Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915895311335424 |
|---|---|
| author | Leeney, S. A. K. Gessey-Jones, T. Handley, W. J. Acedo, E. de Lera Bevins, H. T. J. Tutt, J. L. |
| author_facet | Leeney, S. A. K. Gessey-Jones, T. Handley, W. J. Acedo, E. de Lera Bevins, H. T. J. Tutt, J. L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26489 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation Leeney, S. A. K. Gessey-Jones, T. Handley, W. J. Acedo, E. de Lera Bevins, H. T. J. Tutt, J. L. Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine Learning 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. |
| title | Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2603.26489 |