Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
For frontier experiments operating at the edge of detectability, instrument design directly determines the probability of discovery. We introduce Conditional Neural Bayes Ratio Estimation (cNBRE), which extends neural Bayes ratio estimation by conditioning on design parameters, enabling a single trained network to estimate Bayes factors across a continuous design space. Applied to 21-cm radio cosmology with simulations representative of the REACH experiment, the amortised nature of cNBRE enables systematic design space exploration that would be intractable with traditional point-wise methods, while recovering established physical relationships. The analysis demonstrates a ~20 percentage p...
| Authors | S. A. K. Leeney, T. Gessey-Jones, W. J. Handley et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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