arXiv subjects

Masaki Iwaya

Publications and source records attributed to Masaki Iwaya.

5 recordsLinked to original sources

arXiv2026

Constraint on the progenitor of binary black hole merger using Population III star formation channel

The observations of gravitational waves have revealed the existence of black holes above $30 M_\odot$. A variety of channels have been proposed as their origin, including the Population III (Pop. III) star channel. In this channel, Binary Black Holes (BBHs) containing such massive BHs are naturally produced. In this paper, we examine the relative fractions of five formation channels that may contribute to the origins of BBHs: isolated binaries of either Population I or Population II stars, PopIII isolated binaries, chemically homogeneous evolution, and the dynamical evolution in globular clusters and nuclear star clusters, using the LIGO-Virgo-KAGRA gravitational-wave transient catalog (GWTC-3) events through hierarchical Bayesian inference. We find that the branching fraction of the Pop. III BBH channel is $0.11^{+0.08}_{-0.06}$ within our framework, consistent with the local merger rate density of the model of the Pop. III BBH channel we adopt. We also evaluate the contributions to the catalogue using the selection effect of each formation channel and find that Pop. III BBH could contribute at a non-negligible rate, though the adequacy of these ratios should be subject to ongoing discussion.

astro-ph.HE↗

arXiv2026

Simulation-based Inference for Gravitational Waves from Binary Neutron Stars: Application of Summary Data from Heterodyning

Gravitational-wave parameter estimation for binary neutron star (BNS) systems poses severe computational challenges due to the extended signal duration, which can reach several minutes in current detectors. Neural posterior estimation (NPE), a simulation-based inference approach, offers dramatic speedups but requires effective dimensionality reduction of the high-dimensional input data. We present a novel compression strategy based on likelihood-oriented summary statistics derived from the relative binning formalism of Zackay et al. (2018), which compresses raw frequency-domain data into the summary data. The summary data is based on a polynomial approximation of the waveform ratio using frequency banding grounded in post-Newtonian approximation, and directly evaluated with only $O(1000)$ sample points of the waveform. As a result, both the training and storage cost become more efficient than previously reported networks for BNS inference. We train a set of NPE networks on these summary statistics and validate a network against traditional nested sampling over 1024 BNS injections. The network produces well-calibrated posteriors across all source parameters we consider, with Jensen-Shannon divergences (JSD) consistent with numerical noise for most parameters. Although we find that the median JSD for the most inconsistent parameter exceeds $10^{-2}$ bits with current configurations, our results show potential for rapid parameter estimation of the BNS signal.

gr-qc↗

arXiv2026

Simulation-based Inference towards Gravitational-wave waveform systematics in Intermediate-Mass Binary Black Holes

Parameter estimation for gravitational-wave signals is computationally demanding due to the high dimensionality of the parameter space and the cost of repeated waveform generation in traditional Bayesian inference. These analyses require on the order of 10^8 likelihood evaluations and waveform generations, resulting in inference times of hours to days per event. Furthermore, discrepancies between waveform models introduce systematic uncertainties that can bias inferred source properties. To address these challenges, we propose a novel framework based on Simulation-based Inference (SBI) and Neural Posterior Estimation (NPE) and apply it to signals from Intermediate-Mass Black Holes (IMBH). In this framework, we train a single amortised neural posterior estimator on a large simulated dataset generated using two state-of-the-art waveform approximants, IMRPhenomXPHM and SEOBNRv5PHM. By treating the waveform model index as a latent variable, the network learns to produce posterior distributions that are naturally marginalized over the discrepancies of the two waveform models. Once trained, the model enables direct posterior sampling in milliseconds per event, eliminating the need for likelihood evaluations while simultaneously accounting for model systematics. We demonstrate that this approach recovers accurate posterior distributions for IMBH signals injected into Gaussian noise, achieving close agreement with traditional nested-sampling results while reducing inference time by several orders of magnitude. Our results show that NPE can robustly incorporate waveform-model systematics within a unified framework, offering a scalable path toward rapid, systematics-aware gravitational-wave inference. Establishing these methods as promising alternatives to classical likelihood-based pipelines for current and future high-mass gravitational-wave observations.

gr-qc↗

arXiv2026

Impact of Spin Priors on the Population Inference of Merging Binary Black Holes

The spins of merging binary black holes (BBHs) inferred from gravitational-wave (GW) observations provide key insights into their formation channels. However, spin parameters are typically weakly constrained from data, and their inferred values are often strongly influenced by the assumed prior in Bayesian analyses. A commonly used prior, uniform in spin magnitudes and isotropic in spin directions, assigns vanishing probability density to spin-orbit-aligned configurations, potentially biasing inferences for BBH parameters. The prior choice can also affect population-level analyses by degrading the convergence of Monte Carlo integrations used to evaluate the likelihood in hierarchical Bayesian inference. In this work, we propose a novel spin prior that is uniform in the effective spin parameters Xeff and Xp, two spin combinations that can be relatively well measured from GW data, conditioned on the mass ratio. Using simulated BBH populations, we show that the inferred spin population can depend on the choice of prior, and that the proposed prior more accurately recovers the underlying spin population, particularly when the true distribution favors aligned-spin configurations. Because mass and spin measurements are correlated, our prior also enables a more accurate recovery of the underlying mass distribution.

gr-qc↗

arXiv2024

An analytical joint prior for effective spins for inference on the spin distribution of binary black holes

We derive an analytical form of the joint prior of effective spin parameters, $χ_\mathrm{eff}$ and $χ_\mathrm{p}$, assuming an isotropic and uniform-in-magnitude spin distribution. This is a vital factor in performing hierarchical Bayesian inference for studying the population properties of merging compact binaries observed with gravitational waves. In previous analyses, this was evaluated numerically using kernel density estimation (KDE). However, we find that this numerical approach is inaccurate in certain parameter regions, where both $|χ_\mathrm{eff}|$ and $χ_\mathrm{p}$ are small. Our analytical approach provides accurate computations of the joint prior across the entire parameter space and enables more reliable population inference. Employing our analytic prior, we reanalyze binary black holes in the Gravitational-Wave Transient Catalog 3 (GWTC-3) by the LIGO-Virgo-KAGRA collaboration. While the results are largely unchanged, log-likelihood errors due to the use of the inaccurate prior evaluations are $\mathcal{O}(1)$. Since these errors accumulate with the increasing number of events, our analytical prior will be crucial in the future analyses.

gr-qc↗