Thesis

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Machine Learning approaches for Gravitational-Wave Astronomy: data analysis and detection

Details

  • Call:

    PT-CERN Call 2020/2

  • Academic Year:

    2020/2021

  • Domain:

    Astroparticle Physics

  • Supervisor:

    Antonio Onofre

  • Co-Supervisor:

  • Institution:

    Universidade do Minho

  • Host Institution:

    CF-UM-UP - Centro de Física das Universidades do Minho e do Porto

  • Abstract:

    The first detection of a gravitational wave (GW) signal in 2015, generated by the merger of two black holes (BHs), opened an entirely new way to understand the cosmos [1] unveiling a previously unknown population of massive stellar BHs and enabling the first tests of General Relativity (GR) in the strong-field regime. In 2017, the first observation of a binary neutron star (BNS) merger by the LIGO and Virgo detectors opened the era of multi-messenger astronomical observations [2]. With the unprecedented coordinated action of the LIGO Scientific Collaboration (LSC), the Virgo Collaboration, and dozens of astronomical facilities, key evidence to address open issues in relativistic astrophysics was collected [3]. The evolution of the kilonova phenomenon has been studied in detail [4,5] and GW observations of the event have constrained the equation of state of neutron stars (NS) [6]. The combination of the electromagnetic (EM) and GW observations has also opened the field of GW cosmology with a new way to measure the Hubble constant [7]. Searches of neutrino counterparts of GW events are also carried out routinely, but have not yet led to a detection [8]. Moreover, during the last observational campaign of the LIGO-Virgo detectors, O3, GW candidate events have been released as public alerts to facilitate the rapid identification of EM or neutrino counterparts, expanding the capabilities of multi-messenger astronomy. A significant number of candidates have been publicly announced on the GW candidate event database (gracedb.ligo.org) and some confirmed detections have already been published [9-13], increasing the number of detections from the first two observing runs [14].