Searching for structure in the binary black hole spin distribution

The spins of black holes in merging binaries can reveal information related to the formation and evolution of these systems through their gravitational wave emission. Combining events to infer the astrophysical distribution of black hole spins allows us to determine the relative contribution from different formation scenarios to the population. Many previous works have modeled spin population distributions using parametric models. While these are valuable approaches when the observed population is small, they make strong assumptions about the shape of the underlying distribution and are highly susceptible to biases due to mismodeling. The results obtained with such parametric models are only valid if the allowed shape of the distribution is well-motivated (i.e. for astrophysical reasons). In this work, we relax these prior assumptions and model the spin distributions using a more data-driven approach, modeling these distributions with flexible cubic spline interpolants in order to allow for capturing structures that the parametric models cannot. We find that adding this flexibility to the model substantially increases the uncertainty in the inferred distributions, but find a general trend for lower support at high spin magnitude and a spin tilt distribution consistent with isotropic orientations. We infer that 62 - 87% of black holes have spin magnitudes less than a = 0.5, and 27- 50% of black holes exhibit negative $\chi_{\rm eff}$. Using the inferred $\chi_{\rm eff}$ distribution, we place a conservative upper limit of 37% for the contribution of hierarchical mergers to the astrophysical BBH population. Additionally, we find that artifacts from unconverged Monte Carlo integrals in the likelihood can manifest as spurious peaks and structures in inferred distributions, mandating the use of a sufficient number of samples when using Monte Carlo integration for population inference.Comment: 15 pages, 14 figure

Growing Pains: Understanding the Impact of Likelihood Uncertainty on Hierarchical Bayesian Inference for Gravitational-Wave Astronomy

Observations of gravitational waves emitted by merging compact binaries have provided tantalising hints about stellar astrophysics, cosmology, and fundamental physics. However, the physical parameters describing the systems, (mass, spin, distance) used to extract these inferences about the Universe are subject to large uncertainties. The current method of performing these analyses requires performing many Monte Carlo integrals to marginalise over the uncertainty in the properties of the individual binaries and the survey selection bias. These Monte Carlo integrals are subject to fundamental statistical uncertainties. Previous treatments of this statistical uncertainty has focused on ensuring the precision of the inferred inference is unaffected, however, these works have neglected the question of whether sufficient accuracy can also be achieved. In this work, we provide a practical exploration of the impact of uncertainty in our analyses and provide a suggested framework for verifying that astrophysical inferences made with the gravitational-wave transient catalogue are accurate. Applying our framework to models used by the LIGO-Virgo-Kagra collaboration, we find that Monte Carlo uncertainty in estimating the survey selection bias is the limiting factor in our ability to probe narrow populations model and this will rapidly grow more problematic as the size of the observed population increases.Comment: 8 pages, 6 figure

Kierkegaard in Zion

Kierkegaard in Zio

The curious case of GW200129: interplay between spin-precession inference and data-quality issues

Measurement of spin-precession in black hole binary mergers observed with gravitational waves is an exciting milestone as it relates to both general relativistic dynamics and astrophysical binary formation scenarios. In this study, we revisit the evidence for spin-precession in GW200129 and localize its origin to data in LIGO Livingston in the 20--50\,Hz frequency range where the signal amplitude is lower than expected from a non-precessing binary given all the other data. These data are subject to known data quality issues as a glitch was subtracted from the detector's strain data. The lack of evidence for spin-precession in LIGO Hanford leads to a noticeable inconsistency between the inferred binary mass ratio and precessing spin in the two LIGO detectors, something not expected from solely different Gaussian noise realizations. We revisit the LIGO Livingston glitch mitigation and show that the difference between a spin-precessing and a non-precessing interpretation for GW200129 is smaller than the statistical and systematic uncertainty of the glitch subtraction, finding that the support for spin-precession depends sensitively on the glitch modeling. We also investigate the signal-to-noise ratio $\sim7$ trigger in the less sensitive Virgo detector. Though not influencing the spin-precession studies, the Virgo trigger is grossly inconsistent with the ones in LIGO Hanford and LIGO Livingston as it points to a much heavier system. We interpret the Virgo data in the context of further data quality issues. While our results do not disprove the presence of spin-precession in GW200129, we argue that any such inference is contingent upon the statistical and systematic uncertainty of the glitch mitigation. Our study highlights the role of data quality investigations when inferring subtle effects such as spin-precession for short signals such as the ones produced by high-mass systems.Comment: 17 pages, 14 figures, 2 tables. Data release: https://zenodo.org/record/725965

Two novel/ancient myosins in mammalian skeletal muscles: MYH14/7b and MYH15 are expressed in extraocular muscles and muscle spindles

The mammalian genome contains three ancient sarcomeric myosin heavy chain (MYH) genes, MYH14/7b, MYH15 and MYH16, in addition to the two well characterized clusters of skeletal and cardiac MYHs. MYH16 is expressed in jaw muscles of carnivores; however the expression pattern of MYH14 and MYH15 is not known. MYH14 and MYH15 orthologues are present in frogs and birds, coding for chicken slow myosin 2 and ventricular MYH, respectively, whereas only MYH14 orthologues have been detected in fish. In all species the MYH14 gene contains a microRNA, miR-499. Here we report that in rat and mouse, MYH14 and miR-499 transcripts are detected in heart, slow muscles and extraocular (EO) muscles, whereas MYH15 transcripts are detected exclusively in EO muscles. However, MYH14 protein is detected only in a minor fibre population in EO muscles, corresponding to slow-tonic fibres, and in bag fibres of muscle spindles. MYH15 protein is present in most fibres of the orbital layer of EO muscles and in the extracapsular region of bag fibres. During development, MYH14 is expressed at low levels in skeletal muscles, heart and all EO muscle fibres but disappears from most fibres, except the slow-tonic fibres, after birth. In contrast, MYH15 is absent in embryonic and fetal muscles and is first detected after birth in the orbital layer of EO muscles. The identification of the expression pattern of MYH14 and MYH15 brings to completion the inventory of the MYH isoforms involved in sarcomeric architecture of skeletal muscles and provides an unambiguous molecular basis to study the contractile properties of slow-tonic fibres in mammals