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.
Holographic Ricci dark energy: Interacting model and cosmological constraints
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abstract
We extend the holographic Ricci dark energy model to include some direct, non-gravitational interaction between dark energy and dark matter. We consider three phenomenological forms for the interaction term $Q$ in the model, namely, $Q$ is taken proportional to the Hubble expansion rate and the energy densities of dark sectors (taken to be $\rho_{\rm de}$, $\rho_{\rm m}$, and $\rho_{\rm de}+\rho_{\rm m}$, respectively). We obtain a uniform analytical solution to the three interacting models. Furthermore, we constrain the models by using the latest observational data, including the 557 Union2 type Ia supernovae data, the cosmic microwave background anisotropy data from the 7-yr WMAP, and the baryon acoustic oscillation data from the SDSS. We show that in the interacting models of the holographic Ricci dark energy, a more reasonable value of $\Omega_{\rm m0}$ will be obtained, and the observations favor a rather strong coupling between dark energy and dark matter.
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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.