arXiv subjects

Tatsuki Washimi

Publications and source records attributed to Tatsuki Washimi.

6 recordsLinked to original sources

arXiv2025

Nonlinear Independent Component Analysis Scheme and its application to gravitational wave data analysis

Noise subtraction is a crucial process in gravitational wave (GW) data analysis to improve the sensitivity of interferometric detectors. While linear noise coupling has been extensively studied and successfully mitigated using methods such as Wiener filtering, subtraction of non-linearly coupled and non-stationary noise remains a significant challenge. In this work, we propose a novel independent component analysis (ICA)-based framework designed to address non-linear coupling in noise subtraction. Building upon previous developments, we derive a method to estimate general quadratic noise coupling while maintaining computational transparency compared to machine learning approaches. The proposed method is tested with simulated data and real GW strain data from KAGRA. Our results demonstrate the potential of this framework to effectively mitigate complex noise structures, providing a promising avenue for improving the sensitivity of GW detectors.

astro-ph.IM↗

arXiv2024

TOrsion-Bar Antenna: A Ground-Based Detector for Low-Frequency Gravity Gradient Measurement

The Torsion-Bar Antenna (TOBA) is a torsion pendulum-based gravitational detector developed to observe gravitational waves in frequencies between 1 mHz and 10 Hz. The low resonant frequency of the torsion pendulum enables observation in this frequency band on the ground. The final target of TOBA is to observe gravitational waves with a 10 m detector and expand the observation band of gravitational waves. In this paper, an overview of TOBA, including the previous prototype experiments and the current ongoing development, is presented.

gr-qc↗

arXiv2022

Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time-frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and reported.

gr-qc↗

arXiv2022

Identifying multichannel coherent couplings and causal relationships in gravitational wave detectors

The gravitational-wave detector is a complex and sensitive collection of advanced instruments that are impacted not only by mechanical/electronics systems but also by the surrounding environment. Hence, it is of great importance to classify and mitigate noises to detect gravitational-wave signals by using information from many auxiliary channels related to such devices and surroundings. This improves the signal-to-noise ratio and reduces false alarms from coincident loud events. For this reason, it is essential for identifying coherent relationships between complex channels. This study presents a way of identifying (non-) linear couplings between associated channels by using the method of correlation coefficients. And we show that the method can be applied to practical problems in the gravitational-wave detector, such as noises by lightning strokes, air compressors vibrations, and noises caused by wind effects.

astro-ph.IM↗

arXiv2022

Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors

In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding and/or imitation of gravitational-wave signals. This transient noise has various characteristics in the time--frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer clues for exploring its origin and improving the performance of the detector. One approach for accomplishing this is supervised learning. However, in general, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corresponding new classes. By contrast, unsupervised learning can reduce the annotation work for the training data and ensure objectivity in the classification and its corresponding new classes. In this study, we propose an unsupervised learning architecture for the classification of transient noise that combines a variational autoencoder and invariant information clustering. To evaluate the effectiveness of the proposed architecture, we used the dataset (time--frequency two-dimensional spectrogram images and labels) of the Laser Interferometer Gravitational-wave Observatory (LIGO) first observation run prepared by the Gravity Spy project. The classes provided by our proposed unsupervised learning architecture were consistent with the labels annotated by the Gravity Spy project, which manifests the potential for the existence of unrevealed classes.

gr-qc↗

arXiv2021

The KAGRA underground environment and lessons for the Einstein Telescope

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 possibility to extend the observation band to frequencies well below 10 Hz. For this reason, the proposed next-generation infrastructure Einstein Telescope in Europe would be realized underground aiming for an observation band that extends from 3 Hz to several kHz. However, it is known that ambient noise in the low-frequency band 10 Hz - 20 Hz at current surface sites of the Virgo and LIGO detectors is predominantly produced by the detector infrastructure. It is of utmost importance to avoid spoiling the quality of an underground site with noisy infrastructure, at least at frequencies where this noise can turn into a detector-sensitivity limitation. In this paper, we characterize the KAGRA underground site to determine the impact of its infrastructure on environmental fields. We find that while excess seismic noise is observed, its contribution in the important band below 20 Hz is minor preserving the full potential of this site to realize a low-frequency gravitational-wave detector. Moreover, we estimate the Newtonian-noise spectra of surface and underground seismic waves and of the acoustic field inside the caverns. We find that these will likely remain a minor contribution to KAGRA's instrument noise in the foreseeable future.

astro-ph.IM↗