Extended Vision techniques are ubiquitous in physics. However, the data cubes steaming from such analysis often pose a challenge in their interpretation, due to the intrinsic difficulty in discerning the relevant information from the spectra composing the data cube. Furthermore, the huge dimensionality of data cube spectra poses a complex task in its statistical interpretation; nevertheless, this complexity contains a massive amount of statistical information that can be exploited in an unsupervised manner to outline some essential properties of the case study at hand, e.g.~it is possible to obtain an image segmentation via (deep) clustering of data-cube's spectra, performed in a suitably defined low-dimensional embedding space. To tackle this topic, we explore the possibility of applying unsupervised clustering methods in encoded space, i.e. perform deep clustering on the spectral properties of datacube pixels. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, in charge of mapping spectra into lower dimensional metric spaces, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm. We apply this technique to two different use cases, of different physical origins: a set of Macro mapping X-Ray Fluorescence (MA-XRF) synthetic data on pictorial artworks, and a dataset of simulated astrophysical observations.

翻译：扩展视觉技术在物理学中广泛应用。然而，此类分析产生的数据立方体常因其光谱中有效信息难以辨别而给解读带来挑战。此外，数据立方体光谱的巨大维度对其统计分析构成了复杂任务；尽管如此，这种复杂性中蕴含着大量可无监督利用的统计信息，用以勾勒当前案例研究的基本特性——例如，通过在恰当定义的低维嵌入空间中对数据立方体光谱进行（深度）聚类，可实现图像分割。针对这一主题，我们探索了在编码空间中应用无监督聚类方法的可能性，即对数据立方体像素的光谱特性执行深度聚类。通过专门训练的（变分）自编码器实现统计降维，负责将光谱映射到低维度量空间；而聚类过程则由（可学习的）迭代K均值聚类算法执行。我们将该技术应用于两个不同物理来源的案例：一组关于绘画艺术品的宏观X射线荧光（MA-XRF）合成数据，以及一个模拟天体物理观测数据集。