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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Soares-Santoset al.(DES, LIGO Scientific, Virgo), Astrophys
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abstract
We present a multi-messenger measurement of the Hubble constant H_0 using the binary-black-hole merger GW170814 as a standard siren, combined with a photometric redshift catalog from the Dark Energy Survey (DES). The luminosity distance is obtained from the gravitational wave signal detected by the LIGO/Virgo Collaboration (LVC) on 2017 August 14, and the redshift information is provided by the DES Year 3 data. Black-hole mergers such as GW170814 are expected to lack bright electromagnetic emission to uniquely identify their host galaxies and build an object-by-object Hubble diagram. However, they are suitable for a statistical measurement, provided that a galaxy catalog of adequate depth and redshift completion is available. Here we present the first Hubble parameter measurement using a black-hole merger. Our analysis results in $H_0 = 75.2^{+39.5}_{-32.4}~{\rm km~s^{-1}~Mpc^{-1}}$, which is consistent with both SN Ia and CMB measurements of the Hubble constant. The quoted 68% credible region comprises 60% of the uniform prior range [20,140] ${\rm km~s^{-1}~Mpc^{-1}}$, and it depends on the assumed prior range. If we take a broader prior of [10,220] ${\rm km~s^{-1}~Mpc^{-1}}$, we find $H_0 = 78^{+ 96}_{-24}~{\rm km~s^{-1}~Mpc^{-1}}$ ($57\%$ of the prior range). Although a weak constraint on the Hubble constant from a single event is expected using the dark siren method, a multifold increase in the LVC event rate is anticipated in the coming years and combinations of many sirens will lead to improved constraints on $H_0$.
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Using simulated binary black hole mergers and neutral hydrogen maps, the radio sirens method constrains H0 to 8% precision with 3000 high-SNR events, offering a 90% improvement over standard dark siren analyses.
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Angular auto-correlation of gravitational wave sources decreases with lensing dispersion, and joint cross-correlation with galaxies partially breaks the degeneracy with source bias.
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
Proposes primordial black holes from modified small-scale fluctuations and entropic acceleration in expanding spacetime as explanations for dark matter and dark energy.
The Hubble tension between local and early-universe expansion-rate measurements may be resolved by early dark energy that speeds up expansion before recombination while satisfying existing constraints.
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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Radio sirens: inferring $H_0$ with binary black holes and neutral hydrogen in the era of the Einstein Telescope and the SKA Observatory
Using simulated binary black hole mergers and neutral hydrogen maps, the radio sirens method constrains H0 to 8% precision with 3000 high-SNR events, offering a 90% improvement over standard dark siren analyses.
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A unified harmonic framework for dark siren cosmology
The GW-galaxy cross-correlation method, unified with spectral sirens in a harmonic framework, can measure H0 to 1% and Omega_m to 5% precision with 2 years of data from next-generation detectors like Einstein Telescope and Cosmic Explorer.
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Constraining the lensing dispersion from the angular clustering of binary black hole mergers
Angular auto-correlation of gravitational wave sources decreases with lensing dispersion, and joint cross-correlation with galaxies partially breaks the degeneracy with source bias.
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Beyond the Standard Model of Cosmology: Testing new paradigms with a Multiprobe Exploration of the Dark Universe
Proposes primordial black holes from modified small-scale fluctuations and entropic acceleration in expanding spacetime as explanations for dark matter and dark energy.
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The Hubble Tension and Early Dark Energy
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