A hierarchical Bayesian approach with multivariate Gaussian population prior reduces bias in demographic inference of SN Ia power-law rise parameters compared to individual fitting.
S., Narayan, G., & Kirshner, R
5 Pith papers cite this work. Polarity classification is still indexing.
representative citing papers
Pantheon+ delivers tighter SN Ia constraints on Ω_M, w0, wa, and H0 from 1550 events, consistent with a cosmological constant, with SN systematics contributing less than one third to H0 uncertainty.
BayeSN analysis of ZTF Type Ia supernovae confirms a ~0.1 mag intrinsic environmental step in standardized brightness that is not explained by differences in dust extinction properties.
GIGA-Lens 2.0 scales strong gravitational lens modeling across up to 128 GPU nodes and demonstrates it on 100 simulated systems plus one real DESI lens.
Bulk flow measurements from Hawai`i Supernova Flows SNe Ia yield speeds of 100-400 km/s consistent with ΛCDM expectations at z ≲ 0.1.
citing papers explorer
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Decoding the Early-Time Light Curves of Type Ia Supernovae. I. A Hierarchical Bayesian Framework for Demographic Inference
A hierarchical Bayesian approach with multivariate Gaussian population prior reduces bias in demographic inference of SN Ia power-law rise parameters compared to individual fitting.