Foschino S, Berné O, Joblin C
Institut de Recherche en Astrophysique et Planetologie, Université de Toulouse, CNRS, CNES, UPS, Toulouse, France, 9 Av. du colonel Roche, 31028 Toulouse Cedex 04, France.
Astron Astrophys. 2019 Dec 5;632. doi: 10.1051/0004-6361/201935085. eCollection 2019 Dec.
The (JWST) will deliver an unprecedented quantity of high-quality spectral data over the 0.6-28 m range. It will combine sensitivity, spectral resolution, and spatial resolution. Specific tools are required to provide efficient scientific analysis of such large data sets.
Our aim is to illustrate the potential of unsupervised learning methods to get insights into chemical variations in the populations that carry the aromatic infrared bands (AIBs), more specifically polycyclic aromatic hydrocarbon (PAH) species and carbonaceous very small grains (VSGs).
We present a method based on linear fitting and blind signal separation for extracting representative spectra for a spectral data set. The method is fast and robust, which ensures its applicability to JWST spectral cubes. We tested this method on a sample of ISO-SWS data, which resemble most closely the JWST spectra in terms of spectral resolution and coverage.
Four representative spectra were extracted. Their main characteristics appear consistent with previous studies with populations dominated by cationic PAHs, neutral PAHs, evaporating VSGs, and large ionized PAHs, known as the PAH population. In addition, the 3 m range, which is considered here for the first time in a blind signal separation (BSS) method, reveals the presence of aliphatics connected to neutral PAHs. Each representative spectrum is found to carry second-order spectral signatures (e.g., small bands), which are connected with the underlying chemical diversity of populations. However, the precise attribution of theses signatures remains limited by the combined small size and heterogeneity of the sample of astronomical spectra available in this study.
The upcoming JWST data will allow us to overcome this limitation. The large data sets of hyperspectral images provided by JWST analysed with the proposed method, which is fast and robust, will open promising perspectives for our understanding of the chemical evolution of the AIB carriers.
詹姆斯·韦布空间望远镜（JWST）将在0.6 - 28微米范围内提供前所未有的高质量光谱数据。它将灵敏度、光谱分辨率和空间分辨率结合在一起。需要特定的工具来对如此庞大的数据集进行高效的科学分析。
我们的目的是说明无监督学习方法在洞察携带芳香红外波段（AIBs）的群体中的化学变化方面的潜力，更具体地说是多环芳烃（PAH）物种和碳质非常小的颗粒（VSGs）。
我们提出了一种基于线性拟合和盲信号分离的方法，用于为光谱数据集提取代表性光谱。该方法快速且稳健，确保了其对JWST光谱立方体的适用性。我们在ISO - SWS数据样本上测试了此方法，该样本在光谱分辨率和覆盖范围方面与JWST光谱最为相似。
提取了四个代表性光谱。它们的主要特征似乎与先前以阳离子PAHs、中性PAHs、蒸发的VSGs和大的离子化PAHs为主的群体研究一致，这些群体被称为PAH群体。此外，在盲信号分离（BSS）方法中首次在此考虑的3微米范围揭示了与中性PAHs相连的脂肪族的存在。发现每个代表性光谱都携带二阶光谱特征（例如小波段），这与群体潜在的化学多样性相关。然而，这些特征的精确归属仍然受到本研究中可用天文光谱样本的小尺寸和异质性的限制。
即将到来的JWST数据将使我们能够克服这一限制。用所提出的快速且稳健的方法分析JWST提供的高光谱图像的大数据集，将为我们理解AIB载体的化学演化开辟有前景的视角。
