A Study of Systematics on the Cosmological Inference of the Hubble Constant from Gravitational Wave Standard Sirens

Gravitational waves (GWs) from compact binary coalescences (CBCs) can constrain the cosmic expansion of the universe. In the absence of an associated electromagnetic counterpart, the spectral sirens method exploits the relation between the detector frame and the source frame masses to jointly infer the parameters of the mass distribution of black holes (BH) and the cosmic expansion parameter $H_0$. This technique relies on the choice of the parametrization for the source mass population of BHs observed in binary black holes merger (BBHs). Using astrophysically motivated BBH populations, we study the possible systematic effects affecting the inferred value for $H_0$ when using heuristic mass models like a broken power law, a power law plus peak and a multi-peak distributions. We find that with 2000 detected GW mergers, the resulting $H_0$ obtained with a spectral sirens analysis can be biased up to $3\sigma$. The main sources of this bias come from the failure of the heuristic mass models used so far to account for a possible redshift evolution of the mass distribution and from their inability to model unexpected mass features. We conclude that future dark siren GW cosmology analyses should make use of source mass models able to account for redshift evolution and capable to adjust to unforeseen mass features.Comment: 21 pages, 14 figure

ICAROGW: A python package for inference of astrophysical population properties of noisy, heterogeneous and incomplete observations

We present icarogw 2.0, a pure CPU/GPU python code developed to infer astrophysical and cosmological population properties of noisy, heterogeneous, and incomplete observations. icarogw 2.0 is mainly developed for compact binary coalescence (CBC) population inference with gravitational wave (GW) observations. The code contains several models for masses, spins, and redshift of CBC distributions, and is able to infer population distributions as well as the cosmological parameters and possible general relativity deviations at cosmological scales. We present the theoretical and computational foundations of icarogw, and we describe how the code can be employed for population and cosmological inference using (i) only GWs, (ii) GWs and galaxy surveys and (iii) GWs with electromagnetic counterparts. Although icarogw 2.0 has been developed for GW science, we also describe how the code can be used for any physical and astrophysical problem involving observations from noisy data in the presence of selection biases. With this paper, we also release tutorials on Zenodo.Comment: 33 pages, code available at (https://github.com/simone-mastrogiovanni/icarogw), tutorials available at (https://zenodo.org/record/7846415#.ZG0l0NJBxQo

Gravitational Wave Cosmology: Be Careful of the Black Hole Mass Spectrum

International audienceGravitational waves (GWs) from compact binary coalescences (CBCs) offer insights into the universe expansion. The spectral siren method, used without electromagnetic counterparts (EMC), infers cosmic expansion (Hubble constant) by relating detector and source frame masses of black hole (BH) mergers. However, heuristic mass models (broken power law, power law plus peak, multipeak) may introduce biases in the Hubble constant estimation, potentially up to 3 sigma with 2000 detected GW mergers. These biases stem from the models inability to consider redshift evolution and unexpected mass features. Future GW cosmology studies should employ adaptable source mass models to address these issues

The spin magnitude of stellar-mass binary black holes evolves with the mass: evidence from gravitational wave data

International audienceThe relation between the mass and spin of stellar-mass binary black holes (BBHs) has been proposed to be a smoking gun for the presence of multiple formation channels for compact objects. First-generation black holes (BHs) formed by isolated binary stellar progenitors are expected to have nearly aligned small spins, while nth-generation BBHs resulting from hierarchical mergers are expected to have misaligned and higher spins. Leveraging data from the third observing run O3 (GWTC-2.1 and GWTC-3), we employ hierarchical Bayesian methods to conduct a comprehensive study of possible correlations between the BBH masses and spins. We use parametric models that either superpose independent BBH populations or explicitly model a mass-spin correlation. We unveil strong evidence for a correlation between normalized spin magnitudes and masses of BBHs. The correlation can be explained as a transition from a BBH population with low spins at low masses and higher spins for higher masses. Although the spin magnitude distribution at high masses lacks robust constraints, we find strong evidence that a transition between two BBH populations with different spin distributions should happen at 40-50 $M_{\odot}$. In particular, we find that the population of BBHs above 40-50 $M_{\odot}$ should compose the $\sim 2 \%$ of the overall population, with a spin magnitude $\chi$ peaking around 0.7, consistently with the fraction of nth-generation BBHs formed by hierarchical mergers in the latest state-of-the-art BBH genesis simulations

