A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
New agegraphic dark energy as a rolling tachyon
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abstract
Combining the general relativity and the uncertainty relation in quantum mechanics, the energy density of quantum fluctuations of space-time can be viewed as dark energy. The so-called agegraphic dark energy model is just based on this viewpoint, in which the age of the universe is introduced as the length measure. Recently, the new agegraphic dark energy model was proposed, where the dynamical dark energy is measured by the conformal age of the universe. On the other hand, scalar-field dark energy models like tachyon are often regarded as an effective description of some underlying theory of dark energy. In this paper, we show that the new agegraphic dark energy can be described completely by a tachyon scalar-field. We thus reconstruct the potential and the dynamics of the tachyon scalar-field, according to the evolution of the new agegraphic dark energy.
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Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.