AutoClassMK: A public neural network for automatic 2D MK classification of normal stars in basic Python

[2606.11161] AutoClassMK: A public neural network for automatic 2D MK classification of normal stars in basic Python Skip to main content Search Submit Donate Log in Astrophysics > Solar and Stellar Astrophysics arXiv:2606.11161 (astro-ph) [Submitted on 9 Jun 2026 ( v1 ), last revised 26 Aug 2026 (this version, v2)] Title: AutoClassMK: A public neural network for automatic 2D MK classification of normal stars in basic Python Authors: C. Ian Short View PDF HTML (experimental) Abstract: We present AutoClassMK, a simple, fully-connected, five-layer double-headed neural network written entirely in Python and Numpy that classifies normal stellar spectra conforming to the libr18 MK atlas in the 2D MK classification system with a high degree of precision and recall. AutoClassMK has the distinction of having transparent basic code with no calls to specialized libraries. In this paper we take care to explicitly describe in detail the ideas and operations that enable the network. Training AutoClassMK required us to develop large, noisy artificial training and test sets by augmenting the libr18 and libr18_27 MK atlases and to simplify the luminosity classification so that every combination of spectral- and luminosity-class is represented in the training set. We then test the network's ability to predict the MK spectral type of noisy augmentations of spectra in the libr18_225 MK atlas. We then implemented the same architecture in PyTorch to gain further insight and to enable execution on CUDA GPU's. All codes and the training and test sets are available from the OpenStars www site: this http URL . Comments: 26 pages pre-print format, 7 figures. New Fig. (2) showing weak spectral features. Missing data points added to Fig. 6. Few minor clarifications Subjects: Solar and Stellar Astrophysics (astro-ph.SR) ; Instrumentation and Methods for Astrophysics (astro-ph.IM) Cite as: arXiv:2606.11161 [astro-ph.SR] (or arXiv:2606.11161v2 [astro-ph.SR] for this version) https://doi.org/10.48550/arXiv.2606.11161 Focus to learn more arXiv-issued DOI via DataCite Submission history From: C. Ian Short [ view email ] [v1] Tue, 9 Jun 2026 17:43:03 UTC (3,481 KB) [v2] Wed, 26 Aug 2026 18:30:19 UTC (3,577 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: astro-ph.SR < prev | next > new | recent | 2026-06 Change to browse by: astro-ph astro-ph.IM References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• AutoClassMK: A public neural network for automatic 2D MK classification of normal stars in basic Python#674437
• Astronomy for amateurs#646026
• Spectral classification of OB-type stars using tree-based ensemble methods and evaluation of their explainability#1012767
• OB stars identified in LAMOST Data Release 10#799421
• OB stars identified in LAMOST Data Release 10#797998