用神经网络统一评估不同实验设计的发现概率，提升科研效率。
Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
- 用条件神经贝叶斯比估计法，一次训练覆盖连续设计空间
- 模拟显示天线朝向改变可使探测概率相差约20个百分点
- 适合需要高效优化实验设计的前沿科学领域
针对处于探测极限的前沿实验，仪器设计直接决定发现概率。我们提出条件神经贝叶斯比估计（cNBRE），通过在设计参数上施加条件，使单个训练好的网络能够估计连续设计空间中的贝叶斯因子。应用于代表REACH实验的21厘米射电宇宙学模拟中，cNBRE的摊销特性实现了传统逐点方法无法处理的系统性设计空间探索，同时恢复了已知物理关系。分析表明，单晚观测中天线朝向变化可导致探测概率波动约20个百分点，这一设计决策若在建造前确定则极为简便。该框架为多种科学应用提供了高效、全局感知的实验设计优化能力。
原文摘要 · Abstract (English)
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.
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