发表机构
Université Paris-Saclay; CNRS, Institut d’Astrophysique Spatiale; Centre national d’études spatiales (CNES)（巴黎萨克雷大学; 法国国家科学研究中心，天体物理研究所; 法国国家空间研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于机器学习的优化方法，用于探索天文仪器多维参数空间，并应用于FOSSIL任务的CMB谱畸变测量，识别关键参数以提升实验性能。
AI 中文摘要
理解仪器和任务参数如何影响测量微弱天体物理信号的能力，是未来实验设计中的关键挑战。这对于CMB谱畸变尤其重要，其微弱信号同时受到仪器效应和天体物理前景的影响。我们开发了一种方法来探索和优化天文仪器的多维参数空间，并将其应用于使用FOSSIL任务概念的CMB谱畸变测量。我们将专门的天空模型与真实的仪器模型相结合，并使用Fisher预测来评估模拟仪器的测量能力。然后使用基于决策树的回归模型来学习仪器参数与天空可观测量的预测信噪比之间的非线性映射。随机森林和梯度提升模型准确再现了预测的测量性能。使用SHAP值来解释仪器参数对测量的影响。发现最暖仪器组件的温度是所有三个可观测量的主导参数，突出了限制内部发射和在低温下运行仪器的重要性。频率覆盖也极具影响力，揭示了光谱覆盖与仪器灵敏度之间的权衡。所提出的优化方法提供了一种快速且可解释的方法来探索高维仪器参数空间，并识别对实验科学性能影响最大的参数。据我们所知，这项工作代表了机器学习方法在天文仪器全局优化中的首次应用，并且可以轻松扩展到其他天文仪器和任务概念。
英文摘要
Understanding how instrumental and mission parameters affect the ability to measure faint astrophysical signals is a key challenge in the design of future experiments. This is particularly relevant for CMB spectral distortions, whose weak signals are affected by both instrumental effects and astrophysical foregrounds. We develop a method to explore and optimise the multidimensional parameter space of an astronomical instrument, and apply it to CMB spectral distortion measurements using the FOSSIL mission concept. We combine a dedicated sky model with a realistic instrument model and use Fisher forecasts to assess measurement capabilities of the simulated instrument. Decision-tree-based regression models are then used to learn the non-linear mapping between instrumental parameters and the predicted signal-to-noise ratios of the sky observables. Random Forest and gradient boosting models accurately reproduce the forecasted measurement performance. SHAP values are used to interpret the impact of instrumental parameters on the measurement. The temperature of the warmest instrumental component is found to be the dominant parameter for all three observables, highlighting the importance of limiting internal emission and operating the instrument at cryogenic temperatures. Frequency coverage is also highly influential, revealing a trade-off between spectral coverage and instrumental sensitivity. The proposed optimisation method provides a fast and interpretable approach to explore high-dimensional instrumental parameter spaces and identify parameters that most strongly influence the scientific performance of an experiment. To the best of our knowledge, this work represents the first application of machine-learning methods to the global optimisation of an astronomical instrument and can readily be extended to other astronomical instruments and mission concepts.
Comments16 pages, 11 figures
