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Federico Stachurski

Publications and source records attributed to Federico Stachurski.

3 recordsLinked to original sources

Blinded Mock Data Challenge for Gravitational-Wave Cosmology-I: Assessing the Robustness of Methods Using Binary Black Holes Mass Spectrum

Gravitational Wave (GW) sources are standard sirens that provide an independent way to map the cosmic expansion history by combining with an independent redshift measurement either from an electromagnetic counterpart for a bright siren or using different statistical techniques for dark sirens. In this analysis, we perform the first Blinded Mock Data Challenge (\texttt{Blinded-MDC}) to test the robustness in inferring the value of Hubble constant $H_0$ for a dark siren technique which depends on astrophysical mass distribution of Binary Black Holes (BBHs). We have considered different analysis setups for the \texttt{Blinded-MDC} to test both statistical and systematic uncertainties and demonstrate the capabilities in inferring $H_0$ with detector sensitivity as per the fourth observation run of LIGO-Virgo-KAGRA. We find that when the astrophysical population of BBHs matches with the underlying assumption of the model, a cosmological pipeline can recover the injected parameters using the observed mass distribution. However, when the mock mass distribution of the astrophysical population depends slightly on redshift and one is ignorant about it in analyzing the data, it can cause a systematic discrepancy in the inferred value of $H_0$ by about $1.5σ$, above the statistical fluctuations due to noise and a limited number of events. In the future, elaborate studies will be required to mitigate systematic uncertainties due to unknown astrophysical complexities. This MDC framework sets the road map for inspecting the precision and accuracy of standard siren cosmology and provides the first insight into the robustness of the population-dependent cosmology inference in a blinded analysis setup.

astro-ph.CO

Cosmological Inference using Gravitational Waves and Normalising Flows

We present a machine learning approach using normalising flows for inferring cosmological parameters from gravitational wave events. Our methodology is general to any type of compact binary coalescence event and cosmological model and relies on the generation of training data representing distributions of gravitational wave event parameters. These parameters are conditional on the underlying cosmology and incorporate prior information from galaxy catalogues. We provide an example analysis inferring the Hubble constant using binary black holes detected during the O1, O2, and O3 observational runs conducted by the advanced LIGO/VIRGO gravitational wave detectors. We obtain a Bayesian posterior on the Hubble constant from which we derive an estimate and 1$σ$ confidence bounds of $H_{0} = 74.51^{+14.80}_{-13.63} \: \text{km} \:\text{s}^{-1} \text{Mpc}^{-1}$. We are able to compute this result in $\mathcal{O}(1)$ s using our trained Normalising Flow model.

gr-qc

GWSim: Python package for creating mock GW samples for different astrophysical populations and cosmological models of binary black holes

Precision cosmology with gravitational wave (GW) sources requires a deeper understanding of the interplay between GW source population and cosmological parameters governing the dynamics of the Universe. With the swift increase in GW detections, it is necessary to develop a tool for exploring many aspects of cosmology and fundamental physics; this tools allows to simulate GW mock samples for several populations and cosmological models. We have developed a new code called GWSim, which allows us to make a large sample of GW mock events from a broad range of configurations, while varying the cosmology, the merger rate, and the GW source parameters (i.e. mass and spin distributions in particular) for a given network of GW detectors. A large sample of simulated mock GW events will be useful for improving our understanding of the statistical properties of the distribution of GW sources, as long as it is detectable for a given detector noise and an astrophysical and cosmological model. It will also be useful to compare simulated samples with the observed distribution of the GW sources from data and infer the underlying population of the GW source parameters and cosmology. We restricted the cosmology to spatially flat universes, including models with varying dark energy equation of state. The GWSim code provides each mock event with a position in the sky and a redshift; these values can be those of random host galaxies coming from an isotropic and homogeneous simulated Universe or a user-supplied galaxy catalog. We used realistic detector configurations of the LIGO and Virgo network of detectors to demonstrate the performance of this code for the latest observation runs and the upcoming observation run.

astro-ph.CO