发表机构
University of Southampton; University of Cambridge; University of Oxford; Space Telescope Science Institute; Eureka Scientific Inc; Queen’s University Belfast; ComplyAdvantage; Univ. Grenoble Alpes CNRS IPAG（南安普顿大学; 剑桥大学; 牛津大学; 太空望远镜科学研究所; 尤里卡科学公司; 贝尔法斯特女王大学; ComplyAdvantage; 格勒诺布尔阿尔卑斯大学CNRS IPAG）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍开源框架Speculate，利用Sirocco模拟光谱库和多种插值方法快速仿真拟合吸积盘风光谱，应用于CV观测，实现高效参数推断并揭示模型局限。
AI 中文摘要
盘风是吸积天体物理系统中的关键组成部分，对其观测可以产生多种多样的谱线轮廓。然而，推断盘风参数仍具挑战性：底层参数空间是高维且简并的，而辐射转移模拟既不完美又计算昂贵。本文旨在弥合这一差距。在此，我们介绍“Speculate”，一个开源框架，用于快速仿真和拟合由蒙特卡洛辐射转移代码Sirocco生成的合成吸积盘风光谱观测。Speculate包括我们发布的激变变星（CV）训练数据，一个包含88,680个稀疏网格化Sirocco光谱的库，覆盖紫外和光学波段，一套预训练的仿真器，以及其交互式Marimo用户界面。Speculate使用主成分分析压缩光谱网格，然后提供三种方法来插值得到的潜在表示：正则网格插值、神经网络或高斯过程。这三种架构都能使Speculate在1-40毫秒内生成一个Sirocco光谱，从而实现高效推断。前两种支持快速χ²优化。高斯过程利用贝叶斯推断引擎，结合协方差感知的灵活似然函数，该函数改编自Starfish框架并加速。此函数有助于吸收模拟与数据之间的相关差异。测试表明，所有架构都能以大致相当的精度重现未见过的Sirocco光谱，并恢复其物理参数。随后，我们将Speculate应用于5个代表性的类新星CV光谱，重现了几个观测到的谱线特征，同时也暴露了当前模型网格忽略相关物理的失配之处。
英文摘要
Disc winds are critical components of accreting astrophysical systems and their observations can yield a wide variety of spectral line profiles. Yet, inferring disc wind parameters remains challenging: the underlying parameter spaces are high-dimensional and degenerate, while the radiative-transfer simulations are imperfect and computationally expensive. This paper tackles bridging this gap. Here, we present 'Speculate', an open-source framework for rapidly emulating and fitting observations with synthetic accretion disc wind spectra, generated from the Monte Carlo radiative-transfer code Sirocco. Speculate includes the release of our cataclysmic variable (CV) training data, a library of 88,680 sparsely gridded Sirocco spectra spanning the ultraviolet and optical wave bands, a suite of pre-trained emulators, and its interactive Marimo user interface. Speculate compresses spectral grids using principal component analysis and then provides three approaches to interpolate the resulting latent representation: with a regular-grid interpolation, a neural network, or a Gaussian process. All three architectures enable Speculate to generate a Sirocco spectrum between $1-40\,\mathrm{ms}$, thereby enabling efficient inference. The first two support rapid $χ^2$ optimisation. The Gaussian process utilises a Bayesian inference engine coupled with a covariance-aware, flexible likelihood function, adapted and accelerated from the Starfish framework. This function helps absorb correlated discrepancies between simulations and data. Tests show all architectures reproduce unseen Sirocco spectra with broadly comparable accuracy and can recover their physical parameters. Subsequently, we apply Speculate to 5 representative nova-like CV spectra, reproducing several observed line features while also exposing mismatches where the current model grid omits relevant physics.
Comments31 pages, 11 figures, Submitted to RASTI and Awaiting Peer Review
