相关论文: Deep-Learning Classification and Parameter Inferen…
We present an analysis of the circular polarization of gravitational-waves (GWs) using results from three-dimensional (3D), general relativistic (GR) core-collapse simulations of a non-rotating $15 M_{\odot}$ star. For the signal detection,…
We discuss the science motivations and prospects for a joint analysis of gravitational-wave (GW) and low-energy neutrino data to search for prompt signals from nearby supernovae (SNe). Both gravitational-wave and low-energy neutrinos are…
We study supernova (SN) classification using the machine learning method of the Recurrent Neural Network (RNN) in the Chinese Space Station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey, and explore the improvement of the…
Improvements in ground-based, advanced gravitational wave (GW) detectors may allow in the near future to observe the GW signal of a nearby core-collapse supernova. For the most common type of progenitors, likely with slowly rotating cores,…
Gravitational waves are emitted from deep within a core-collapse supernova, which may enable us to determine the mechanism of the explosion from a gravitational-wave detection. Previous studies suggested that it is possible to determine if…
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…
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…
We propose a self-supervised learning model to denoise gravitational wave (GW) signals in the time series strain data without relying on waveform information. Denoising GW data is a crucial intermediate process for machine-learning-based…
Context. Gravitational waves (GW) provide a unique probe of the explosion mechanism of massive stars and the evolution of nascent proto-neutron stars (PNS). Magnetorotational explosions are one of the promising non-canonical core-collapse…
Core-collapse supernovae are fascinating astrophysical objects for multimessenger studies. Gravitational waves (GWs) are expected to play a role in the supernova explosion mechanism, but their modelling is also challenging due to the…
Recently, deep learning (DL) has been emerging as a promising approach for channel estimation and signal detection in wireless communications. The majority of the existing studies investigating the use of DL techniques in this domain focus…
Through numerical simulations, it is predicted that the gravitational waves (GWs) reflect the characteristics of the core-collapse supernova (CCSN) explosion mechanism. There are multiple GW excitation processes that occur inside a star…
Gravitational Waves (GWs) from coalescing binaries carry crucial information about their component sources, like mass, spin and tidal effects. This implies that the analysis of GW signals from binary neutron star mergers can offer unique…
Deep learning (DL) is an emerging analysis tool across sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nano-scale deeply sub-diffractional…
We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first…
Among astrophysical sources in the Advanced LIGO and Advanced Virgo detectors' frequency band are rotating non-axisymmetric neutron stars emitting long-lasting, almost-monochromatic gravitational waves. Searches for these continuous…
Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we…
The upper limit on the energy density of a stochastic gravitational wave (GW) background obtained from the two-year science run (S5) of the Laser Interferometer Gravitational-wave Observatory (LIGO) is used to constrain the average GW…
We have done a series of two-dimensional hydrodynamic simulations of the rotational collapse of a supernova core in axisymmetry. We have employed a realistic equation of state (EOS) and taken into account electron captures and neutrino…
In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections…