Di Cesare, Martina (2026) All sky searches for isolated and binary neutron star system: study of improved procedures, based on classical and Machine Learning techniques, for present and future gravitational wave detectors. [Tesi di dottorato]

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: All sky searches for isolated and binary neutron star system: study of improved procedures, based on classical and Machine Learning techniques, for present and future gravitational wave detectors
Autori:
Autore
Email
Di Cesare, Martina
martina.dicesare@unina.it
Data: 2026
Numero di pagine: 159
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Fisica
Dottorato: Fisica
Ciclo di dottorato: 38
Coordinatore del Corso di dottorato:
nome
email
Canale, Vincenzo
[non definito]
Tutor:
nome
email
De Rosa, Rosario
[non definito]
Astone, Pia
[non definito]
Serra, Marco
[non definito]
Data: 2026
Numero di pagine: 159
Parole chiave: Gravitational waves, Neutron stars, Machine Learning
Settori scientifico-disciplinari del MIUR: Area 02 - Scienze fisiche > FIS/05 - Astronomia e astrofisica
Informazioni aggiuntive: Dottoranda appartenente al ciclo XVIII.
Depositato il: 17 Feb 2026 07:24
Ultima modifica: 12 Ago 2026 05:37
URI: https://www.fedoa.unina.it/id/eprint/16183

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Abstract

Gravitational waves (GWs), predicted by General Relativity and first directly detected in 2015, have opened a new observational window on compact objects such as black holes (BHs) and neutron stars (NSs). Among the various classes of GW sources, continuous waves (CWs) - long-lived, nearly monochromatic signals likely emitted by rotating NSs - remain undetected, motivating increasingly sensitive searches with both current and future interferometric detectors. This thesis investigates two complementary directions aimed at improving the detection prospects of CWs in all-sky searches: (i) assessing the performance and limitations of the Frequency Hough (FH) pipeline, which is part of a hierarchical procedure designed to look for unknown isolated NSs, when signals originate from NSs in binary systems, and (ii) developing and testing a Machine-Learning-based alternative to the FH candidates follow-up (FU) stage. (i) For the binary scenario, we quantify the degradation produced by the binary Rømer delay - an additional Doppler effect due to the NS being in a binary system. By exploring various orbital periods, semi-major axes, and sky positions, we show that parameter recovery worsens predictably with stronger binary modulation, though standard coincidence thresholds between detectors remain effective. These results delineate the operational limits of FH for binary CW searches and provide guidance for future observing runs and third-generation detectors, where many known binaries are expected to emit detectable CWs. (ii) Using real O3 coincidence candidates from the two LIGO detectors and software injections at different strain levels, we obtain robust performance, with only a small fraction of errors in signal characterization, and further improvement after cross-detector coincidence. Application to hardware-injection regions reproduces the behaviour of the FH pipeline. Overall, this thesis demonstrates that the FH pipeline, designed to search for isolated NS, can also be used to indicate the presence of a binary CW signals, within a predictable and obviously limited parameter space and that the ML-based strategy offers a feasible and competitive alternative for candidate FU.

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