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Universe
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Newest Results in Gravitational Waves and Machine Learning
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Newest Results in Gravitational Waves and Machine Learning

A special issue of Universe (ISSN 2218-1997). This special issue belongs to the section "Gravitation".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4749

Special Issue Editors

Prof. Dr. Zhoujian Cao

E-Mail Website
Guest Editor
Department of Astronomy, Beijing Normal University, Beijing, China
Interests: gravitational waves; LIGO (observatory); compact
Prof. Dr. Lijing Shao

E-Mail Website
Guest Editor
Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100080, China
Interests: tests of gravity theories; gravitational waves; pulsars and neutron stars; astrophysical studies of dark matter; black hole spacetime; precision tests of fundamental physics; Bayesian data analysis and statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhixiang Ren

E-Mail Website
Guest Editor
Peng Cheng Laboratory, Shenzhen, China
Interests: machine learning and gravitational wave

Special Issue Information

Dear Colleagues,

Since the first detection of gravitational waves in 2015, gravitational wave astronomy has developed extremely fast. At present, artificial intelligence, especially machine learning, is becoming a very powerful tool with which to treat hard problems in many different fields. In recent years there have been many research works applying artificial intelligence techniques to gravitational wave studies, including those on hardware design, gravitational wave signal search, source parameter estimation, and others. We can expect that artificial intelligence will soon help scientists perform gravitational wave data analyses much faster than if they were using traditional methods and even discover exciting new signals that will promote research on fundamental physics and astronomy.

This Special Issue will gather a collection of articles on artificial intelligence technique development for gravitational waves, algorithm design and pipeline software development for gravitational wave data analyses, the newest results on gravitational wave science with and without artificial intelligence methods, and others.

Contributions are welcome on theories, simulations, and data. The selection criteria consider the formal and technical soundness, experimental support, and relevance of the contribution.

Prof. Dr. Zhoujian Cao
Prof. Dr. Lijing Shao
Dr. Zhixiang Ren
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Universe is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • gravitational wave
  • artificial intelligence
  • machine learning
  • coalescence of binary compact objects
  • supernova
  • glitch classification
  • Einstein equation
  • numerical relativity
  • quantum gravity

Published Papers (4 papers)

