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The dark sirens method combines gravitational waves and catalogs of galaxies to constrain the cosmological expansion history, merger rates and mass distributions of compact objects, and the laws of gravity. However, the incompleteness of galaxy catalogs means faint potential host galaxies are unobserved, and must be modeled to avoid inducing a bias. The majority of dark sirens analyses to date assume that the missing galaxies are distributed uniformly across the sky, which is clearly unphysical. We introduce a new Bayesian approach to the reconstruction of galaxy catalogs, which makes full use of our knowledge of large-scale structure. Our method quantifies the uncertainties on the estimated true galaxy number count in each voxel, and is marginalized over cosmological parameters and bias parameters. Crucially, our method further assesses the (absolute) magnitude distribution of galaxies, which is not known from the galaxy catalog itself. We present the details of our method and validate our approach on a galaxy catalog associated to the Millennium Simulation. The tools developed here generate physically-informed and robust host galaxy reconstructions, enabling more informative dark sirens analyses. Stage IV galaxy surveys will display greater redshift overlap with GW observations, whilst remaining incomplete -- emphasizing the importance of our work., Comment: 43 pages, 15 figures

We apply neural posterior estimation for fast-and-accurate hierarchical Bayesian inference of gravitational wave populations. We use a normalizing flow to estimate directly the population hyper-parameters from a collection of individual source observations. This approach provides complete freedom in event representation, automatic inclusion of selection effects, and (in contrast to likelihood estimation) without the need for stochastic samplers to obtain posterior samples. Since the number of events may be unknown when the network is trained, we split into sub-population analyses that we later recombine; this allows for fast sequential analyses as additional events are observed. We demonstrate our method on a toy problem of dark siren cosmology, and show that inference takes just a few minutes and scales to $\sim 600$ events before performance degrades. We argue that neural posterior estimation therefore represents a promising avenue for population inference with large numbers of events., Comment: 16 + 5 pages, 7 + 1 figures. Small changes according to the published version

In the absence of numerous gravitational-wave detections with confirmed electromagnetic counterparts, the "dark siren" method has emerged as a leading technique of gravitational-wave cosmology. The method allows redshift information of such events to be inferred statistically from a catalogue of potential host galaxies. Due to selection effects, dark siren analyses necessarily depend on the mass distribution of compact objects and the evolution of their merger rate with redshift. Informative priors on these quantities will impact the inferred posterior constraints on the Hubble constant ($H_0$). It is thus crucial to vary these unknown distributions during an $H_0$ inference. This was not possible in earlier analyses due to the high computational cost, restricting them to either excluding galaxy catalogue information, or fixing the gravitational-wave population mass distribution and risking introducing bias to the $H_0$ measurement. This paper introduces a significantly enhanced version of the Python package GWCOSMO, which allows joint estimation of cosmological and compact binary population parameters. This thereby ensures the analysis is now robust to a major source of potential bias. The gravitational-wave events from the Third Gravitational-Wave Transient Catalogue are reanalysed with the GLADE+ galaxy catalogue, and an updated, more reliable measurement of $H_0=69^{+12}_{-7}$ km s$^{-1}$ Mpc$^{-1}$ is found (maximum a posteriori probability and 68% highest density interval). This improved method will enable cosmological analyses with future gravitational-wave detections to make full use of the information available (both from galaxy catalogues and the compact binary population itself), leading to promising new independent bounds on the Hubble constant., Comment: 31 pages, 11 figures, accepted for publication in JCAP

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 2.0, 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. We discuss the code performance on Graphical Processing Units (GPUs), finding a gain in computation time of about two orders of magnitudes when more than 100 GW events are involved for the analysis. We validate the code by re-analyzing GW population and cosmological studies, finding very good agreement with previous publications., Comment: Code available at (https://github.com/simone-mastrogiovanni/icarogw), tutorials available at (https://zenodo.org/record/7846415#.ZG0l0NJBxQo). A more detailed guide about the code is going to be linked on the github page

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 \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., Comment: 16 pages, 9 figures, Accepted version on PRD