A Study of Systematics on the Cosmological Inference of the Hubble Constant from Gravitational Wave Standard Sirens

International audienceGravitational waves (GWs) from compact binary coalescences (CBCs) can constrain the cosmic expansion of the universe. In the absence of an associated electromagnetic counterpart, the spectral sirens method exploits the relation between the detector frame and the source frame masses to jointly infer the parameters of the mass distribution of black holes (BH) and the cosmic expansion parameter $H_0$. This technique relies on the choice of the parametrization for the source mass population of BHs observed in binary black holes merger (BBHs). Using astrophysically motivated BBH populations, we study the possible systematic effects affecting the inferred value for $H_0$ when using heuristic mass models like a broken power law, a power law plus peak and a multi-peak distributions. We find that with 2000 detected GW mergers, the resulting $H_0$ obtained with a spectral sirens analysis can be biased up to $3\sigma$. The main sources of this bias come from the failure of the heuristic mass models used so far to account for a possible redshift evolution of the mass distribution and from their inability to model unexpected mass features. We conclude that future dark siren GW cosmology analyses should make use of source mass models able to account for redshift evolution and capable to adjust to unforeseen mass features

ICAROGW: A python package for inference of astrophysical population properties of noisy, heterogeneous and incomplete observations

International audienceWe present icarogw 2.0, a pure CPU/GPU python code developed to infer astrophysical and cosmological population properties of noisy, heterogeneous, and incomplete observations. icarogw 2.0 is mainly developed for compact binary coalescence (CBC) population inference with gravitational wave (GW) observations. The code contains several models for masses, spins, and redshift of CBC distributions, and is able to infer population distributions as well as the cosmological parameters and possible general relativity deviations at cosmological scales. We present the theoretical and computational foundations of icarogw, and we describe how the code can be employed for population and cosmological inference using (i) only GWs, (ii) GWs and galaxy surveys and (iii) GWs with electromagnetic counterparts. Although icarogw 2.0 has been developed for GW science, we also describe how the code can be used for any physical and astrophysical problem involving observations from noisy data in the presence of selection biases. With this paper, we also release tutorials on Zenodo

ICAROGW: A python package for inference of astrophysical population properties of noisy, heterogeneous and incomplete observations

International audienceWe present icarogw 2.0, a pure CPU/GPU python code developed to infer astrophysical and cosmological population properties of noisy, heterogeneous, and incomplete observations. icarogw 2.0 is mainly developed for compact binary coalescence (CBC) population inference with gravitational wave (GW) observations. The code contains several models for masses, spins, and redshift of CBC distributions, and is able to infer population distributions as well as the cosmological parameters and possible general relativity deviations at cosmological scales. We present the theoretical and computational foundations of icarogw, and we describe how the code can be employed for population and cosmological inference using (i) only GWs, (ii) GWs and galaxy surveys and (iii) GWs with electromagnetic counterparts. Although icarogw 2.0 has been developed for GW science, we also describe how the code can be used for any physical and astrophysical problem involving observations from noisy data in the presence of selection biases. With this paper, we also release tutorials on Zenodo

ICAROGW: A python package for inference of astrophysical population properties of noisy, heterogeneous and incomplete observations

International audienceWe present icarogw 2.0, a pure CPU/GPU python code developed to infer astrophysical and cosmological population properties of noisy, heterogeneous, and incomplete observations. icarogw 2.0 is mainly developed for compact binary coalescence (CBC) population inference with gravitational wave (GW) observations. The code contains several models for masses, spins, and redshift of CBC distributions, and is able to infer population distributions as well as the cosmological parameters and possible general relativity deviations at cosmological scales. We present the theoretical and computational foundations of icarogw, and we describe how the code can be employed for population and cosmological inference using (i) only GWs, (ii) GWs and galaxy surveys and (iii) GWs with electromagnetic counterparts. Although icarogw 2.0 has been developed for GW science, we also describe how the code can be used for any physical and astrophysical problem involving observations from noisy data in the presence of selection biases. With this paper, we also release tutorials on Zenodo