Probing Rotation of Core-collapse Supernova with Concurrent Analysis of Gravitational Waves and Neutrinos

The next time a core-collapse supernova (SN) explodes in our galaxy, vari- ous detectors will be ready and waiting to detect its emissions of gravitational waves (GWs) and neutrinos. Current numerical simulations have successfully introduced multi-dimensional effects to produce exploding SN models, but thus far the explosion mechanism is not well understood. In this paper, we focus on an investigation of progenitor core rotation via comparison of the start time of GW emission and that of the neutronization burst. The GW and neutrino de- tectors are assumed to be, respectively, the KAGRA detector and a co-located gadolinium-loaded water Cherenkov detector, either EGADS or GADZOOKS!. Our detection simulation studies show that for a nearby supernova (0.2 kpc) we can confirm the lack of core rotation close to 100% of the time, and the presence of core rotation about 90% of the time. Using this approach there is also po- tential to confirm rotation for considerably more distant Milky Way supernova explosions.Comment: 31pages, 15figures, submit to Ap

Identifying and diagnosing coherent associations and causalities between multi-channels of the gravitational wave detector

The gravitational-wave detector is a very complicated and sensitive collection of advanced instruments, which is influenced not only by the mutual interaction between mechanical/electronics systems but also by the surrounding environment. Thus, it is necessary to categorize and reduce noises from many channels interconnected by such instruments and environment for achieving the detection of gravitational waves because it enhances to increase of a signal-to-noise ratio and reduces false alarms from coincident loud events. For this reason, it is of great importance to identify some coherent associations between complicated channels. This study presents a way of identifying (non-) linear couplings between interconnected channels by using some correlation coefficients, which are applied to practical issues such as noises by hardware injection test, lightning strokes, and air compressor vibrations gravitational-wave detector.Comment: 10 pages, 8 figures, and 2 table

Unsupervised learning architecture for classifying the transient noise of interferometric gravitational-wave detectors

Abstract: 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

Local Hurst Exponent Computation of Data from Triaxial Seismometers Monitoring KAGRA

The Hurst exponent was computed locally for three triaxial seismometers monitoring the KAGRA corner station and the two end stations of the interferometer allowing to estimate variability in the persistent behaviour of the seismometer time series. Results show that, in all the three cases considered, the vertical component of the seismometer has lower persistency compared to the horizontal degrees of freedom, as also confirmed by the low frequency region of the amplitude spectral density of the data. Furthermore, data of the horizontal components of the EXV end station, located in the direction of the X arm of the interferometer, exhibits lower values of seismic noise and of its Hurst exponent. This is possibly due to the lack of an exit point at this location, and hence to a reduced human activity, to the presence of water flow in the mine hosting the KAGRA detector or to differences in seismometers' self noise

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