Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
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Abstract (arXiv)
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 point variation in detection probability with antenna orientation for a single night of observation, a design decision that would be trivial to implement if determined prior to antenna construction. This framework enables efficient, globally-informed experimental design optimisation for a wide range of scientific applications.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 66 references that are in the map. First posted on arXiv 27 Mar 2026.
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19 of 66 references.
References · 19
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7 more
- 2020Simulation-based inference methods for particle physicscited by 11
- 2023Learning Likelihood Ratios with Neural Network Classifierscited by 11
- 2023Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparisoncited by 10
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Citing papers per year: 2026: 1.
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