相关论文: Comparative study of 1D and 2D convolutional neura…
Electrocardiogram (ECG)-based biometric recognition has emerged as a promising solution for secure authentication and liveness detection. However, most existing methods rely on unimodal deep learning architectures that independently process…
We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with…
The early inspiral from stellar-mass binary black holes (sBBHs) can emit milli-Hertz gravitational wave signals, making them detectable sources for space-borne gravitational wave missions like TianQin. However, the traditional matched…
Gravitational waves emitted from the coalescence of neutron star binaries open a new window to probe matter and fundamental physics in unexplored, extreme regimes. To extract information about the supranuclear matter inside neutron stars…
In the observation of gravitational waves (GWs) from a compact binary coalescence system where the mass of one of the companions is $\leq 5~M_{\odot}$ the nature of the object is ambiguous until the measurements of tidal effects give…
One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the…
Gravitational wave-forms from coalescences of binary black hole systems and binary neutron star systems with low tidal effects can hardly be distinguished if the two systems have similar masses. In the absence of discriminating power based…
Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artefacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact…
The observation of gravitational wave signals from binary black hole mergers has established the field of gravitational wave astronomy. It is expected that future networks of gravitational wave detectors will possess great potential in…
The gravitational-wave signal from the merger of two neutron stars cannot be easily differentiated from the signal produced by a comparable-mass mixed binary of a neutron star and a black hole. Indeed, both binary types can account for the…
Black hole (BH) - neutron star (NS) binary mergers are not only strong sources of gravitational waves (GWs), but they are also candidates for joint detections in the GW and electromagnetic (EM) spectra. However, the possible emergence of an…
In August 2017, the first detection of a binary neutron star merger, GW170817, made it possible to study neutron stars in compact binary systems using gravitational waves. Despite being the loudest gravitational wave event detected to date…
Massive black hole binaries (MBHBs) are binary systems formed by black holes with mass exceeding millions of solar masses, expected to form and evolve in the nuclei of galaxies. The extreme compact nature of such objects determines a loud…
We present a comprehensive study of the effectiveness of Convolution Neural Networks (CNNs) to detect long duration transient gravitational-wave signals lasting $O(hours-days)$ from isolated neutron stars. We determine that CNNs are robust…
The high computational cost of wide-parameter-space searches for continuous gravitational waves (CWs) significantly limits the achievable sensitivity. This challenge has motivated the exploration of alternative search methods, such as deep…
The detection of the events GW150914 and GW151226, both consistent with the merger of a binary black hole system (BBH), opened the era of gravitational wave (GW) astronomy. Besides BBHs, the most promising GW sources are the coalescences of…
This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a classification problem with one class for the interesting…
In this work, we propose a novel Convolutional Neural Network (CNN) architecture for the joint detection and matching of feature points in images acquired by different sensors using a single forward pass. The resulting feature detector is…
The coalescence of binary neutron stars (BNS) is a prolific source of gravitational waves (GWs) and electromagnetic (EM) radiation, offering a dual observational window into the Universe. Lowering the signal-to-noise ratio (S/N) threshold…
Classification of EEG signals using shallow Convolutional Neural Networks (CNNs) is a prevalent and successful approach across a variety of fields. Most of these models use independent one-dimensional (1D) convolutional layers along the…