2025/08/21 by A. K. Mehta, Digvijay Wadekar, Mehta, Ajit Kumar +15 · 2 citations
Physics and Astronomy · #FOS: Physical sciences #Gamma-ray bursts and supernovae #General Relativity and Quantum Cosmology (gr-qc) #High Energy Astrophysical Phenomena (astro-ph.HE) #Particle physics theoretical and experimental studies #Pulsars and Gravitational Waves Research
paper · pdf · doi:10.48550/arxiv.2508.15350
openalex publication_date 2025/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
We present the population properties of binary black hole mergers identified by the \ttIAS-HM pipeline (which incorporates higher-order modes in the search templates) during the third observing run (O3) of the LIGO, Virgo, and KAGRA (LVK) detectors. In our population inference analysis, instead of only using events above a sharp cut based on a particular detection threshold (e.g., false alarm rate), we use a Bayesian framework to consistently include both marginal and confident events. We find that our inference based solely on highly significant events (pastro ∼ 1) is broadly consistent with the GWTC-3 population analysis performed by the LVK collaboration. However, incorporating marginal events into the analysis leads to a preference for stronger redshift evolution in the merger rate and an increased density of asymmetric mass-ratio mergers relative to the GWTC-3 analysis, while remaining within its allowed parameter ranges. Using simple parametric models to describe the binary black hole population, we estimate a merger rate density of 32.4+18.5-12.2 Gpc-3 yr-1 at redshift z = 0.2, and a redshift evolution parameter of κ= 4.4+1.9-2.0. Assuming a power-law form for the mass ratio distribution (∝ qβ), we infer β= 0.1+1.9-1.4, indicating a relatively flat distribution. These results highlight the potential impact of marginal events on population inferences and motivate future analyses with data from upcoming observing runs.