相关论文: Comparison of neural network architectures for fea…

The GWTC-4.0 catalog of transient gravitational wave signals describes observations made in the first part of the fourth observing run of the LIGO-Virgo-KAGRA (LVK) gravitational wave detector network. Here we extend the LVK's GWTC-4.0…

We examined multiple deep neural network (DNN) architectures for suitability in predicting neurotransmitter concentrations from labeled in vitro fast scan cyclic voltammetry (FSCV) data collected on carbon fiber electrodes. Suitability is…

The first direct detection of neutron-star-black-hole binaries will likely be made with gravitational-wave observatories. Advanced LIGO and Advanced Virgo will be able to observe neutron-star-black-hole mergers at a maximum distance of…

The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of…

We present a comprehensive exploration of hierarchical triple black hole (BH) systems to address the "initial separation" problem in gravitational wave astrophysics. This problem arises because isolated BH binaries must have extremely small…

Optimal extraction of information from gravitational-wave observations of binary black-hole coalescences requires detailed knowledge of the waveforms. Current approaches for representing waveform information are based on spin-weighted…

We present PhenomPNR, a frequency-domain phenomenological model of the gravitational-wave (GW) signal from binary-black-hole mergers that is tuned to numerical relativity (NR) simulations of precessing binaries. In many current waveform…

Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specifically, recent focus is on time-domain based architectures such…

Deep learning based on deep neural networks has been very successful in many practical applications, but it lacks enough theoretical understanding due to the network architectures and structures. In this paper we establish some analysis for…

The extraction and proper utilization of convolution neural network (CNN) features have a significant impact on the performance of image super-resolution (SR). Although CNN features contain both the spatial and channel information, current…

The signal-to-noise ratios (SNRs) for quasi-circular binary black hole inspirals computed from restricted post-Newtonian waveforms are compared with those attained by more complete post-Newtonian signals, which are superpositions of…

The detection and characterization of post-merger gravitational wave signals from binary neutron star mergers remains challenging with current ground-based detectors. We present a convolutional neural network framework designed for…

A waveform model for the eccentric binary black holes named SEOBNRE has been used to analyze the LIGO-Virgo's gravitational wave data by several groups. The accuracy of this model has been validated by comparing it with numerical…

As gravitational-wave (GW) observations of binary black holes are becoming a precision tool for physics and astronomy, several subdominant effects in the GW signals need to be accurately modeled. Previous studies have shown that neglecting…

Temporal Neural Networks (TNNs) are spiking neural networks that use time as a resource to represent and process information, similar to the mammalian neocortex. In contrast to compute-intensive deep neural networks that employ separate…

We study the effectiveness of stationary-phase approximated post-Newtonian waveforms currently used by ground-based gravitational-wave detectors to search for the coalescence of binary black holes by comparing them to an accurate waveform…

Despite the effectiveness of Convolutional Neural Networks (CNNs) for image classification, our understanding of the relationship between shape of convolution kernels and learned representations is limited. In this work, we explore and…

In this project, we simulate the collision of two and three black holes using NRPy+ (`Python-based code generation for numerical relativity and beyond') module and BSSN (Baumgarte-Shapiro-Shibata-Nakamura) formulation, and extract the…

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…

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…