Overview of KAGRA: Detector design and construction history

Akutsu, T.; Ando, Masaki; Arai, K.; Arai, Y.; Araki, S.; Araya, Akito; Aritomi, N.; Aso, Y.; Bae, S.; Bae, Y.; Baiotti, Luca; Bajpai, R.; Barton, M. A.; Cannon, K. C.; Capocasa, E.; Chan, M.; Chen, C; Chen, K; Chen, Y; Chu, Hong‐Yu; Chu, Y-K.; Eguchi, S.; Enomoto, Y.; Flaminio, R.; Fujii, Yoshinori; Fukunaga, Masataka; Fukushima, Mitsuhiro; Ge, G.; Hagiwara, A.; Haino, S.; Hasegawa, K.; Hayakawa, Hisao; Hayama, K.; Himemoto, Y.; Hiranuma, Y.; Hirata, N.; Hirose, E.; Zhou, Hong; Hsieh, B-H.; Huang, C-Z.; Huang, P.; Huang, Y-C.; Ikenoue, B.; Imam, S.; Inayoshi, Kohei; Inoue, Yoshiyuki; Ioka, Kunihito; Itoh, Y.; Izumi, K.; Jung, K.; Jung, P.; Kajita, T.; Kamiizumi, M.; Kanda, Nobuyuki; Kang, G.; Kawaguchi, Kyohei; Kawai, N.; Kawasaki, T.; Kim, C; Kim, J C; Kim, W S; Kim, Y -M; Kimura, N.; Kita, Naoki; Kitazawa, H.; Kojima, Yasufumi; Kokeyama, K.; Komori, K; Kong, Albert; Kotake, Kei; Kozakai, C.; Kozu, R.; Kumar, Rakesh; Kume, J.; Kuo, C.; Kuo, H-S.; Kuroyanagi, Sachiko; Kusayanagi, K.; Kwak, Kyujin; Lee, H K; Lee, H W; Lee, R; Leonardi, M.; Lin, L. C.-C.; Lin, C‐Y.; Lin, F-K.; Liu, G C; Luo, L.-W.; Marchio, M.; Michimura, Yuta; Mio, Norikatsu; Miyakawa, O.; Miyamoto, A.; Miyazaki, Yuki; Miyo, K.; Miyoki, S.; Morisaki, S.; Moriwaki, Y.; Nagano, Koji; Nagano, S.; Nakamura, Kouji; Nakano, Hiroyuki; Nakano, Masayuki; Nakashima, Ryosuke; Narikawa, T.; Negishi, R.; Ni, Wei-Tou; Nishizawa, A.; Obuchi, Yasunari; Ogaki, W.; Oh, J. J.; Oh, Sang Hoon; Ohashi, M.; Ohishi, N.; Ohkawa, Masashi; Okutomi, K.; Oohara, Ken–ichi; Ooi, C.; Oshino, S.; Pan, K.; Pang, H.; Park, J; Arellano, F. E. Peña; Pinto, I. M.; Sago, Norichika; Saito, Shinya; Saito, Yoshihiko; Sakai, Kazuki; Sakai, Y.; Sakuno, Yuji; Sato, S.; Sato, T.; Sawada, T.; Sekiguchi, T.; Sekiguchi, Y.; Shibagaki, S.; Shimizu, R.; Shimoda, T.; Shimode, K.; Shinkai, H.; Shishido, T.; Shoda, A.; Somiya, K.; Son, E. J.; Sotani, Hajime; Sugimoto, R.; Suzuki, Toshikazu; Tagoshi, Hideyuki; Takahashi, H.; Takahashi, Ryutaro; Takamori, A.; Takano, S.; Takeda, H.; Takeda, M.; Tanaka, H.; Tanaka, K.; Tanaka, Taiki; Tanioka, S.; Martín, E. N. Tapia San; Telada, Souichi; Tomaru, T.; Tomigami, Y.; Tomura, T.; Travasso, F.; Trozzo, L.; Tsang, T.; Tsubono, K.; Tsuchida, S.; Tsuzuki, Toshihiro; Tuyenbayev, D.; Uchikata, N.; Uchiyama, Takashi; Ueda, A.; Uehara, T.; Ueno, K.; Ueshima, G.; Uraguchi, Fumihiro; Ushiba, T.; Putten, Maurice H. P. M. van; Vocca, H.; Wang, J; Wu, C.; Wu, H.; Wu, S.; Xu, W-R.; Yamada, Tomohiro; Yamamoto, K.; Yamamoto, Takahiro; Yokogawa, K.; Yokoyama, Jun′ichi; Yokozawa, T.; Yoshioka, T.; Yuzurihara, H.; Zeidler, S.; Zhao, Yuhang; Zhu, Z-H

doi:10.1093/ptep/ptaa125

Cited by 359 publications

(176 citation statements)

References 54 publications

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“…6 with and without distance marginalisation. Nessai requires a median of 5.04 ⇥ 10 6 and 7.22 ⇥ 10 6 likelihood evaluations to converge with and without distance marginalisation respectively and dynesty requires 10.44⇥10 6 and 9.67⇥10 6 . In contrast to dynesty, sampling with Nessai is more ecient without distance marginalisation, we attribute this to a combination of the reparameterisation used for luminosity distance and the sampler settings we converged on for Nessai.…”

