Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric
parameters from observed light spectra, typically by framing the task as a
Bayesian inference problem. However, traditional approaches such as nested
sampling are computationally expensive, thus sparking an interest in solutions
based on machine learning (ML). In this ongoing work, we first explore flow
matching posterior estimation (FMPE) as a new ML-based method for AR and find
that, in our case, it is more accurate than neural posterior estimation (NPE),
but less accurate than nested sampling. We then combine both FMPE and NPE with
importance sampling, in which case both methods outperform nested sampling in
terms of accuracy and simulation efficiency. Going forward, our analysis
suggests that simulation-based inference with likelihood-based importance
sampling provides a framework for accurate and efficient AR that may become a
valuable tool not only for the analysis of observational data from existing
telescopes, but also for the development of new missions and instruments.

大气检索通过估计观测光谱中的大气参数来表征外行星，通常将其作为贝叶斯推断问题来解决。我们探索了基于流匹配后验估计（FMPE）的机器学习方法用于大气检索，并发现在这种情况下，它比神经后验估计（NPE）更准确，但比嵌套采样方法的准确性稍低。我们将 FMPE 和 NPE 与重要性采样相结合，两种方法在准确性和模拟效率方面均优于嵌套采样。进一步分析表明，基于仿真和基于似然的重要性采样提供了一个准确高效的大气检索框架，不仅可以用于分析现有望远镜的观测数据，还可以用于新任务和仪器的开发。

利用流匹配和神经重要性抽样推断系外行星的大气特性

Inferring Atmospheric Properties of Exoplanets with Flow Matching and  Neural Importance Sampling

Atmospheric retrievals (AR) of exoplanets typically rely on a combination of
a Bayesian inference technique and a forward simulator to estimate atmospheric
properties from an observed spectrum. A key component in simulating spectra is
the pressure-temperature (PT) profile, which describes the thermal structure of
the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here
that limit the retrieved PT profiles to simple approximations, but still use a
relatively large number of parameters. In this work, we introduce a
conceptually new, data-driven parameterization scheme for physically consistent
PT profiles that does not require explicit assumptions about the functional
form of the PT profiles and uses fewer parameters than existing methods. Our
approach consists of a latent variable model (based on a neural network) that
learns a distribution over functions (PT profiles). Each profile is represented
by a low-dimensional vector that can be used to condition a decoder network
that maps $P$ to $T$. When training and evaluating our method on two publicly
available datasets of self-consistent PT profiles, we find that our method
achieves, on average, better fit quality than existing baseline methods,
despite using fewer parameters. In an AR based on existing literature, our
model (using two parameters) produces a tighter, more accurate posterior for
the PT profile than the five-parameter polynomial baseline, while also speeding
up the retrieval by more than a factor of three. By providing parametric access
to physically consistent PT profiles, and by reducing the number of parameters
required to describe a PT profile (thereby reducing computational cost or
freeing resources for additional parameters of interest), our method can help
improve AR and thus our understanding of exoplanet atmospheres and their
habitability.

利用少量参数的基于神经网络的潜变量模型的参数化方案，实现了对大气检测和外行星大气特征的更准确和更高效的估计。