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Stochastic background gravitational waves have not yet been detected by ground-based laser interferometric detectors, but recent improvements in detector sensitivity have raised considerable expectations for their eventual detection. Previous studies have introduced methods for exploring anisotropic background gravitational waves using Bayesian statistics. These studies represent a groundbreaking approach by offering physically motivated anisotropy mapping that is distinct from the Singular Value Decomposition regularization of the Fisher Information Matrix. However, they are limited by the use of a single model, which can introduce potential bias when dealing with complex data that may consist of a mixture of multiple models. Here, we demonstrate the bias introduced by a single-component model approach in the parametric interpretation of anisotropic stochastic gravitational-wave backgrounds, and we confirm that using multiple-component models can mitigate this bias.

The recent announcement of strong evidence for a stochastic gravitational-wave background (SGWB) by various pulsar timing array collaborations has highlighted this signal as a promising candidate for future observations. Despite its non-detection by ground-based detectors such as Advanced LIGO and Advanced Virgo, Callister \\textit{et al.}~\\cite{tom_nongr_method} developed a Bayesian formalism to search for an isotropic SGWB with non-tensorial polarizations, imposing constraints on signal amplitude in those components that violate general relativity using LIGO's data. Since our ultimate aim is to estimate the spatial distribution of gravitational-wave sources, we have extended this existing method to allow for anisotropic components in signal models. We then examined the potential benefits from including these additional components. Using injection campaigns, we found that introducing anisotropic components into a signal model led to more significant identification of the signal itself and violations of general relativity. Moreover, the results of our Bayesian parameter estimation suggested that anisotropic components aid in breaking down degeneracies between different polarization components, allowing us to infer model parameters more precisely than through an isotropic analysis. In contrast, constraints on signal amplitude remained comparable in the absence of such a signal. Although these results might depend on the assumed source distribution on the sky, such as the Galactic plane, the formalism presented in this work has laid a foundation for establishing a generalized Bayesian analysis for an SGWB, including its anisotropies and non-tensorial polarizations.

Stochastic background gravitational waves have not yet been detected by ground-based laser interferometric detectors, but recent improvements in detector sensitivity have raised considerable expectations for their eventual detection. Previous studies have introduced methods for exploring anisotropic background gravitational waves using Bayesian statistics. These studies represent a groundbreaking approach by offering physically motivated anisotropy mapping that is distinct from the Singular Value Decomposition regularization of the Fisher Information Matrix. However, they are limited by the use of a single model, which can introduce potential bias when dealing with complex data that may consist of a mixture of multiple models. Here, we demonstrate the bias introduced by a single-component model approach in the parametric interpretation of anisotropic stochastic gravitational-wave backgrounds, and we confirm that using multiple-component models can mitigate this bias.

Extended sources of the stochastic gravitational backgrounds have been conventionally searched on the spherical harmonics bases. The analysis during the previous observing runs by the ground-based gravitational wave detectors, such LIGO and Virgo, have yielded the constraints on the angular power spectrum \\(C_\\ell\\), yet it lacks the capability of estimating model parameters. In this paper, we introduce an alternative Bayesian formalism to search for such stochastic signals with a particular distribution of anisotropies on the sky. This approach provides a Bayesian posterior of model parameters and also enables selection tests among different signal models. While the conventional analysis fixes the highest angular scale \\textit{a priori}, here we show a more systematic and quantitative way to determine the cut-off scale based on a Bayes factor, which depends on the amplitude and the angular scale of observed signals. Also, we analyze the third observing runs of LIGO and Virgo for the population of milli-second pulsars and obtain the 95 % constrains of the signal amplitude, \\(\\epsilon < 2.7\\times 10^{-8}\\).

Multi-messenger astronomy is of great interest. The localization speed of gravitational wave sources is important for the success of electromagnetic follow-up. Although current gravitational wave source localization methods take up to a few seconds, even that is not sufficient for some electromagnetic bands. Therefore, one needs a more rapid localization method even if it is less accurate. Building upon an Excess power method, we describe a new localization method for compact object collisions that produces posterior probability maps in only a few hundred milliseconds. Some accuracy is lost, with the searched sky areas being approximately \\(10\\) times larger. We imagine this new technique playing a role in a hierarchical scheme where fast early location estimates are iteratively improved upon as better analyses complete on longer time scales.

