Výsledky vyhledávání - "Edwards, A. C."

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A novel stacked hybrid autoencoder for imputing LISA data gaps

Autor: Mao, Ruiting, Lee, Jeong Eun, Edwards, Matthew C.

The Laser Interferometer Space Antenna (LISA) data stream will contain gaps with missing or unusable data due to antenna repointing, orbital corrections, instrument malfunctions, and unknown random processes. We introduce a new deep learning model to

Externí odkaz: http://arxiv.org/abs/2410.05571

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Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification

Autor: Abylkairov, Y. Sultan, Edwards, Matthew C., Orel, Daniil, Mitra, Ayan, Shukirgaliyev, Bekdaulet, Abdikamalov, Ernazar

We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating GW signals from simulations with various EOSs, we train machine learnin

Externí odkaz: http://arxiv.org/abs/2409.14508

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A generative adversarial network for stellar core-collapse gravitational-waves

Autor: Eccleston, Tarin, Edwards, Matthew C.

We present a rapid stellar core-collapse waveform emulator built using a deep convolutional generative adversarial network (DCGAN). The DCGAN was trained on the Richers \textit{et al.~}\cite{richers:2017} waveform catalogue to learn the structure of

Externí odkaz: http://arxiv.org/abs/2408.02895

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Calibrating approximate Bayesian credible intervals of gravitational-wave parameters

Autor: Mao, Ruiting, Lee, Jeong Eun, Burke, Ollie, Chua, Alvin J. K., Edwards, Matthew C., Meyer, Renate

Publikováno v: Phys. Rev. D 109, 083002 (2024)

Approximations are commonly employed in realistic applications of scientific Bayesian inference, often due to convenience if not necessity. In the field of gravitational-wave (GW) data analysis, fast-to-evaluate but approximate waveform models of ast

Externí odkaz: http://arxiv.org/abs/2310.06321

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