Confusing Head-On Collisions with Precessing Intermediate-Mass Binary Black Hole Mergers
Abstract
We report a degeneracy between the gravitational-wave signals from quasicircular precessing black-hole mergers and those from extremely eccentric mergers, namely, head-on collisions. Performing model selection on numerically simulated signals of head-on collisions using models for quasicircular binaries, we find that, for signal-to-noise ratios of 15 and 25, typical of Advanced LIGO observations, head-on mergers with respective total masses of M∈(125,300)M_{⊙} and M∈(200,440)M_{⊙} would be identified as precessing quasicircular intermediate-mass black-hole binaries located at a much larger distance. Ruling out the head-on scenario would require us to perform model selection using currently nonexistent waveform models for head-on collisions, together with the application of astrophysically motivated priors on the (rare) occurrence of those events. We show that in situations where standard parameter inference of compact binaries may report component masses inside (outside) the pair-instability supernova gap, the true object may be a head-on merger with masses outside (inside) this gap. We briefly discuss the potential implications of these findings for GW190521, which we analyze in detail in J. Calderón Bustillo et al., Phys. Rev. Lett. 126, 081101 (2021)PRLTAO0031-900710.1103/PhysRevLett.126.081101.
Read the paper
Related studies
- A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
- Toxicity of nanomaterials
- The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies
- Halogen bonding (X‐bonding): A biological perspective
- Biofilm formation at the air–liquid interface by the Pseudomonas fluorescens SBW25 wrinkly spreader requires an acetylated form of cellulose
- Opioid Activation of Toll-Like Receptor 4 Contributes to Drug Reinforcement
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.