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.
Exploring neutrino mass and mass hierarchy in the scenario of vacuum energy interacting with cold dark matter
5 Pith papers cite this work. Polarity classification is still indexing.
abstract
We investigate the constraints on total neutrino mass in the scenario of vacuum energy interacting with cold dark matter. We focus on two typical interaction forms, i.e., $Q=\beta H\rho_{\rm c}$ and $Q=\beta H\rho_{\Lambda}$. To avoid the occurrence of large-scale instability in interacting dark energy cosmology, we adopt the parameterized post-Friedmann approach to calculate the perturbation evolution of dark energy. We employ observational data, including the Planck cosmic microwave background temperature and polarization data, baryon acoustic oscillation data, a JLA sample of type Ia supernovae observation, direct measurement of the Hubble constant, and redshift space distortion data. We find that, compared with those in the $\Lambda$CDM model, much looser constraints on $\sum m_{\nu}$ are obtained in the $Q=\beta H\rho_{\rm c}$ model, whereas slightly tighter constraints are obtained in the $Q=\beta H\rho_{\Lambda}$ model. Consideration of the possible mass hierarchies of neutrinos reveals that the smallest upper limit of $\sum m_{\nu}$ appears in the degenerate hierarchy case. By comparing the values of $\chi^2_{\rm min}$, we find that the normal hierarchy case is favored over the inverted one. In particular, we find that the difference $\Delta \chi^2_{\rm min} \equiv \chi^2_{\rm IH; min}-\chi^2_{\rm NH; min}> 2$ in the $Q=\beta H\rho_{\rm c}$ model. In addition, we find that $\beta=0$ is consistent with the current observations in the $Q=\beta H\rho_{\rm c}$ model, and $\beta < 0$ is favored at more than the $1\sigma$ level in the $Q=\beta H\rho_{\Lambda}$ model.
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New ACT and DESI data yield model-dependent upper limits on sum of neutrino masses, with holographic dark energy giving the tightest bounds and a consistent preference for degenerate hierarchy.
Upper bounds on total neutrino mass in four phenomenological interacting dark energy models are derived from DESI DR2 BAO plus CMB and SNIa data, showing strong dependence on the interaction term form and statistical preference for models that tighten the bound below the oscillation lower limit.
citing papers explorer
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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.
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HIcosmo: a differentiable JAX-based framework for cosmology inference
HIcosmo is a new JAX-based differentiable framework for background cosmology inference that matches Cobaya results while delivering 8.7x CPU and up to 20x GPU speedups.
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Model-independent H0 from GWTC-4 standard sirens and TDCOSMO 2025 strong lensing time delays
Combining GWTC-4 standard sirens with TDCOSMO2025 lensing data under the distance sum rule yields H0 = 83.78 +12.53/-10.23 km/s/Mpc (13.6% precision) in one configuration, consistent with both Planck and SH0ES.
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Measuring neutrino mass in light of ACT DR6 and DESI DR2
New ACT and DESI data yield model-dependent upper limits on sum of neutrino masses, with holographic dark energy giving the tightest bounds and a consistent preference for degenerate hierarchy.
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Neutrino mass constraints in interacting dark energy models after DESI DR2
Upper bounds on total neutrino mass in four phenomenological interacting dark energy models are derived from DESI DR2 BAO plus CMB and SNIa data, showing strong dependence on the interaction term form and statistical preference for models that tighten the bound below the oscillation lower limit.