AutoClassMK: Neural Network Tool for Automated Stellar Spectral Classification
Researchers have developed AutoClassMK, a neural network written in basic Python that automatically classifies stellar spectra according to the MK classification system with high precision. The tool uses a five-layer neural network trained on augmented spectral atlases and is designed to be transparent and accessible, with code available publicly. The work addresses the need for automated methods to classify large numbers of stellar spectra in modern astronomical surveys.
AutoClassMK is a fully-connected neural network implemented in Python and NumPy that classifies normal stellar spectra into the two-dimensional MK classification system. The researchers developed the tool with an emphasis on transparency, using only basic Python and NumPy without specialized machine learning libraries in the primary implementation. To train the network, the team created large artificial training and test sets by augmenting existing MK spectral atlases (libr18 and libr18_27) and simplified the luminosity classification scheme to ensure all spectral-luminosity class combinations were represented. The network was tested on noisy augmentations of spectra from the libr18_225 atlas and demonstrated high precision and recall. The authors also implemented the same architecture in PyTorch to enable GPU acceleration. All code, training data, and test sets have been made publicly available through the OpenStars website.
What different sources said
- arXiv astro-phCenter
AutoClassMK: A public neural network for automatic 2D MK classification of normal stars in basic Python
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