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Method of Gravitational Wave Search Based on Adaptive Time-Frequency

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Method of Gravitational Wave Search Based on Adaptive Time-Frequency Impact Objectives • Develop novel signal-processing algorithms that use the synergy between machine learning/artificial intelligence and high-performance computing • Propose a new model for high-quality denoising with the ensemble learning technique Making waves in astrophysics Professor Hirotaka Takahashi is striving to play a key role in the quest to understand gravitational waves and multi-messenger astronomy and astrophysics Could you explain gravitational wave data analysis: the depth and machine learning can affect a wide area a little about and speed of gravitational wave searches beyond the gravitational wave data analysis. gravitational waves? are insufficient, so we need to develop algorithms that significantly increase their Could you describe your work on noise Recent detections/ speed and efficiency. It is critical to accelerate reduction in the context of machine learning? observations the development of novel signal-processing of gravitational algorithms that use the synergy between One of the representative methods of noise waves reported by the Laser Interferometer machine learning/artificial intelligence and reduction based on machine learning is a Gravitational-Wave Observatory (LIGO) and high-performance computing to maximise denoising autoencoder (DAE). The DAE Virgo collaborations have had a significant the potential for scientific discovery with is an extension of an autoencoder, which impact. Gravitational waves have been the gravitational wave and multi-messenger is a kind of a neural network (NN) used believed to be in existence since Hulse and astronomy and astrophysics. for dimensional reduction. The DAE is Taylor discovered the binary pulsar PSR B1913 constructed to output an original signal + 16 in 1974. The long-term radio observation Can you touch on some of the methods or when a superposition of the signal and of this system has shown that the observed tools you have been using in your research? noise is input. We found that the denoising orbital decay is well described by the energy/ performance can be enhanced by lining up angular momentum loss due to gravitational We are working on original analysis multiple DAEs. We named it ensemble DAE waves emission as predicted by Einstein. codes, called ‘KAGRA Algorithmic Library (E-DAE) and quantitatively evaluated its However, the direct detection/observation (KAGALI)’; ‘kagari (篝)’ is a Japanese word performance. of gravitational waves had an extraordinary for the bonfire at a celebration. The code impact, not only on the scientific community includes, for example, a couple of new We are proposing a new model for high- but also on the general public. methods developed in this research, and quality denoising named direct and parallel such new ideas are expected to enhance the DAE with the ensemble learning technique. The direct detection/observation of advantages of joint observations with LIGO We are also developing the model with the gravitational waves from binary black hole and Virgo. aim of applying it to the reconstruction of a and binary neutron star (BNS) mergers by burst gravitational waveform from obtained the LIGO Scientific Collaboration and Virgo Will your work have an impact beyond noisy data. Collaboration (LVC) opened a new window gravitational wave research? to probing the deep Universe with the Are there any challenges this research field dawn of multi-messenger observation with New signal processing and machine learning faces? electromagnetic waves. have begun to be studied and utilised in a wide range of fields such as voice/speech One serious challenge is the shortage of What is the context of your research on processing, image processing, biosignal manpower in the data analysis group at gravitational waves? processing (including electrocardiogram, KAGRA. It is important to attract students myoelectricity and EEG), and they also and postdocs in the other fields of physics To fully realise the multi-messenger have potential applications in the area of and astronomy, and to collaborate with astronomy and astrophysics science, we sports. Ultimately, the knowledge obtained industry. need to address specific problems in by this research of new signal processing www.impact.pub 43 Enhancing gravitational wave data analysis Gravitational wave research at Tokyo City University is building the potential of this field using machine learning/artificial intelligence techniques and novel signal-processing algorithms In simple terms, gravitational waves are impact in the field. LIGO collaborates and exploring gravitational physics. His ripples in space-time caused by energetic closely with the Virgo interferometer; a research on gravitational