Since the first observation of a gravitational-wave (GW) in 2015, the LIGO and Virgo detectors reported tens of astrophysical signals interpreted as mergers of compact objects. These observations provide invaluable tests of the General Relativity and open a new era of astronomy, unveiling compact objects’ nature. The focus of the thesis is the detection and the characterization of GW transients with minimal assumptions on the GW sources. To identify astrophysical signals embedded in detector noise, there are two main approaches: template-based and unmodelled searches. The firsts look for GW signals with a time-frequency evolution consistent to the waveform models contained in extensive template banks. Instead, unmodelled or burst searches do not assume a waveform model, but look for excess of power that is coherent on multiple GW detectors. Burst search are fundamental to observe GWs from various astrophysical sources. Unmodelled searches observe GWs originated from the coalescence of compact binaries, and might observe GWs that are expected by other sources such as supernovae, isolated neutron stars, and cosmic strings. Burst searches also provide the reconstruction of the GW waveform with minimal assumptions, and are able to identify discrepancies between theoretical models and measured data, which may reveal new physics. A well-known software for burst searches is Coherent WaveBurst (cWB). cWB identifies excess of power with respect to the detector noise that are coherent in the GW detectors network. Within this framework, the thesis presents three author’s original contributions to this field.
The first is the search sensitivity of three-detectors network in burst searches. Having more detectors participating in the GW observations generally improves the source localization and the characterization of the GW signals. The capability of burst searches to distinguish between potential signals and transient noise depends on the orientation of the detectors and on their relative sensitivities. In literature, the cWB search sensitivity of the three-detectors network composed of the LIGO and Virgo detectors (HLV) is lower than the one achieved using only LIGO detectors (HL). cWB uses likelihood regulators to force the reconstruction of the GW component observed by the LIGO aligned detectors. These regulators successfully reduce the false alarm rate of the HL coherent analysis, but to make full use of a third, not-aligned detector, they should be relaxed. The fifth chapter investigates the impact of the likelihood regulators in cWB for HLV network, first in a simplified case assuming Gaussian noise only, and then in the data from the third LIGO-Virgo-KAGRA observing run. Thanks to latest cWB enhancements and relaxed likelihood regulators, we show that the HLV network reduces significantly the gap w.r.t. HL, having a higher sensitivity for several waveforms tested on average over the sky directions. Moreover, we investigate the use of the HLV network to test the consistency between cWB unmodelled signal reconstruction and the GW waveform models. The second original contribution is the development of an autoencoder neural network integrated into GW burst searches to improve the rejection of noise transients GW data contains short-duration disturbances, called glitches, which can mimic astrophysical signals. Mitigation of glitches is particularly difficult for unmodelled algorithm, such as cWB, that do not use GW waveform models to filter the data, but are sensitive to the widest possible range of morphologies. Noise mitigation is a long-term effort in cWB, which led to the introduction of specific estimators and a machine-learning based signal-noise classification algorithm. The sixth chapter presents an autoencoder neural network, integrated into cWB, that learns transient noise morphologies from GW time-series and it improves their rejection. An autoencoder is an unsupervised learning neural network that compresses the input data into a lower dimensional space, called latent space, and then re-constructs an output with the original dimensions. Here, the autoencoder is trained on time-series belonging to a single glitch family, known as blip, and the network learns that specific morphology. The autoencoder improves cWB discrimination between blip-like glitches and potential GW signals, reducing the background trigger at low frequencies. We inject in the LIGO detectors’ data from the third Advanced LIGO-Virgo observing run a wide range of simulated signals, and we evaluate the cWB search sensitivity including the autoencoder output in the cWB ranking statistics. At a false alarm rate of one event per 50 years, the sensitivity volume increases up to 30% for signal morphologies similar to blip glitches. Finally, the thesis presents the search for hyperbolic encounters between compact objects in the data from the third LIGO-Virgo-KAGRA observing run. As GW detectors sensitivity increases, new astrophysical sources could emerge. Close hyperbolic encounters (HE) are one such source class: scattering of stellar mass compact objects is expected to manifest as GW burst signals in the frequency band of current detectors. The seventh chapter presents the search for GWs from HE in the data from the second-half of the third observing run using cWB. No significant event has been identified in addition to known detections of GW events. We inject third Post-Newtonian order accurate HE waveforms with component masses between [2,100]M ⊙ . For the first time, we report the sensitivity volume achieved for such sources, i.e. the portion of the Universe in which the proposed analysis would have detected a HE signal with a certain significance, if any. The sensitivity volume peaks at 3.9±1.4×10 5 Mpc3 year for compact objects with masses between [20, 40] M ⊙, corresponding to a rate density upper limit of 0.589±0.094 ×10 −5 Mpc −3 year −1. Moreover, the sensitive volume prospects for the next observing runs of current detectors are discussed. All the result shown are based on the latest publicly available data from the third observing run of the LIGO-Virgo-KAGRA collaboration.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/415930 |
| Date | 03 July 2024 |
| Creators | Bini, Sophie |
| Contributors | Bini, Sophie, Prodi, Giovanni Andrea |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:130, numberofpages:130 |
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