A neural-network-based simulation inference method improves 3σ detection probability of gravitational-wave background anisotropies by 90-200% over Gaussian frequentist searches by learning non-Gaussian structure in pulsar timing residuals.
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Physics equation corpora exhibit exponential decay in mathematical operator frequencies, proposed as a meta-law that narrows the space of plausible expressions for symbolic regression.
Bayesian posteriors from JETSCAPE jet-quenching model are largely compatible across centrality but exhibit shifts across beam energy and observable class, with varying ability to predict complementary datasets.
citing papers explorer
-
Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference
A neural-network-based simulation inference method improves 3σ detection probability of gravitational-wave background anisotropies by 90-200% over Gaussian frequentist searches by learning non-Gaussian structure in pulsar timing residuals.
-
Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature
Physics equation corpora exhibit exponential decay in mathematical operator frequencies, proposed as a meta-law that narrows the space of plausible expressions for symbolic regression.
-
Bayesian inference constraints on jet quenching across centrality, beam energy, and observable classes in LHC heavy-ion collisions
Bayesian posteriors from JETSCAPE jet-quenching model are largely compatible across centrality but exhibit shifts across beam energy and observable class, with varying ability to predict complementary datasets.