Fitting infrared ice spectra with genetic modelling algorithms. Presenting the ENIIGMA fitting tool
Fitting infrared ice spectra with genetic modelling algorithms. Presenting the ENIIGMA fitting tool
复制标题
用遗传建模算法拟合红外冰光谱。
DOI：
10.1051/0004-6361/202039360
复制
发表时间：
2021
影响因子：
6.5
通讯作者：
J. Jørgensen
中科院分区：
文献类型：
--
作者：
W. Rocha;G. Perotti;L. Kristensen;J. Jørgensen
语境。为了准确解释原恒星的红外光谱，需要模拟不同化学环境、冰形态以及热和能量处理的各种实验室冰光谱。为了破译最适合观察结果的实验室数据组合，需要一种基于统计的自动化计算方法。目标。我们的目标是引入一种基于进化算法的新方法，通过实验室冰光谱的红外观测数据的光谱分解来搜索冰幔中的分子。方法。我们引入了一种公开可用的开源拟合工具，称为 ENIIGMA（使用遗传建模算法对红外冰特征进行分解）。该工具具有专用的 Python 函数来执行原恒星光谱的连续测定、硅酸盐提取、光谱分解和统计分析，以计算置信区间并量化简并性。我们对已知的冰样本和构建的混合物进行了完全盲和非盲测试，以评估代码。此外，我们对 Elias 29 谱进行了完整分析，并将我们的发现与之前的文献结果进行了比较。结果。 ENIIGMA 拟合工具可以在本文中测试的已知样品的所有检查中识别正确的冰样品及其分数。在实验数据中添加高斯噪声的情况下，需要更鲁棒的遗传算子和更多的迭代。关于 Elias 29 光谱，经过连续测定和硅酸盐萃取后，2.5 至 20 µm 之间的宽光谱范围已成功分解。该分析能够识别冰幔中的不同分子，包括初步检测到 CH 3 CH 2 OH。结论。 ENIIGMA 是一个用于红外光谱分析的工具箱，与詹姆斯·韦伯太空望远镜的发射恰逢其时。此外，它还允许探索不同的化学环境和辐射场，从而使用户能够正确解释天文观测结果。
Context. A variety of laboratory ice spectra simulating di ff erent chemical environments, ice morphologies, and thermal and energetic processing are needed in order to provide an accurate interpretation of the infrared spectra of protostars. To decipher the combination of laboratory data that best fits the observations, an automated, statistics-based computational approach is necessary. Aims. We aim to introduce a new approach, based on evolutionary algorithms, to searching for molecules in ice mantles via spectral decomposition of infrared observational data with laboratory ice spectra. Methods. We introduce a publicly available and open-source fitting tool called ENIIGMA (dEcompositioN of Infrared Ice features using Genetic Modelling Algorithms). The tool has dedicated Python functions to carry out continuum determination of the protostellar spectra, silicate extraction, spectral decomposition, and statistical analysis to calculate confidence intervals and quantify degeneracy. We conducted fully blind and non-blind tests with known ice samples and constructed mixtures in order to asses the code. Additionally, we performed a complete analysis of the Elias 29 spectrum and compared our findings with previous results from the literature. Results. The ENIIGMA fitting tool can identify the correct ice samples and their fractions in all checks with known samples tested in this paper. In the cases where Gaussian noise was added to the experimental data, more robust genetic operators and more iterations became necessary. Concerning the Elias 29 spectrum, the broad spectral range between 2.5 and 20 µ m was successfully decomposed after continuum determination and silicate extraction. This analysis allowed the identification of di ff erent molecules in the ice mantle, including a tentative detection of CH 3 CH 2 OH. Conclusions. The ENIIGMA is a toolbox for spectroscopy analysis of infrared spectra that is well-timed with the launch of the James Webb Space Telescope. Additionally, it allows di ff erent chemical environments and irradiation fields to be explored, allowing the user to correctly interpret astronomical observations.