A conditional variational autoencoder is trained on public kilonova light curves to enable rapid parameter inference for binary neutron star merger models in under three hours total.
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Bayesian statistics supplies an automatic Occam's razor that penalizes unnatural models needing precise fine-tuning to agree with data, justifying naturalness arguments without aleatoric uncertainty.
citing papers explorer
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Precise and Rapid Parameter Inference of Kilonova with Conditional Variational Autoencoder
A conditional variational autoencoder is trained on public kilonova light curves to enable rapid parameter inference for binary neutron star merger models in under three hours total.
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It's all in your head -- fine-tuning arguments do not require aleatoric uncertainty
Bayesian statistics supplies an automatic Occam's razor that penalizes unnatural models needing precise fine-tuning to agree with data, justifying naturalness arguments without aleatoric uncertainty.