Related papers: Simulation-based Inference of Massive Black Hole B…
We review recent progress in numerical relativity simulations of black-hole (BH) spacetimes. Following a brief summary of the methods employed in the modeling, we summarize the key results in three major areas of BH physics: (i) BHs as…
We test the potential of Bayesian synthesis of upcoming multi-instrument data to extract orbital parameters and individual light curves of close binary supermassive black holes (CB-SMBH) with subparsec separations. Next generation (ng)…
Searching for gravitational waves in pulsar timing array data is computationally intensive. The data is unevenly sampled, and the noise is heteroscedastic, necessitating the use of a time-domain likelihood function with attendant expensive…
The observation of massive black hole binaries (MBHBs) with Pulsar Timing Arrays (PTAs) is one of the goals of gravitational wave astronomy in the coming years. Massive (>10^8 solar masses) and low-redshift (< 1.5) sources are expected to…
The sub/millimeter wavelengths (86-690 GHz) very long baseline interferometry (VLBI) will provide $\sim5-40\ \mu$as angular resolution, $\sim10$ mJy baseline sensitivity, and $\sim 1\ \mu$as/yr proper motion precision, which can directly…
Binaries of supermassive black holes (MBHBs) represent the primary sources of the gravitational wave background (GWB) detectable by Pulsar Timing Arrays (PTAs). The eccentricity with which binaries form in galactic mergers is the key…
Detecting binary black holes in interferometer data requires an accurate knowledge of the orbital phase evolution of the system. From the point of view of data analysis one also needs fast algorithms to compute the templates that will…
The early inspiral from stellar-mass binary black holes (sBBHs) can emit milli-Hertz gravitational wave signals, making them detectable sources for space-borne gravitational wave missions like TianQin. However, the traditional matched…
Population synthesis simulations of compact binary coalescences~(CBCs) play a crucial role in extracting astrophysical insights from an ensemble of gravitational wave~(GW) observations. However, realistic simulations can be costly to…
We introduce deep learning models to estimate the masses of the binary components of black hole mergers, $(m_1,m_2)$, and three astrophysical properties of the post-merger compact remnant, namely, the final spin, $a_f$, and the frequency…
NANOGrav, EPTA, PPTA, and CPTA have announced the evidence for a stochastic signal from their latest data sets. Supermassive black hole binaries (SMBHBs) are supposed to be the most promising gravitational-wave (GW) sources of pulsar timing…
Gravitational-wave (GW) observations of binary black-hole (BBH) coalescences are expected to address outstanding questions in astrophysics, cosmology, and fundamental physics. Realizing the full discovery potential of upcoming…
Deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is challenging. But the success of these approaches depends on…
In machine learning and statistics, probabilistic inference involving multimodal distributions is quite difficult. This is especially true in high dimensional problems, where most existing algorithms cannot easily move from one mode to…
Studying how the black hole (BH) - (galaxy) bulge mass relation evolves with redshift provides valuable insights into the co-evolution of supermassive black holes and their host galaxies. However, obtaining accurate measurement of BH masses…
Deep architecture such as hierarchical semi-Markov models is an important class of models for nested sequential data. Current exact inference schemes either cost cubic time in sequence length, or exponential time in model depth. These costs…
Next-generation gravitational wave detectors such as the Einstein Telescope and Cosmic Explorer will have increased sensitivity and observing volumes, enabling unprecedented precision in parameter estimation. However, this enhanced…
We present a semi-analytical binary black hole (BBH) metric approximation that models the entire evolution of the system from inspiral to merger. The metric is constructed as a boosted Kerr-Schild superposition following post-Newtonian (PN)…
We introduce a technique for gravitational-wave analysis, where Gaussian process regression is used to emulate the strain spectrum of a stochastic background using population-synthesis simulations. This leads to direct Bayesian inference on…
Hierarchical structure formation inevitably leads to the formation of supermassive binary black holes (BBHs) with a sub-parsec separation in galactic nuclei. However, to date there has been no unambiguous detection of such systems. In an…