Related papers: Simulation-based Inference of Massive Black Hole B…
This paper considers the problem of searching for quiet, long-duration and broadband gravitational wave signals, such as stellar-mass binary black hole binaries, in mock LISA data. We propose a method that combines a semi-coherent…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
We investigate the coalescence of massive black hole ($M_{\rm BH}\gtrsim 10^{6}~\rm M_{\odot}$) binaries (MBHBs) at $6<z<10$ by adopting a suite of cosmological hydrodynamical simulations of galaxy formation, zoomed-in on biased ($ >3…
Simulations predict that supermassive black hole binaries (SMBHBs) will exhibit periodic brightness variations that may exceed the stochastic variability intrinsic to active galactic nuclei (AGN). In this paper, we simulate SMBHBs with…
We present a Bayesian parameter-estimation pipeline to measure the properties of inspiralling stellar-mass black hole binaries with LISA. Our strategy (i) is based on the coherent analysis of the three noise-orthogonal LISA data streams,…
We present a novel formation channel for supermassive black hole (SMBH) binaries in the early Universe, driven by primordial black holes (PBHs). Using high-resolution hydrodynamical simulations, we explore the role of massive PBHs ($m_{BH}…
Black hole X-ray binaries (BHBs) offer insights into extreme gravitational environments and the testing of general relativity. The X-ray spectrum collected by NICER offers valuable information on the properties and behaviour of BHBs through…
In this paper, we present a method for computing the marginal likelihood, also known as the model likelihood or Bayesian evidence, from Markov Chain Monte Carlo (MCMC), or other sampled posterior distributions. In order to do this, one…
Dynamically assembled binary black holes are expected to retain measurable orbital eccentricity in the LIGO-Virgo-KAGRA band, but most parameter estimation analyses still assume quasi-circular inspirals. This raises a critical question: how…
Supermassive binary black holes (SMBBHs) are the anticipated byproducts of galaxy mergers and play a pivotal role in shaping galaxy evolution, gravitational wave emissions, and accretion physics. Despite their theoretical prevalence, direct…
Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle predict IMBH host candidates based on observable features. This…
Astrophysical black holes could be nearly extremal (that is, rotating nearly as fast as possible); therefore, nearly extremal black holes could be among the binaries that current and future gravitational-wave observatories will detect.…
Motivated by observational searches for sub-parsec supermassive black hole binaries (SBHBs) we develop a modular analytic model to determine the likelihood for detection of SBHBs by ongoing spectroscopic surveys. The model combines the…
There may exist stellar-mass binary black holes (BBH) which merge while orbiting nearby a supermassive black hole (SMBH). In such a triple system, the SMBH will modulate the gravitational waveform of the BBH through orbital Doppler shift…
We present a highly-scalable framework that targets problems of interest to the numerical relativity and broader astrophysics communities. This framework combines a parallel octree-refined adaptive mesh with a wavelet adaptive…
In this paper, we develop a Neural Likelihood Estimator and apply it to analyse real gravitational-wave (GW) data for the first time. We assess the usability of neural likelihood for GW parameter estimation and report the parameter space…
Binary black hole simulations have traditionally been computationally very expensive: current simulations are performed in supercomputers involving dozens if not hundreds of processors, thus systematic studies of the parameter space of…
Gravitational-wave approximants are essential for gravitational-wave astronomy, allowing the coverage binary black hole parameter space for inference or match filtering without costly numerical relativity (NR) simulations, but generally…
The search for subsolar mass primordial black holes (PBHs) poses a challenging problem due to the low signal-to-noise ratio, extended signal duration, and computational cost demands, compared to solar mass binary black hole events. In this…
We present an algorithm based on artificial neural networks (ANNs), that estimates the mass ratio in a binary black hole collision out of given Gravitational Wave (GW) strains. In this analysis, the ANN is trained with a sample of GW…