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The H{\alpha} spectral line is a well-studied absorption line revealingproperties of the highly structured and dynamic solar chromosphere. Typicalfeatures with distinct spectral signatures in H{\alpha} include filaments andprominences, bright active-region plages, superpenumbrae around sunspots,surges, flares, Ellerman bombs, filigree, and mottles and rosettes, amongothers. This study is based on high-spectral resolution H{\alpha} spectraobtained with the echelle spectrograph of the Vacuum Tower Telescope (VTT)located at Observatorio del Teide (ODT), Tenerife, Spain. The t-distributedStochastic Neighbor Embedding (t-SNE) is a machine learning algorithm, which isused for nonlinear dimensionality reduction. In this application, it projectsH{\alpha} spectra onto a two-dimensional map, where it becomes possible toclassify the spectra according to results of Cloud Model (CM) inversions. TheCM parameters optical depth, Doppler width, line-of-sight velocity, and sourcefunction describe properties of the cloud material. Initial results of t-SNEindicate its strong discriminatory power to separate quiet-Sun and plageprofiles from those that are suitable for CM inversions. In addition, adetailed study of various t-SNE parameters is conducted, the impact of seeingconditions on the classification is assessed, results for various types ofinput data are compared, and the identified clusters are linked tochromospheric features. Although t-SNE proves to be efficient in clusteringhigh-dimensional data, human inference is required at each step to interpretthe results. This exploratory study provides a framework and ideas on how totailor a classification scheme towards specific spectral data and sciencequestions.

Classification of High-resolution Solar H{\alpha} Spectra using t-distributed Stochastic Neighbor Embedding