FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning
FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning
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FlopPITy：通过机器学习实现自洽的系外行星大气检索
DOI：
10.1051/0004-6361/202348367
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发表时间：
2024
影响因子：
6.5
通讯作者：
Ardévol Martínez F
中科院分区：
文献类型：
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作者：
Ardévol Martínez F
解释系外行星大气的观测结果以限制物理和化学性质通常是使用贝叶斯检索技术完成的。由于这些方法需要许多模型计算，因此必须在模型的复杂性和运行时间之间进行折衷。实现这一妥协导致许多物理和化学过程的简化（例如参数化的温度结构）。AimsHere，我们实现和测试顺序神经后验估计（SNPE），一种用于系外行星大气反演的机器学习推理算法。我们的目标是加快检索，使他们可以运行更昂贵的计算大气模型，如那些计算温度结构使用辐射transfect.MethodsWe生成100合成观测使用巧妙的建模代码为系外行星科学（ARCiS），这是一个大气模拟代码，具有灵活性，可以计算不同复杂程度的模型，并对它们进行检索，以测试SNPE后验者的忠诚度。忠实度量化了后验是否像我们预期的那样经常包含地面真理。我们还产生了一个合成的观测一个凉爽的褐矮星使用的自洽能力的ARCiS和运行检索与自洽的模型，以展示SNPE.ResultsWe开辟的可能性，SNPE提供了忠实的后验，因此是一个可靠的工具系外行星大气检索。我们能够运行一个自我一致的检索合成棕矮星光谱使用只有50 000前向模型评估。我们发现，SNPE可以加快反演之间的102 ×和≥ 10 ×取决于计算负荷的正演模型，观测的维数，和它的信噪比（S/N）。我们已经在Github上向社区公开了代码。
ContextInterpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Since these methods require many model computations, a compromise must be made between the model’s complexity and its run time. Achieving this compromise leads to a simplification of many physical and chemical processes (e.g. parameterised temperature structure).AimsHere, we implement and test sequential neural posterior estimation (SNPE), a machine learning inference algorithm for atmospheric retrievals for exoplanets. The goal is to speed up retrievals so they can be run with more computationally expensive atmospheric models, such as those computing the temperature structure using radiative transfer.MethodsWe generated 100 synthetic observations using ARtful Modeling Code for exoplanet Science (ARCiS), which is an atmospheric modelling code with the flexibility to compute models across varying degrees of complexity and to perform retrievals on them to test the faithfulness of the SNPE posteriors. The faithfulness quantifies whether the posteriors contain the ground truth as often as we expect. We also generated a synthetic observation of a cool brown dwarf using the self-consistent capabilities of ARCiS and ran a retrieval with self-consistent models to showcase the possibilities opened up by SNPE.ResultsWe find that SNPE provides faithful posteriors and is therefore a reliable tool for exoplanet atmospheric retrievals. We are able to run a self-consistent retrieval of a synthetic brown dwarf spectrum using only 50 000 forward model evaluations. We find that SNPE can speed up retrievals between ∼2× and ≥10× depending on the computational load of the forward model, the dimensionality of the observation, and its signal-to-noise ratio (S/N). We have made the code publicly available for the community on Github.