Deep learning forecasts of cosmic acceleration parameters from DECi-hertz Interferometer Gravitational-wave Observatory
Meng-Fei Sun, Jin Li, Shuo Cao, Xiaolin Liu

TL;DR
This paper demonstrates that deep learning models applied to gravitational wave data from neutron star binaries can significantly improve constraints on the universe's acceleration parameter, aiding in understanding cosmic expansion.
Contribution
It introduces a novel application of CNN, LSTM, and GRU models to constrain cosmic acceleration parameters using simulated DECIGO gravitational wave data.
Findings
CNN limits relative error to 14.09%
LSTM combined with GRU limits relative error to 13.53%
Fisher information matrix limits relative error to 32.94%
Abstract
Validating the accelerating expansion of the universe is an important issue for understanding the evolution of the universe. By constraining the cosmic acceleration parameter , we can discriminate between the (cosmological constant plus cold dark matter) model and LTB (the Lema\^itre-Tolman-Bondi) model. In this paper, we explore the possibility of constraining the cosmic acceleration parameter with the inspiral gravitational waveform of neutron star binaries (NSBs) in the frequency range of 0.1Hz-10Hz, which can be detected by the second-generation space-based gravitational wave detector DECIGO. We use a convolutional neural network (CNN), a long short-term memory (LSTM) network combined with a gated recurrent unit (GRU), and Fisher information matrix to derive constraints on the cosmic acceleration parameter . Based on the simulated gravitational wave…
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Taxonomy
TopicsGeophysics and Gravity Measurements · Pulsars and Gravitational Waves Research · Solar and Space Plasma Dynamics