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Research

20 pages, 851 KiB  
Article
Search for Extreme Mass Ratio Inspirals Using Particle Swarm Optimization and Reduced Dimensionality Likelihoods
by Xiao-Bo Zou, Soumya D. Mohanty, Hong-Gang Luo and Yu-Xiao Liu
Universe 2024, 10(4), 171; https://doi.org/10.3390/universe10040171 - 06 Apr 2024
Viewed by 586
Abstract
Extreme-mass-ratio inspirals (EMRIs) are significant observational targets for spaceborne gravitational wave detectors, namely, LISA, Taiji, and Tianqin, which involve the inspiral of stellar-mass compact objects into massive black holes (MBHs) with a mass range of approximately 104∼107M⊙ [...] Read more.
Extreme-mass-ratio inspirals (EMRIs) are significant observational targets for spaceborne gravitational wave detectors, namely, LISA, Taiji, and Tianqin, which involve the inspiral of stellar-mass compact objects into massive black holes (MBHs) with a mass range of approximately 104∼107M⊙. EMRIs are estimated to produce long-lived gravitational wave signals with more than 105 cycles before plunge, making them an ideal laboratory for exploring the strong-gravity properties of the spacetimes around the MBHs, stellar dynamics in galactic nuclei, and properties of the MBHs itself. However, the complexity of the waveform model, which involves the superposition of multiple harmonics, as well as the high-dimensional and large-volume parameter space, make the fully coherent search challenging. In our previous work, we proposed a 10-dimensional search using Particle Swarm Optimization (PSO) with local maximization over the three initial angles. In this study, we extend the search to an 8-dimensional PSO with local maximization over both the three initial angles and the angles of spin direction of the MBH, where the latter contribute a time-independent amplitude to the waveforms. Additionally, we propose a 7-dimensional PSO search by using a fiducial value for the initial orbital frequency and shifting the corresponding 8-dimensional Time Delay Interferometry responses until a certain lag returns the corresponding 8-dimensional log-likelihood ratio’s maximum. The reduced dimensionality likelihoods enable us to successfully search for EMRI signals with a duration of 0.5 years and signal-to-noise ratio of 50 within a wider search range than our previous study. However, the ranges used by both the LISA Data Challenge (LDC) and Mock LISA Data Challenge (MLDC) to generate their simulated signals are still wider than the those we currently employ in our direct searches. Consequently, we discuss further developments, such as using a hierarchical search to narrow down the search ranges of certain parameters and applying Graphics Processing Units to speed up the code. These advances aim to improve the efficiency, accuracy, and generality of the EMRI search algorithm. Full article
(This article belongs to the Special Issue Newest Results in Gravitational Waves and Machine Learning)
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20 pages, 714 KiB  
Article
Swarm Intelligence Methods for Extreme Mass Ratio Inspiral Search: First Application of Particle Swarm Optimization
by Xiao-Bo Zou, Soumya D. Mohanty, Hong-Gang Luo and Yu-Xiao Liu
Universe 2024, 10(2), 96; https://doi.org/10.3390/universe10020096 - 17 Feb 2024
Viewed by 1340
Abstract
Swarm intelligence (SI) methods are nature-inspired metaheuristics for global optimization that exploit a coordinated stochastic search strategy by a group of agents. Particle swarm optimization (PSO) is an established SI method that has been applied successfully to the optimization of rugged high-dimensional likelihood [...] Read more.
Swarm intelligence (SI) methods are nature-inspired metaheuristics for global optimization that exploit a coordinated stochastic search strategy by a group of agents. Particle swarm optimization (PSO) is an established SI method that has been applied successfully to the optimization of rugged high-dimensional likelihood functions, a problem that represents the main bottleneck across a variety of gravitational wave (GW) data analysis challenges. We present results from the first application of PSO to one of the most difficult of these challenges, namely the search for the Extreme Mass Ratio Inspiral (EMRI) in data from future spaceborne GW detectors such as LISA, Taiji, or Tianqin. We use the standard Generalized Likelihood Ratio Test formalism, with the minimal use of restrictive approximations, to search 6 months of simulated LISA data and quantify the search depth, signal-to-noise ratio (SNR), and breadth, within the ranges of the EMRI parameters, that PSO can handle. Our results demonstrate that a PSO-based EMRI search is successful for a search region ranging over ≳10σ for the majority of parameters and ≳200σ for one, with σ being the SNR-dependent Cramer–Rao lower bound on the parameter estimation error and 30≤SNR≤50. This is in the vicinity of the search ranges that the current hierarchical schemes can identify. Directions for future improvement, including computational bottlenecks to be overcome, are identified. Full article
(This article belongs to the Special Issue Newest Results in Gravitational Waves and Machine Learning)
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19 pages, 828 KiB  
Article
Kilonova-Targeting Lightcurve Classification for Wide Field Survey Telescope
by Runduo Liang, Zhengyan Liu, Lei Lei and Wen Zhao
Universe 2024, 10(1), 10; https://doi.org/10.3390/universe10010010 - 25 Dec 2023
Viewed by 1168
Abstract
With the enhancement of the sensitivity of gravitational wave (GW) detectors and capabilities of large survey facilities, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and the 2.5 m Wide Field Survey Telescope (WFST), we now have the [...] Read more.
With the enhancement of the sensitivity of gravitational wave (GW) detectors and capabilities of large survey facilities, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and the 2.5 m Wide Field Survey Telescope (WFST), we now have the potential to detect an increasing number of distant kilonova (KN). However, distinguishing KN from the plethora of detected transients in ongoing and future follow-up surveys presents a significant challenge. In this study, our objective is to establish an efficient classification mechanism tailored for the follow-up survey conducted by WFST, with a specific focus on identifying KN associated with GW. We employ a novel temporal convolutional neural network architecture, trained using simulated multi-band photometry lasting for 3 days by WFST, accompanied by contextual information, i.e., luminosity distance information by GW. By comparison of the choices of contextual information, we can reach 95% precision and 94% recall for our best model. It also performs good validation of photometry data on AT2017gfo and AT2019npv. Furthermore, we investigate the ability of the model to distinguish KN in a GW follow-up survey. We conclude that there is over 80% probability that we can capture true KN in 20 selected candidates among ∼250 detected astrophysical transients that have passed the real–bogus filter and cross-matching. Full article
(This article belongs to the Special Issue Newest Results in Gravitational Waves and Machine Learning)
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11 pages, 651 KiB  
Article
Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning
by Wenhong Ruan, He Wang, Chang Liu and Zongkuan Guo
Universe 2023, 9(9), 407; https://doi.org/10.3390/universe9090407 - 06 Sep 2023
Cited by 3 | Viewed by 1048
Abstract
In the 2030s, a new era of gravitational wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji, and TianQin, will open the millihertz window for GW astronomy. These detectors are poised to detect a [...] Read more.
In the 2030s, a new era of gravitational wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji, and TianQin, will open the millihertz window for GW astronomy. These detectors are poised to detect a multitude of GW signals emitted by different sources. It is a challenging task for GW data analysis to recover the parameters of these sources at a low computational cost. Generally, the matched filtering approach entails exploring an extensive parameter space for all resolvable sources, incurring a substantial cost owing to the generation of GW waveform templates. To alleviate the challenge, we make an attempt to perform parameter inference for coalescing massive black hole binaries (MBHBs) using deep learning. The model trained in this work has the capability to produce 50,000 posterior samples for the redshifted total mass, mass ratio, coalescence time, and luminosity distance of an MBHB in about twenty seconds. Our model can serve as an effective data pre-processing tool, reducing the volume of parameter space by more than four orders of magnitude for MBHB signals with a signal-to-noise ratio larger than 100. Moreover, the model exhibits robustness when handling input data that contain multiple MBHB signals. Full article
(This article belongs to the Special Issue Newest Results in Gravitational Waves and Machine Learning)
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