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

Joint population and cosmological properties inference with gravitational waves standard sirens and galaxy surveys

Gravitational wave (GW) sources at cosmological distances can be used to probe the expansion rate of the Universe. GWs directly provide a distance estimation of the source but no direct information on its redshift. The optimal scenario to obtain a redshift is through the direct identification of an electromagnetic (EM) counterpart and its host galaxy. With almost 100 GW sources detected without EM counterparts (dark sirens), it is becoming crucial to have statistical techniques able to perform cosmological studies in the absence of EM emission. Currently, only two techniques for dark sirens are used on GW observations; the spectral siren method, which is based on the source-frame mass distribution to estimate conjointly cosmology and the source’s merger rate, and the galaxy survey method, which uses galaxy surveys to assign a probabilistic redshift to the source while fitting cosmology. It has been recognized, however, that these two methods are two sides of the same coin. In this paper, we present a novel approach to unify these two methods. We apply this approach to several observed GW events using the glade+ galaxy catalog discussing limiting cases. We provide estimates of the Hubble constant, modified gravity propagation effects, and population properties for binary black holes. We also estimate the binary black hole merger rate per galaxy to be 10−6–10−5  yr−1 depending on the galaxy catalog hypotheses

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

A novel approach to infer population and cosmological properties with gravitational waves standard sirens and galaxy surveys

International audienceGravitational wave (GW) sources at cosmological distances can be used to probe the expansion rate of the Universe. GWs directly provide a distance estimation of the source but no direct information on its redshift. The optimal scenario to obtain a redshift is through the direct identification of an electromagnetic (EM) counterpart and its host galaxy. With almost 100 GW sources detected without EM counterparts (dark sirens), it is becoming crucial to have statistical techniques able to perform cosmological studies in the absence of EM emission. Currently, only two techniques for dark sirens are used on GW observations: the spectral siren method, which is based on the source-frame mass distribution to estimate conjointly cosmology and the source's merger rate, and the galaxy survey method, which uses galaxy surveys to assign a probabilistic redshift to the source while fitting cosmology. It has been recognized, however, that these two methods are two sides of the same coin. In this paper, we present a novel approach to unify these two methods. We apply this approach to several observed GW events using the \textsc{glade+} galaxy catalog discussing limiting cases. We provide estimates of the Hubble constant, modified gravity propagation effects, and population properties for binary black holes. We also estimate the binary black hole merger rate per galaxy to be $10^{-6}-10^{-5} {\rm yr^{-1}}$ depending on the galaxy catalog hypotheses

A novel approach to infer population and cosmological properties with gravitational waves standard sirens and galaxy surveys

International audienceGravitational wave (GW) sources at cosmological distances can be used to probe the expansion rate of the Universe. GWs directly provide a distance estimation of the source but no direct information on its redshift. The optimal scenario to obtain a redshift is through the direct identification of an electromagnetic (EM) counterpart and its host galaxy. With almost 100 GW sources detected without EM counterparts (dark sirens), it is becoming crucial to have statistical techniques able to perform cosmological studies in the absence of EM emission. Currently, only two techniques for dark sirens are used on GW observations: the spectral siren method, which is based on the source-frame mass distribution to estimate conjointly cosmology and the source's merger rate, and the galaxy survey method, which uses galaxy surveys to assign a probabilistic redshift to the source while fitting cosmology. It has been recognized, however, that these two methods are two sides of the same coin. In this paper, we present a novel approach to unify these two methods. We apply this approach to several observed GW events using the \textsc{glade+} galaxy catalog discussing limiting cases. We provide estimates of the Hubble constant, modified gravity propagation effects, and population properties for binary black holes. We also estimate the binary black hole merger rate per galaxy to be $10^{-6}-10^{-5} {\rm yr^{-1}}$ depending on the galaxy catalog hypotheses

A novel approach to infer population and cosmological properties with gravitational waves standard sirens and galaxy surveys

International audienceGravitational wave (GW) sources at cosmological distances can be used to probe the expansion rate of the Universe. GWs directly provide a distance estimation of the source but no direct information on its redshift. The optimal scenario to obtain a redshift is through the direct identification of an electromagnetic (EM) counterpart and its host galaxy. With almost 100 GW sources detected without EM counterparts (dark sirens), it is becoming crucial to have statistical techniques able to perform cosmological studies in the absence of EM emission. Currently, only two techniques for dark sirens are used on GW observations: the spectral siren method, which is based on the source-frame mass distribution to estimate conjointly cosmology and the source's merger rate, and the galaxy survey method, which uses galaxy surveys to assign a probabilistic redshift to the source while fitting cosmology. It has been recognized, however, that these two methods are two sides of the same coin. In this paper, we present a novel approach to unify these two methods. We apply this approach to several observed GW events using the \textsc{glade+} galaxy catalog discussing limiting cases. We provide estimates of the Hubble constant, modified gravity propagation effects, and population properties for binary black holes. We also estimate the binary black hole merger rate per galaxy to be $10^{-6}-10^{-5} {\rm yr^{-1}}$ depending on the galaxy catalog hypotheses