We consider an anisotropic search for the stochastic gravitational-wave (GW) background by decomposing the gravitational-wave sky into its spherical harmonics components. Previous analyses have used the diffraction limit to define the highest-order spherical harmonics components used in this search. We investigate whether the angular resolution of this search is indeed diffraction-limited by testing our ability to detect and localize simulated GW signals. We show that while using low-order spherical harmonics modes is optimal for initially detecting GW sources, the detected sources can be better localized with higher-order spherical harmonics than expected based on the diffraction limit argument. Additionally, we discuss how the ability to recover simulated GW sources is affected by the number of detectors in the network, the frequency range over which the search is performed, and the method by which the covariance matrix of the GW skymap is regularized. While we primarily consider point-source signals in this study, we briefly apply our methodology to spatially-extended sources and discuss potential future modifications of our analysis for such signals.

Ultralight bosons, which are predicted in a variety of beyond-Standard-Model scenarios as dark-matter candidates, can trigger the superradiant instability around spinning black holes. This instability gives rise to oscillating boson condensates which then dissipate through the emission of nearly monochromatic gravitational waves. Such systems are promising sources for current and future gravitational-wave detectors. In this work, we consider minimally-coupled, massive vector bosons, which can produce a significantly stronger gravitational-wave signal compared to the scalar case. We adopt recently obtained numerical results for the gravitational-wave flux, and astrophysical models of black hole populations that include both isolated black holes and binary merger remnants, to compute and study in detail the stochastic gravitational-wave background emitted by these sources. Using a Bayesian framework, we search for such a background signal emitted using data from the first and second observing runs of Advanced LIGO. We find no evidence for such a signal. Therefore, the results allow us to constrain minimally coupled vector fields with masses in the range \\(0.8\\times10^{-13}\\mathrm{eV}\\leq m_b\\leq 6.0\\times10^{-13}\\mathrm{eV}\\) at 95% credibility, assuming optimistically that the dimensionless spin distribution for the isolated black hole population is uniform in the range \\([0,1]\\). With more pessimistic assumptions, a narrower range around \\(m_b\\approx 10^{-13}\\mathrm{eV}\\) can still be excluded as long as the upper end of the uniform distribution for dimensionless black hole spin is \\(\\gtrsim 0.2\\).

To evaluate the probability of a gravitational-wave candidate originating from noise, GstLAL collects noise statistics from the data it analyzes. Gravitational-wave signals of astrophysical origin get added to the noise statistics, harming the sensitivity of the search. We present the Background Filter, a novel tool to prevent this by removing noise statistics that were collected from gravitational-wave candidates. To demonstrate its efficacy, we analyze one week of LIGO and Virgo O3 data, and show that it improves the sensitivity of the analysis by 20-40% in the high mass region, in the presence of 868 simulated gravitational-wave signals. With the upcoming fourth observing run of LIGO, Virgo, and KAGRA expected to yield a high rate of gravitational-wave detections, we expect the Background Filter to be an important tool for increasing the sensitivity of a GstLAL analysis.

As gravitational-wave observations expand in scope and detection rate, the data analysis infrastructure must be modernized to accommodate rising computational demands and ensure sustainability. We present a scalable gravitational-wave search pipeline which modernizes the GstLAL pipeline by adapting the core filtering engine to the PyTorch framework, enabling flexible execution on both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Offline search results on the same 8.8 day stretch of public gravitational-wave data indicate that the GstLAL and the PyTorch adaptation demonstrate comparable search performance, even with float16 precision. Lastly, computational benchmarking results show that the GPU float16 configuration of the PyTorch adaptation executed on an A100 GPU can achieve a speedup factor of up to 169 times compared to GstLAL's performance on a single CPU core.

In this work, we develop a Bayesian data analysis framework to study the SGWB from bosonic clouds using data from Advanced LIGO and Advanced Virgo, building on previous work by Brito et.al (2017). We further improve this model by adding a BH population of binary merger remnants. To assess the performance of our pipeline, we quantify the range of boson masses that can be constrained by Advanced LIGO and Advanced Virgo measurements at design sensitivity. Furthermore, we explore our capability to distinguish an ultralight boson SGWB from a stochastic signal due to distant compact binary coalescences (CBC). Finally, we present results of a search for the SGWB from bosonic clouds using data from Advanced LIGO's first observing run. We find no evidence of such a signal. Due to degeneracies between the boson mass and unknown astrophysical quantities such as the distribution of isolated BH spins, our analysis cannot robustly exclude the presence of a bosonic field at any mass. Nevertheless, we show that under optimistic assumptions about the BH formation rate and spin distribution, boson masses in the range \\( \\SI{2.0e-13}{eV}\\leq m_\\mathrm{b}\\leq\\SI{3.8e-13}{eV} \\) are excluded at 95% credibility, although with less optimistic spin distributions, no masses can be excluded. The framework established here can be used to learn about the nature of fundamental bosonic fields with future gravitational wave observations.