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We describe the extension to multiple datasets of a coherent method for the search of continuous gravitational wave signals, based on the computation of 5-vectors. In particular, we show how to coherently combine different datasets…
Gravitational wave detectors will need optimal signal-processing algorithms to extract weak signals from the detector noise. Most algorithms designed to date are based on the unrealistic assumption that the detector noise may be modeled as…
The gravitational waveform of merging binary neutron stars encodes information about extreme states of matter. Probing these gravitational emissions requires the gravitational-wave detectors to have high sensitivity above 1 kHz. Fortunately…
In order to analyze data produced by the kilometer-scale gravitational wave detectors that will begin operation early next century, one needs to develop robust statistical tools capable of extracting weak signals from the detector noise.…
Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched…
Gravitational wave echoes offer a unique probe of the near-horizon structure of astrophysical black holes, beyond the standard ''black hole spectroscopy''. Theoretical waveform predictions, however, remain uncertain, motivating robust…
Gravitational waves detected by advanced ground-based detectors have allowed studying the universe in a way which is fully complementary to electromagnetic observations. As more sources are detected, it will be possible to measure…
The Gravitational waves have opened a new window on the Universe and paved the way to a new era of multimessenger observations of cosmic sources. Second-generation ground-based detectors such as Advanced LIGO and Advanced Virgo have been…
Searches for continuous gravitational waves target nearly monochromatic gravitational wave emission from e.g. non-axysmmetric fast-spinning neutron stars. Broad surveys often require to explicitly search for a very large number of different…
Gravitational wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template based matched filtering method. While this method is optimal for stationary and Gaussian data…
Estimating the parameters of gravitational wave signals detected by ground-based detectors requires an understanding of the properties of the detectors' noise. In particular, the most commonly used likelihood function for gravitational wave…
Approximations are commonly employed in realistic applications of scientific Bayesian inference, often due to convenience if not necessity. In the field of gravitational-wave (GW) data analysis, fast-to-evaluate but approximate waveform…
Machine learning can be a powerful tool to discover new signal types in astronomical data. We here apply it to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield physical insight into the…
The accuracy of Bayesian inference can be negatively affected by the use of inaccurate forward models. In the case of gravitational-wave inference, accurate but computationally expensive waveform models are sometimes substituted with faster…
In this work we use genetic algorithm to search for the gravitational wave signal from the inspiralling massive Black Hole binaries in the simulated LISA data. We consider a single signal in the Gaussian instrumental noise. This is a first…
Searching for gravitational-wave signals is a challenging and computationally intensive endeavor undertaken by multiple independent analysis pipelines. While detection depends only on observed noisy data, it is sometimes inconsistently…
With the anticipated launch of space-based gravitational wave detectors, including LISA, TaiJi, TianQin, and DECIGO, expected around 2030, the detection of gravitational waves generated by intermediate-mass black hole binaries (IMBBHs)…
We present a machine learning framework for testing general relativity (GR) with gravitational wave signals from binary black hole mergers. Using the source parameters of 173 BBH events from the GWTC catalog as a realistic astrophysical…
We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and…
This thesis presents advancements in the detection of gravitational waves from compact binary coalescences, utilising the most sensitive observatories constructed to date. The research focuses on enhancing gravitational-wave signal searches…