相关论文: Noise subtraction from KAGRA O3GK data using Indep…
Ground-based gravitational-wave detectors are based on high precision laser interferometry. One promising technique to improve the detector's sensitivity is the detuning of an optical cavity, which enhances the signal at around certain…
Independent component analysis (ICA) is a statistical method for transforming an observable multidimensional random vector into components that are as statistically independent as possible from each other.Usually the ICA framework assumes a…
Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences. In its classic form, ICA relies on modeling the data as linear mixtures of non-Gaussian…
Independent component analysis (ICA) is a widespread data exploration technique, where observed signals are modeled as linear mixtures of independent components. From a machine learning point of view, it amounts to a matrix factorization…
Control noise is a limiting factor in the low-frequency performance of the LIGO gravitational wave detectors. In this paper we model the effects of using new sensors called HoQIs to control the suspension resonances. We show if we were to…
Independent Component Analysis (ICA) is a fundamental unsupervised learning technique foruncovering latent structure in data by separating mixed signals into their independent sources. While substantial progress has been made in…
Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to search for biomarkers in neurological disorders including autism spectrum disorder…
Independent component analysis (ICA) aims at decomposing an observed random vector into statistically independent variables. Deflation-based implementations, such as the popular one-unit FastICA algorithm and its variants, extract the…
The KAGRA gravitational-wave detector in Japan is the only operating detector hosted in an underground infrastructure. Underground sites promise a greatly reduced contribution of the environment to detector noise thereby opening the…
Blind source separation, particularly through independent component analysis (ICA), is widely utilized across various signal processing domains for disentangling underlying components from observed mixed signals, owing to its fully…
We present a new algorithm for Independent Component Analysis (ICA) which has provable performance guarantees. In particular, suppose we are given samples of the form $y = Ax + \eta$ where $A$ is an unknown $n \times n$ matrix and $x$ is a…
Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the…
Technical and environmental noise in ground-based laser interferometers designed for gravitational-wave observations like Advanced LIGO, Advanced Virgo and KAGRA, can manifest as narrow (<1Hz) or broadband ($10'$s or even $100'$s of Hz)…
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. In this paper we present Multiple-weighted Independent Component Analysis…
KAGRA is the first large-scale gravitational-wave detector with cryogenic test masses. Its target sensitivity is limited mostly by quantum noise in the observation frequency band owing to the remarkable reduction of thermal noise at…
Environmental noise is one of the critical issues for the observation of gravitational waves, but is difficult to predict in advance. Therefore, to evaluate the adverse impact of environmental noise on the detector sensitivity,…
One of the limiting noise sources of ground-based gravitational wave detectors at frequencies below 30 Hz is control-induced displacement noise. Compact laser interferometric sensors are a prime candidate for improved local displacement…
Independent Component Analysis (ICA) is a computational technique for revealing latent factors that underlie sets of measurements or signals. It has become a standard technique in functional neuroimaging. In functional neuroimaging, so…
Gravitational-wave now became one of the important observational methods for studying the Universe since its first detection. However, the ground-based observatories have an inherent barrier to their detection frequency band due to the…
We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R{\'e}nyi's $\alpha$-order entropy functional, our network can…