基于前向建模的无缝分光光度法：原理及其在大气透射率测量中的应用
Slitless spectrophotometry with forward modelling: principles and application to atmospheric transmission measurement
AI总结：
本文提出基于前向建模的无缝分光光度法，开发 Spectractor 软件从无缝光谱数据中提取标准星光谱，并用于现场大气透射率监测，为 Rubin 巡天超新星宇宙学项目实现千分之一星等精度提供支持。
摘要：
在未来十年中，许多光学巡天项目将致力于探究暗能量的本质，以千分之一的精度测量其状态方程参数。这要求对巡天的光度定标达到前所未有的可信精度，并控制众多系统不确定度来源。对每次曝光、每个季节平均或整个巡天平均的现场大气透射率进行测量，有助于使星等达到千分之一精度。本工作旨在证明无缝光谱可用于标准星分光光度测量，并可用于按需监测现场大气透射率，例如 Vera C. Rubin Observatory Legacy Survey of Space and Time 超新星宇宙学项目所需的情形。我们完整处理了滤光轮中放置色散器的情形，这正是 Rubin Auxiliary Telescope 所选择的配置。无缝分光光度法的理论基础是我们前向建模方法的核心，该方法用于从无缝数据中提取光谱信息。我们开发了一个公开可用的软件 Spectractor（https://github.com/LSSTDESC/Spectractor），它实现了模型的各个组成部分，并最终直接在图像数据上对谱图模型进行拟合以获得光谱。我们在模拟中表明，该模型使我们能够理解分光光度曝光图像的结构。我们还展示了其在真实数据上的应用，解决了具体问题，并说明了我们的流程如何改进描述数据的模型。最后，我们讨论了如何利用该方法直接从数据中提取大气透射率参数，从而为现场大气监测提供基础。我们展示了该流程在模拟中的效率，并在有限的可用数据集上对其进行了测试。
英文摘要：
In the next decade, many optical surveys will aim to tackle the question of dark energy nature, measuring its equation of state parameter at the permil level. This requires trusting the photometric calibration of the survey with a precision never reached so far, controlling many sources of systematic uncertainties. The measurement of the on-site atmospheric transmission for each exposure, or on average for each season or for the full survey, can help reach the permil precision for magnitudes. This work aims at proving the ability to use slitless spectroscopy for standard star spectrophotometry and its use to monitor on-site atmospheric transmission as needed, for example, by the Vera C. Rubin Observatory Legacy Survey of Space and Time supernova cosmology program. We fully deal with the case of a disperser in the filter wheel, which is the configuration chosen in the Rubin Auxiliary Telescope. The theoretical basis of slitless spectrophotometry is at the heart of our forward model approach to extract spectroscopic information from slitless data. We developed a publicly available software called Spectractor (https://github.com/LSSTDESC/Spectractor) that implements each ingredient of the model and finally performs a fit of a spectrogram model directly on image data to get the spectrum. We show on simulations that our model allows us to understand the structure of spectrophotometric exposures. We also demonstrate its use on real data, solving specific issues and illustrating how our procedure allows the improvement of the model describing the data. Finally, we discuss how this approach can be used to directly extract atmospheric transmission parameters from data and thus provide the base for on-site atmosphere monitoring. We show the efficiency of the procedure on simulations and test it on the limited data set available.
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