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Register a new userDiscovering features in gravitational-wave data through detector characterization, citizen science and machine learning
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The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that a new glitch class linked to ground motion at the detector sites is especially prevalent, and identify two subclasses of this linked to different types of ground motion. Reclassification of data based on the updated model finds that 27 % of all transient noise at LIGO Livingston belongs to the new glitch class, making it the most frequent source of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets.
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(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGOs first observing run.
The recent detections of gravitational waves (GWs) reported by LIGO/Virgo collaborations have made significant impact on physics and astronomy. A global network of GW detectors will play a key role to solve the unknown nature of the sources in coordinated observations with astronomical telescopes and detectors. Here we introduce KAGRA (former name LCGT; Large-scale Cryogenic Gravitational wave Telescope), a new GW detector with two 3-km baseline arms arranged in the shape of an L, located inside the Mt. Ikenoyama, Kamioka, Gifu, Japan. KAGRAs design is similar to those of the second generations such as Advanced LIGO/Virgo, but it will be operating at the cryogenic temperature with sapphire mirrors. This low temperature feature is advantageous for improving the sensitivity around 100 Hz and is considered as an important feature for the third generation GW detector concept (e.g. Einstein Telescope of Europe or Cosmic Explorer of USA). Hence, KAGRA is often called as a 2.5 generation GW detector based on laser interferometry. The installation and commissioning of KAGRA is underway and its cryogenic systems have been successfully tested in May, 2018. KAGRAs first observation run is scheduled in late 2019, aiming to join the third observation run (O3) of the advanced LIGO/Virgo network. In this work, we describe a brief history of KAGRA and highlights of main feature. We also discuss the prospects of GW observation with KAGRA in the era of O3. When operating along with the existing GW detectors, KAGRA will be helpful to locate a GW source more accurately and to determine the source parameters with higher precision, providing information for follow-up observations of a GW trigger candidate.
KAGRA is a new gravitational wave detector which aims to begin joint observation with Advanced LIGO and Advanced Virgo from late 2019. Here, we present KAGRAs possible upgrade plans to improve the sensitivity in the decade ahead. Unlike other state-of-the-art detectors, KAGRA requires different investigations for the upgrade since it is the only detector which employs cryogenic cooling of the test mass mirrors. In this paper, investigations on the upgrade plans which can be realized by changing the input laser power, increasing the mirror mass, and injecting frequency dependent squeezed vacuum are presented. We show how each upgrade affects to the detector frequency bands and also discuss impacts on gravitational-wave science. We then propose an effective progression of upgrades based on technical feasibility and scientific scenarios.
Cryogenic cooling of the test masses of interferometric gravitational wave detectors is a promising way to reduce thermal noise. However, cryogenic cooling limits the incident power to the test masses, which limits the freedom of shaping the quantum noise. Cryogenic cooling also requires short and thick suspension fibers to extract heat, which could result in the worsening of thermal noise. Therefore, careful tuning of multiple parameters is necessary in designing the sensitivity of cryogenic gravitational wave detectors. Here, we propose the use of particle swarm optimization to optimize the parameters of these detectors. We apply it for designing the sensitivity of the KAGRA detector, and show that binary neutron star inspiral range can be improved by 10%, just by retuning seven parameters of existing components. We also show that the sky localization of GW170817-like binaries can be further improved by a factor of 1.6 averaged across the sky. Our results show that particle swarm optimization is useful for designing future gravitational wave detectors with higher dimensionality in the parameter space.
Upgrades to improve the sensitivity of gravitational wave detectors enable more frequent detections and more precise source parameter estimation. Unlike other advanced interferometric detectors such as Advanced LIGO and Advanced Virgo, KAGRA requires different approach for the upgrade since it is the only detector which employs cryogenic cooling of the test masses. In this paper, we describe possible KAGRA upgrades with technologies focusing on different detector bands, and compare the impacts on the detection of compact binary coalescences. We show that either fivefold improvement in the $100 M_{odot}$--$100 M_{odot}$ binary black hole range, a factor of 1.3 improvement in the binary neutron star range, or a factor of 1.7 improvement in the sky localization of binary neutron stars is well feasible with upgrades that do not require changes in the existing cryogenic or vacuum infrastructure. We also show that twofold broadband sensitivity improvement is possible by applying multiple upgrades to the detector.
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