Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
This addresses the challenge of efficient experimental design optimization for scientific applications, though it is incremental as it extends neural Bayes ratio estimation.
The paper tackles the problem of optimizing experimental design for frontier experiments by introducing Conditional Neural Bayes Ratio Estimation (cNBRE), which estimates Bayes factors across a continuous design space, and demonstrates a ~20 percentage point variation in detection probability with antenna orientation in 21-cm radio cosmology simulations.
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.