waves is attempting processes in the Universe, such as the large interferometer designed to detect to address these challenges by developing movement of mass. One of the exciting gravitational waves, and the Japanese algorithms that can dramatically increase the things about them is that they can be used to Gravitational Wave Telescope in Kamioka speed and efficiency of gravitational wave observe systems that are basically impossible Mine (KAGRA), the Large Scale Cryogenic searches, which he believes are currently to detect using other means. These ripples Gravitational Wave Telescope; a project of insufficient. ‘It is critical to accelerate the were predicted by Albert Einstein almost a the gravitational wave studies group led by development of novel signal-processing century ago, but it wasn’t until 2016 that the Institute for Cosmic Ray Research of The algorithms that use the synergy between scientists announced, for the first time, the University of Tokyo. machine learning/artificial intelligence detection of gravitational waves. (AI) and high-performance computing But there still remain many unknowns, such to maximise the potential for scientific The Laser Interferometer Gravitational- as challenges related to the data analysis discovery with gravitational waves and multi- Wave Observatory (LIGO) is the physics of gravitational waves. Professor Hirotaka messenger astronomy and astrophysics,’ experiment responsible for this detection and Takahashi, based at the Tokyo City University he explains. ‘Based on my experience it has since continued to make a significant has dedicated his career to understanding researching gravitational waves (astro) physics, information science and Big Data analysis, I would like to progress our understanding of this topic.’ To support this endeavour Takahashi is a member of the KAGRA collaboration, which, as of March 2020, consists of more than 390 researchers from 90 institutions in 14 countries and regions. EXTENSIVE EXPERIENCE Indeed, Takahashi has a wealth of knowledge in this field, having worked on the development of algorithms and the analysis of data from interferometric gravitational wave telescopes, LIGO, TAMA300, Virgo and KAGRA, since he was a graduate student, and participated in some high- level collaborations. ‘The first algorithm developed was searching for gravitational waves from merging binary neutron stars (BNS) by employing a matched filter method on observational data of TAMA300,’ he outlines. TAMA300 was planned as a prototype with 300 metre arms to develop Schematic view of KAGRA: Large Scale Cryogenic Gravitational Wave Telescope inside Kamioka Mine. L shape future technologies for a kilometre-scale is KAGRA. One arm length of L shape is 3km. (© ICRR, The University of Tokyo) interferometer. TAMA300 was located in 44 www.impact.pub the city of Mitaka, a suburb of Tokyo. The instantaneous variation of amplitude and gravitational waves and multi-messenger software developed is now a basic part of frequency of data, compared with the Fourier astronomy and astrophysics by developing KAGRA offline Compact Binary Coalescence decomposition and wavelet decomposition,’ novel signal-processing algorithms that (CBC) analysis. ‘I also demonstrated the Takahashi highlights. It is used in areas use the synergy between machine learning/ coincidence analysis method between as diverse as materials damage detection artificial intelligence and high-performance TAMA300 and LISM 20m interferometric and biomedical monitoring. Benefits of the computing to do so. ‘It is extremely gravitational wave telescope in Kamioka HHT include the fact that it provides high important to continue developing the Mine,’ he continues. ‘With these techniques time-frequency resolution, which enables original data analysis methods from a new and a lot of experience in analysing the researchers to investigate phenomena that perspective,’ he concludes. l data from TAMA300, I was also one of the have rapid changes in frequency as well core scientists who initiated the LIGO and as no or slow changes. Takahashi is now TAMA300 joint search. My knowledge and working with collaborators to develop a Project Insights expertise of a wide range of gravitational gravitational wave analysis method from wave signals allows me to participate in these standing Accretion Shock Instability (SASI) FUNDING kinds of international collaborations.’ of a core collapse supernova using HHT. This work was supported in part by JSPS ‘SASI has been considered key to generating Grant-in-Aid for Scientific Research (C) (Grant No. 17K05437), JSPS Grant-in-Aid The method of gravitational wave research gravitational wave emissions in the post- for Scientific Research (B) (Grant No. bounce supernova core,’ says Takahashi. ‘It that Takahashi is developing is based 19H01901) and JSPS KAKENHI (Grant on adaptive time-frequency
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