Some binary black hole systems potentially observable in LISA could be in orbit around a supermassive black hole (SMBH). The imprint of relativistic three-body effects on the waveform of the binary can be used to estimate all the parameters of the triple system, in particular the mass of the SMBH. We determine the phase shift in the waveform due to the Doppler effect of the SMBH up to second order in velocity, which breaks a well-known exact degeneracy of the lowest-order Doppler effect between the mass of the SMBH and its inclination. We perform several parameter estimations for LISA signals including this additional dephasing in the wave, showing that one can determine accurately all parameters of the three-body system. Our results indicate that one can measure the mass of a $10^8\,$M$_{\odot}$ SMBH with an accuracy better than $\sim 30\%$ (resp. $\sim 15\%$) by monitoring the waveform of a binary system whose period around the SMBH is less 100 yr (resp. 20 yr).

We outline the ``dark siren'' galaxy catalog method for cosmological inference using gravitational wave (GW) standard sirens, clarifying some common misconceptions in the implementation of this method. When a confident transient electromagnetic counterpart to a GW event is unavailable, the identification of a unique host galaxy is in general challenging. Instead, as originally proposed by Schutz (1986), one can consult a galaxy catalog and implement a dark siren statistical approach incorporating all potential host galaxies within the localization volume. Trott & Hunterer 2021 recently claimed that this approach results in a biased estimate of the Hubble constant, $H_0$, when implemented on mock data, even if optimistic assumptions are made. We demonstrate explicitly that, as previously shown by multiple independent groups, the dark siren statistical method leads to an unbiased posterior when the method is applied to the data correctly. We highlight common sources of error possible to make in the generation of mock data and implementation of the statistical framework, including the mismodeling of selection effects and inconsistent implementations of the Bayesian framework, which can lead to a spurious bias., Comment: 19 pages, 11 figures. Submitted to ApJ

In this proceedings, we are interested in dark gravitational wave standard sirens and their use for cosmology and for constraining modified gravity theories. Due to the extra friction term introduced in their propagation equation those theories predict different luminosity distances for electromagnetic and gravitational waves (GWs). This effect can be parametrized by the two variables $\Xi_0$ and $n$, that can be measured from gravitational wave observations, and specifically from the binary black hole (BBH) mergers detected by LIGO and Virgo. By fitting jointly BBH population models in mass and redshift, the cosmological parameters, and the modified GW luminosity distance to $\sim$ 60 signals observed during the first three LIGO/Virgo observation runs, we conclude that general relativity is consistently the preferred model. The future observation runs O4 and O5 are also considered. Using the same approach, we forecast a measurement uncertainty on the modified gravity parameter $\Xi_0$ of $51\%$ with O4, and $20\%$ with O4 and O5, respectively if GR is the correct theory of gravity. However, we underline that there are strong correlations between astrophysical, cosmological and modified gravity parameters, possibly leading to bias if wrong priors are assumed., Comment: Contribution to the 2022 Gravitation session of the 56th Rencontres de Moriond

Particle emission, in addition to gravitational radiation from cosmic string loops, affects the resulting loop distribution and hence the corresponding observational consequences of cosmic strings. Here we focus on two models in which loops of length $\ell$ are produced from the infinite string network with a given power-law. For both models we find that, due to particle production, the Stochastic Gravitational Wave Background (SGWB) is cut off outside the region of parameter space probed by any current or planned GW experiment. Therefore the present constraints from the LIGO-Virgo-Kagra (LVK) collaboration still hold. However for one of these models, if a fraction $\gtrsim O(10^{-3})$ of these particles cascades into $\gamma$-rays, and if the gravitational backreaction scale follows the Polchinski-Rocha model, then the string tension is tightly constrained from below by measurements of the Diffuse $\gamma$-Ray Background, and from above by the SGWB. With reasonable assumptions, the joint constraint on the string tension set by these two possible observables reduces the available parameter space of this cosmic string model to a narrow band. Future upgrades to LVK will either rule out this model or detect strings., Comment: 19 pages, 6 figures, matches published version