相关论文: Comparative study of 1D and 2D convolutional neura…

Mergers of compact binaries, such as binary neutron stars (BNSs), neutron star-black hole binaries (NSBHs), and binary black holes (BBHs), are expected to be the best candidates for the sources of gravitational waves (GWs) and the leading…

This paper proposes a 1D residual convolutional neural network (CNN) architecture for music genre classification and compares it with other recent 1D CNN architectures. The 1D CNNs learn a representation and a discriminant directly from the…

In the past few years, the detection of gravitational waves from compact binary coalescences with the Advanced LIGO and Advanced Virgo detectors has become routine. Future observatories will detect even larger numbers of gravitational-wave…

Next-generation gravitational wave detectors are expected to detect millions of compact binary mergers across cosmological distances. The features of the mass distribution of these mergers, combined with gravitational wave distance…

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…

Gravitational-wave signals from compact binary coalescences are most efficiently identified through matched filter searches, which match the data against a pre-generated bank of gravitational-wave templates. Although different techniques…

In deep learning, transfer learning and ensemble models have shown promise in improving computer-aided disease diagnosis. However, applying the transfer learning and ensemble model is still relatively limited. Moreover, the ensemble model's…

Ground-based gravitational wave detectors are sensitive to a narrow range of frequencies, effectively taking a snapshot of merging compact-object binary dynamics just before merger. We demonstrate that by adopting analysis parameters that…

In this study, we use simple performance metrics to assess the science capabilities of future ground-based gravitational-wave detector networks -- composed of A+ or Voyager upgrades to the LIGO, Virgo, and KAGRA observatories and proposed…

This work is focused on the morphological classification of galaxies following the Hubble sequence in which the different classes are arranged in a hierarchy. The proposed method, BCNN, is composed of two main modules. First, a…

Six gravitational wave detections have been reported so far, providing crucial insights on the merger rate of double compact objects. We investigate the cosmic merger rate of double neutron stars (DNSs), neutron star-black hole binaries…

Convolutional Neural Networks (CNN) have emerged as powerful tools for learning discriminative image features. In this paper, we propose a framework of 3-D fully CNN models for Glioblastoma segmentation from multi-modality MRI data. By…

Gravitational-wave detections are enabling measurements of the rate of coalescences of binaries composed of two compact objects -- neutron stars and/or black holes. The coalescence rate of binaries containing neutron stars is further…

Gravitational wave astronomy has been already a well-established research domain for many years. Moreover, after the detection by LIGO/Virgo collaboration, in 2017, of the first gravitational wave signal emitted during the collision of a…

Accurate information from gravitational wave signals from coalescing binary neutron stars provides essential input to downstream interpretations, including inference of the neutron star population and equation of state. However, even…

The growing number of binary black hole mergers detected through gravitational waves offers unprecedented insight into their underlying population, yet their astrophysical formation channels remain unresolved. We present a new method to…

Next-generation gravitational-wave detectors like the Einstein Telescope and Cosmic Explorer, currently in their preparatory phase, have the potential to significantly improve our understanding of astrophysics, cosmology and fundamental…

Currently, the sub-60 Hz sensitivity of gravitational-wave (GW) detectors like Advanced LIGO is limited by the control noises from auxiliary degrees of freedom, which nonlinearly couple to the main GW readout. One particularly promising way…

We describe the PyCBC search for gravitational waves from compact-object binary coalescences in advanced gravitational-wave detector data. The search was used in the first Advanced LIGO observing run and unambiguously identified two black…

We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binary-black-hole mergers. We build…