSs and the dominant oscillation frequency of neutron star merger remnants.
High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology
Equilibrium sequences of differentially rotating stars with post-merger-like rotational profiles
We present equilibrium sequences of rotating relativistic stars, constructed with a new rotation law that was proposed by Uryu et al. (2017).
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena
Comparing Models of Rapidly Rotating Relativistic Stars Constructed by Two Numerical Methods
We present the first direct comparison of codes based on two different numerical methods for constructing rapidly rotating relativistic stars.