Section: B Comparison To Dynestymentioning

confidence: 99%

“…Gravitational-wave astronomy has contributed to our understanding of physics and astrophysics from the atomic scale up to the scale of the Universe [1][2][3]. This is set to continue as the detectors of the LIGO-Virgo-Kagra (LVK) Collaboration [4][5][6] continue to improve in sensitivity [7] and new detectors come online [8] increasing the potential of detecting previously unseen types of sources.…”

Section: Introductionmentioning

confidence: 99%

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Nested sampling with normalizing flows for gravitational-wave inference

2021

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We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our sampler Nessai. Nessai is designed for problems where computing the likelihood is computationally expensive and therefore the cost of training a normalising flow is o↵set by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences whilst requiring 2.07 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelised in Nessai without any modifications to the algorithm. Finally, we outline diagnostics included in Nessai and how these can be used to tune the sampler's settings.

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Section: B Comparison To Dynestymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nested sampling with normalizing flows for gravitational-wave inference

2021

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“…Another approach to test the BD theory in the strong-field regime is to search for gravitational radiation from isolated rotating neutron stars [23]. One can search for scalar, vector or tensor polarizations in LIGO, Virgo, and KAGRA gravitational detector data [24][25][26]. In [27], the first search has been carried out in LIGO detector data from its first observational run for around 200 known pulsars without relying on any particular alternative theory of gravity.…”

Section: Search For Nontensorial Gravitational Wavesmentioning

confidence: 99%

Probing Gravitational Waves from Pulsars in Brans–Dicke Theory

Verma

2021

Universe

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This paper comprises the theoretical background for the data analysis of gravitational waves (GWs) from spinning neutron stars in Brans–Dicke (BD) theory. Einstein’s general theory of relativity (GR) predicts only two tensor polarization states, but a generic metric theory of gravity can also possess scalar and vector polarization states. The BD theory attempts to modify the GR by varying gravitational constant G, and it has three polarization states. The first two states are the same as in GR, and the third one is scalar polarization. We derive the response of a laser interferometric detector to the GW signal from a spinning neutron star in BD theory. We obtain a statistic based on the maximum likelihood principle to identify the signal in BD theory in the detector’s noise. This statistic generalizes the well known F-statistic used in the case of GR. We perform Monte Carlo simulations in Gaussian noise to test the detectability of the signal and the accuracy of estimation of its parameters.

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“…Although the observation with LISA and B-DECIGO alone would provide valuable information on the inspiral GW signal from the GW190521-like BBH system, the true potential of having the low-frequency sensitivity will be revealed only when it is combined with the high-frequency sensitivity in the hecto-Hz band. As illustrated in Figure 1, the lateinspiral and merger-ringdown GW signals of the GW190521-like BBH are best detected with aLIGO, Virgo, and KAGRA [18]. In addition to ground-based GW observatories that are online, the next generation (3G) of ground-based detectors such as the Einstein Telescope (ET) [19,20] (see also Ref.…”

Section: Parameter Symbolmentioning

confidence: 99%

Scope Out Multiband Gravitational-Wave Observations of GW190521-Like Binary Black Holes with Space Gravitational Wave Antenna B-DECIGO

et al. 2021

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The gravitational wave event, GW190521, is the most massive binary black hole merger observed by ground-based gravitational wave observatories LIGO/Virgo to date. While the observed gravitational wave signal is mainly in the merger and ringdown phases, the inspiral gravitational wave signal of the GW190521-like binary will be more visible to space-based detectors in the low-frequency band. In addition, the ringdown gravitational wave signal will be louder in the next generation (3G) of ground-based detectors in the high-frequency band, displaying the great potential of multiband gravitational wave observations. In this paper, we explore the scientific potential of multiband observations of GW190521-like binaries with a milli-Hz gravitational wave observatory: LISA; a deci-Hz observatory: B-DECIGO; and (next generation of) hecto-Hz observatories: aLIGO and ET. In the case of quasicircular evolution, the triple-band observations of LISA, B-DECIGO, and ET will provide parameter estimation errors of the masses and spin amplitudes of component black holes at the level of order of 1–10%. This would allow consistency tests of general relativity in the strong field at an unparalleled precision, particularly with the “B-DECIGO + ET” observation. In the case of eccentric evolution, the multiband signal-to-noise ratio found in “B-DECIGO + ET” observation would be larger than 100 for a five-year observation prior to coalescence, even with high final eccentricities.

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Overview of KAGRA: Detector design and construction history

Cited by 359 publications

References 54 publications

Nested sampling with normalizing flows for gravitational-wave inference

Nested sampling with normalizing flows for gravitational-wave inference

Probing Gravitational Waves from Pulsars in Brans–Dicke Theory

Scope Out Multiband Gravitational-Wave Observations of GW190521-Like Binary Black Holes with Space Gravitational Wave Antenna B-DECIGO

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