📝 SummarXiv

Abstract

Studies in the past few decades have investigated young stellar object evolution based on their spectral energy distribution (SED). The SED is heavily influenced not only by evolutionary stage, but also the morphology of the young star. This work is part of the NEMESIS project which is aiming to revisit star formation with the aid of machine learning techniques and provides the framework for this work. In a first effort towards a novel spectro-morphological classification we analyzed young stellar object morphologies and linked them to the currently used observational classes. Thereby we aim to lay the foundation for a spectro-morphological classification, and apply the insights learned in this study in a future, revisited classification scheme. We obtained archival high-resolution survey images from VISTA for approximately 10,000 literature young stellar object candidates towards the Orion star formation complex (OSFC). Utilizing a Self-Organizing map (SOM) algorithm, an unsupervised machine learning method, we created a grid of morphological prototypes from near- and mid-infrared images. Furthermore, we determined which prototypes are most representative of the different observational classes, derived from the infrared spectral index, via Bayesian inference. We present our grids of morphological prototypes of young stellar objects in the near-infrared, which were created purely from observational data. They are thus non-dependent on theoretical models. In addition, we show maps that indicate the probability for a prototype belonging to any of the observational classes. We find that SOMs created from near-infrared images are a useful tool, with limitations, to identify characteristic morphologies of young stellar objects in different evolutionary stages. This first step lays the foundation for a spectro-morphological classification of young stellar objects to be developed in the future.