Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSource-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
139
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e., without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other autoencoder architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent autoencoder outperforms other autoencoders based on different architectures. The class of recurrent autoencoders presented in this paper could complement the search strategy employed for gravitational wave detection and extend the reach of the ongoing detection campaigns.
rate research
Read More
General Relativity predicts only two tensor polarization modes for gravitational waves while at most six possible polarization modes of gravitational waves are allowed in the general metric theory of gravity. The number of polarization modes is totally determined by the specific modified theory of gravity. Therefore, the determination of polarization modes can be used to test gravitational theory. We introduce a concrete data analysis pipeline for a single space-based detector such as LISA to detect the polarization modes of gravitational waves. Apart from being able to detect mixtures of tensor and extra polarization modes, this method also has the added advantage that no waveform model is needed and monochromatic gravitational waves emitted from any compact binary system with known sky position and frequency can be used. We apply the data analysis pipeline to the reference source J0806.3+1527 of TianQin with 90-days simulation data, and we show that $alpha$ viewed as an indicative of the intrinsic strength of the extra polarization component relative to the tensor modes can be limited below 0.5 for LISA and below 0.2 for Taiji. We investigate the possibility to detect the nontensorial polarization modes with the combined network of LISA, TianQin and Taiji and find that $alpha$ can be limited below 0.2.
Several km-scale gravitational-wave detectors have been constructed world wide. These instruments combine a number of advanced technologies to push the limits of precision length measurement. The core devices are laser interferometers of a new kind; developed from the classical Michelson topology these interferometers integrate additional optical elements, which significantly change the properties of the optical system. Much of the design and analysis of these laser interferometers can be performed using well-known classical optical techniques; however, the complex optical layouts provide a new challenge. In this review we give a textbook-style introduction to the optical science required for the understanding of modern gravitational wave detectors, as well as other high-precision laser interferometers. In addition, we provide a number of examples for a freely available interferometer simulation software and encourage the reader to use these examples to gain hands-on experience with the discussed optical methods.
Gravitational wave (GW) detections have enriched our understanding of the universe. To date, all single-source GW events were found by interferometer-type detectors. We study a detection method using astrometric solutions from photometric surveys and demonstrate that it offers a highly flexible frequency range, uniquely complementing existing detection methods. From repeated point-source astrometric measurements, we may extract GW-induced deflections and infer wave parameters. This method can be applied to any photometric surveys measuring relative astrometry. We show that high-cadence observations of the galactic bulge, such as offered by the Roman Space Telescopes Exoplanet MicroLensing (EML) survey, can be a potent GW probe with complementary frequency range to Gaia, pulsar timing arrays (PTAs), and the Laser Interferometer Space Antenna (LISA). We calculate that the Roman EML survey is sensitive to GWs with frequencies ranging from $7.7times10^{-8}$Hz to $5.6times10^{-4}$Hz, which opens up a unique GW observing window for supermassive black hole binaries and their waveform evolution. While the detection threshold assuming the currently expected performance proves too high for detecting individual GWs in light of the expected supermassive black hole binary population distribution, we show that binaries with chirp mass $M_c>10^{8.3}~M_odot$ out to 100 Mpc can be detected if the telescope is able to achieve an astrometric accuracy of 0.11 mas. To confidently detect binaries with $M_c>10^{7}~M_odot$ out to 50 Mpc, a factor of 100 sensitivity improvement is required. We propose several improvement strategies, including recovering the mean astrometric deflection and increasing astrometric accuracy, number of observed stars, field-of-view size, and observational cadence. We discuss how other existing and planned photometric surveys could contribute to detecting GWs via astrometry.
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire months worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing, and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery.
The future space-based gravitational wave observatory LISA will consist of a constellation of three spacecraft in a triangular constellation, connected by laser interferometers with 2.5 million-kilometer arms. Among other challenges, the success of the mission strongly depends on the quality of the cancellation of laser frequency noise, whose power lies eight orders of magnitude above the gravitational signal. The standard technique to perform noise removal is time-delay interferometry (TDI). TDI constructs linear combinations of delayed phasemeter measurements tailored to cancel laser noise terms. Previous work has demonstrated the relationship between TDI and principal component analysis (PCA). We build on this idea to develop an extension of TDI based on a model likelihood that directly depends on the phasemeter measurements. Assuming stationary Gaussian noise, we decompose the measurement covariance using PCA in the frequency domain. We obtain a comprehensive and compact framework that we call PCI for principal component interferometry, and show that it provides an optimal description of the LISA data analysis problem.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
