arXiv subjects

Ioannis Michaloliakos

Publications and source records attributed to Ioannis Michaloliakos.

3 recordsLinked to original sources

arXiv2023

Measurement of the Cross-Correlation Angular Power Spectrum Between the Stochastic Gravitational Wave Background and Galaxy Over-Density

We study the cross-correlation between the stochastic gravitational-wave background (SGWB) generated by binary black hole (BBH) mergers across the universe and the distribution of galaxies across the sky. We use the anisotropic SGWB measurement obtained using data from the third observing run (O3) of Advanced LIGO detectors and galaxy over-density obtained from the Sloan Digital Sky Survey (SDSS) spectroscopic catalog. We compute, for the first time, the angular power spectrum of their cross-correlation. Instead of integrating the SGWB across frequencies, we analyze the cross-correlation in 10 Hz wide SGWB frequency bands to study the frequency dependence of the cross-correlation angular power spectrum. Finally, we compare the observed cross-correlation to the spectra predicted by astrophysical models. We apply a Bayesian formalism to explore the parameter space of the theoretical models, and we set constraints on a set of (effective) astrophysical parameters describing the galactic process of gravitational wave (GW) emission. Parameterizing with a Gaussian function the astrophysical kernel describing the local process of GW emission at galactic scales, we find the 95\% upper limit on kernel amplitude to be $2.7 \times 10^{-32}$ erg cm$^{-3}$s$^{-1/3}$ when ignoring the shot noise in the GW emission process, and $2.16 \times 10^{-32}$ erg cm$^{-3}$s$^{-1/3}$ when the shot noise is included in the analysis. As the sensitivity of the LIGO-Virgo-KAGRA network improves, we expect to be able to set more stringent bounds on this kernel function and constrain its parameters.

gr-qc↗

arXiv2023

pygwb: Python-based library for gravitational-wave background searches

The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the Universe and the population of GW sources within it. We present a new, user-friendly Python--based package for gravitational-wave data analysis to search for an isotropic GWB in ground--based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one's own needs. We describe the individual modules which make up {\tt pygwb}, following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline which combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results.

gr-qc↗

arXiv2021

Gravitational-Wave Geodesy: Defining False Alarm Probabilities with Respect to Correlated Noise

Future searches for a gravitational-wave background using Earth-based gravitational-wave detectors might be impacted by correlated noise sources. A well known example are the Schumann resonances, which are extensively studied in the context of searches for a gravitational-wave background. Earlier work has shown that a technique termed "gravitational-wave geodesy" can be used to generically differentiate observations of a gravitational-wave background from signals due to correlated terrestrial effects, requiring true observations to be consistent with the known geometry of our detector network. The key result of this test is a Bayes factor between the hypotheses that a candidate signal is astrophysical or terrestrial in origin. Here, we further formalize the geodesy test, mapping distributions of false-alarm and false-acceptance probabilities to quantify the degree with which a given Bayes factor will boost or diminish our confidence in an apparent detection of the gravitational-wave background. To define the false alarm probability of a given Bayes factor, we must have knowledge of our null hypothesis: the space of all possible correlated terrestrial signals. Since we do not have this knowledge we instead construct a generic space of smooth functions in the frequency domain using Gaussian processes, which we tailor to be conservative. This enables us to use draws from our Gaussian processes as a proxy for all possible non-astrophysical signals. As a demonstration, we apply the tool to the SNR = 1.25 excess observed for a 2/3-power law by the LIGO and Virgo collaborations during their second observing run.

gr-qc↗