使用自动微分高斯过程直接检索原行星盘尘埃特性及其在HD 169142盘中的应用
Direct Retrieval of Protoplanetary Disk Dust Properties using Auto-differentiable Gaussian Processes and Its Application to the HD 169142 Disk
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中文总结 AI 辅助
研究旨在检索原行星盘尘埃特性，提出用自动微分高斯过程的新框架，通过计算径向强度分布、生成可见度模型并与观测数据比较，经马尔可夫链蒙特卡罗方法采样后验分布，在JAX中实现加速推理，验证方法并应用于HD 169142盘，提供高精度盘SED建模基础设施。
中文摘要 AI 辅助
在原行星盘中检索尘埃特性是行星形成观测研究的一项基本任务。虽然利用阿塔卡马大型毫米波/亚毫米波阵列（ALMA）等干涉仪对光谱能量分布（SED）进行多波长分析是一种强大的诊断工具，但传统方法常因成像束尺寸有限产生的强偏差而受阻。本文提出了一种新的尘埃盘特性检索框架来克服这一挑战。假设基础物理结构由高斯过程的样本路径表示，计算观测波长处的径向强度分布并生成一维可见度模型，将模型与观测数据比较，通过马尔可夫链蒙特卡罗方法对后验分布进行采样。整个过程在JAX中实现，实现了端到端自动微分并显著加速推理。用模拟数据集验证方法，结果偏差不大且能更好地重现输入剖面。通过应用于HD 169142盘的ALMA波段3、6和9观测展示其能力，揭示了新的复杂结构。开发的代码以Python模块FRAP公开可用。该框架为盘SED建模提供了下一代基础设施，有助于对行星形成的物理环境进行高精度研究。
英文摘要
Retrieving dust properties in protoplanetary disks, including the surface density distribution, temperature, and grain size distribution, is a fundamental task in observational studies of planet formation. While multi-wavelength analysis of the spectral energy distribution (SED) using interferometers such as the Atacama Large Millimeter/submillimeter Array (ALMA) is a powerful diagnostic tool, traditional methods are often hindered by strong biases arising from a limited imaging beamsize. In this paper, we present a new retrieval framework for dust disk properties designed to overcome this challenge. We assume that the underlying physical structures are expressed as sample paths from Gaussian processes, compute the radial intensity distributions at observed wavelengths, and produce one-dimensional visibility models. The models are compared with the observed data and the posterior distributions are sampled via the Markov-Chain Monte-Carlo method. The whole procedure is implemented in JAX, which enables end-to-end auto-differentiation and significantly accelerates the inference. We validate our methodology using mock datasets, and find that the results are not strongly biased and better reproduce the input profiles. We also demonstrate its capabilities through an application to ALMA Band 3, 6, and 9 observations of the HD 169142 disk, revealing a new complex structure. Our developed code is publicly available as a Python module, FRAP (Flexible Radial Analysis of Protoplanetary disks). This framework provides a next-generation infrastructure for disk SED modeling, enabling high-precision studies of the physical environments in which planets form.
