Related papers: Statistically-informed deep learning for gravitati…
Gravitational wave observations of binary black hole mergers probe their astrophysical origins via the binary spin, namely the spin magnitudes and directions of each component black hole, together described by six degrees of freedom.…
With the anticipated launch of space-based gravitational wave detectors, including LISA, TaiJi, TianQin, and DECIGO, expected around 2030, the detection of gravitational waves generated by intermediate-mass black hole binaries (IMBBHs)…
Motivated by the possibility of observing gravitational waves from merging black holes whose spins are nearly extremal (i.e., 1 in dimensionless units), we present numerical waveforms from simulations of merging black holes with the highest…
In the 2030s, a new era of gravitational-wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji and TianQin, open the millihertz window for GW astronomy. These detectors…
The simultaneous observation of gravitational waves (GW) and prompt electromagnetic counterparts from the merger of two neutron stars can help reveal the properties of extreme matter and gravity during and immediately after the final…
We analyze a new numerical relativity dataset of spinning but nonprecessing binary black holes on eccentric orbits, with eccentricities from approximately $0.1$ to $0.5$, with dimensionless spins up to $0.75$ included at mass ratios…
Inferring the properties of colliding black holes from gravitational-wave observations is subject to systematic errors arising from modelling uncertainties. Although the accuracy of each model can be calculated through comparison to…
We present a novel machine learning framework tailored to detect massive black hole binaries observed by spaceborne gravitational wave detectors like the Laser Interferometer Space Antenna (LISA) and predict their future merger times. The…
The inference of source parameters from gravitational-wave signals relies on theoretical models that describe the emitted waveform. Different model assumptions on which the computation of these models is based could lead to biases in the…
We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When…
We present a full sampling of the hierarchical population posterior distribution of merging black holes using current gravitational-wave data. We directly tackle the most relevant intrinsic parameter space made of the binary parameters…
Astrophysically motivated population models for binary black hole observables are often insufficient to capture the imprints of multiple formation channels. This is mainly due to the strongly parametrized nature of such investigations.…
Building on the analytical description of the post-merger (ringdown) waveform of coalescing, non-precessing, spinning, (BBHs) introduced in Phys. Rev. D 90, 024054 (2014), we propose an analytic, closed form, time-domain, representation of…
We present a convolutional neural network, designed in the auto-encoder configuration that can detect and denoise astrophysical gravitational waves from merging black hole binaries, orders of magnitude faster than the conventional…
We conduct a descriptive analysis of the multipolar structure of gravitational-radiation waveforms from equal-mass aligned-spin mergers, following an approach first presented in the complementary context of nonspinning black holes of…
Gravitational Waves (GWs) from coalescing binaries carry crucial information about their component sources, like mass, spin and tidal effects. This implies that the analysis of GW signals from binary neutron star mergers can offer unique…
We derive an analytical form of the joint prior of effective spin parameters, $\chi_\mathrm{eff}$ and $\chi_\mathrm{p}$, assuming an isotropic and uniform-in-magnitude spin distribution. This is a vital factor in performing hierarchical…
Once a gravitational wave signal is detected, the measurement of its source parameters is important to achieve various scientific goals. This is done through Bayesian inference, where the analysis cost increases with the model complexity…
Understanding the characteristics of the remnant black hole formed in a binary black hole merger is crucial for conducting gravitational wave astronomy. Typically, models of remnant black holes provide information about their mass, spin,…
The LIGO-Virgo-KAGRA catalog has been analyzed with an abundance of different population models due to theoretical uncertainty in the formation of gravitational-wave sources. To expedite model exploration, we introduce an efficient and…