Joint Temperature-Luminosity Classification of Stellar Spectra with a Distance-Aware Loss Function

Date

2025-8-28

Author

Sezen, Volga

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Modern sky surveys continue to produce large datasets, making automated classification essential for population studies and new discoveries. In this work, a stellar spectral database was assembled from five public libraries and re-labelled via SIMBAD. A 1-D CNN with three branches of differing kernel sizes was initiated to focus on different features while predicting a star’s MK temperature and luminosity class in one pass. A distance-aware loss was used, coupling cross-entropy with mean-squared error on the 2-D MK grid so physically further misclassifications incur stronger penalties. Alongside the three branch CNN, other architectures were trained and evaluated using several metrics on a held-out test set. Under circularly shifted inputs, performance remained robust up to 3-pixel shifts. An ensemble of the three branch CNN with a custom ResNet50 achieved macro F1 of 67.6% and kappa of 98.3 and 88.4 across two axis, with statistically significant gains over each counterpart under paired bootstrap testing. Guided GradCAM indicates a non-linear relation between branch kernel size and identified features, with some features overlapping known molecular bands. All code, trained weights, curated dataset, and additional results are available at GitHub.

Subject Keywords

Stellar Classification, 1D CNN, Morgan-Keenan Grid, Ordinal Classification, Distance Aware Loss, Ordinal Classification

URI

https://hdl.handle.net/11511/115569

Collections

Graduate School of Informatics, Thesis