ABSTRACT
The joint detection of GW170817 and GRB 170817A opened the era of multimessenger astronomy with gravitational waves (GWs) and provided the first direct probe that at least some binary ...neutron star (BNS) mergers are progenitors of short gamma-ray bursts (S-GRBs). In the next years, we expect to have more multimessenger detections of BNS mergers, thanks to the increasing sensitivity of GW detectors. Here, we present a comprehensive study on the prospects for joint GW and electromagnetic observations of merging BNSs in the fourth Laser Interferometer Gravitational-wave Observatory (LIGO)–Virgo–Kamioka Gravitational Wave Detector (KAGRA) observing run with Fermi Gamma-ray Space Telescope (Fermi), Neil Gehrels Swift Observatory (Swift), INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL), and Space Variable Objects Monitor (SVOM). This work combines accurate population synthesis models with simulations of the expected GW signals and the associated S-GRBs, considering different assumptions about the gamma-ray burst (GRB) jet structure. We show that the expected rate of joint GW and electromagnetic detections could be up to ∼6 yr−1 when Fermi/Gamma-ray Burst Monitor (GBM) is considered. Future joint observations will help us to better constrain the association between BNS mergers and S-GRBs, as well as the geometry of the GRB jets.
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 extremely successful in detecting gravitational wave signals from coalescence of black holes and/or neutron stars. However, in order to reach the required sensitivities, the background noise must be investigated and removed. In particular, transient noise events called “glitches” can affect data quality and mimic real astrophysical signals, and it is therefore of paramount importance to characterize them and find their origin, a task that will support the activities of detector characterization of Virgo and other interferometers. Machine learning is one of the most promising approaches to characterize and remove noise glitches in real time, thus improving the sensitivity of interferometers. A key input to the preparation of a training dataset for these machine learning algorithms can originate from citizen science initiatives, where volunteers contribute to classify and analyze signals collected by detectors. We will present GWitchHunters, a new citizen science project focused on the study of gravitational wave noise, that has been developed within the REINFORCE project (a “Science With And For Society” project funded under the EU’s H2020 program). We will present the project, its development and the key tasks that citizens are participating in, as well as its impact on the study of noise in the Advanced Virgo detector.
The multimessenger sky seen by Fermi Razzano, Massimiliano; Pivato, Giovanna
Nuclear and particle physics proceedings,
August-September 2015, 2015-08-00, Letnik:
265-266
Journal Article
Recenzirano
In its first six years of operations, the Fermi Gamma-ray Space Telescope has revolutionized our view of the gamma-ray Universe. The Large Area Telescope (LAT), the main instrument onboard Fermi, has ...discovered more than two thousand new GeV sources and unveiled new classes of gamma-ray emitters. The LAT is also a very versatile instrument, that offers the possibility to detect not only gamma rays but also charged particles (e.g. electrons and positrons), thus contributing to a better understanding of cosmic ray physics. We will report on the status of the observatory and highlight the most recent science results, with particular attention to the multimessenger context.
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based ...gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.
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