Classification of Astronomical Spectra Using PCA-Compressed Flux and Inverse-Variance Features
arXiv:2606.13978v1 Announce Type: cross Abstract: This paper evaluates a signal-processing and supervised-learning pipeline for classifying SDSS DR17 astronomical spectra into stars, galaxies, and quasars. Each spectrum is represented by its measured flux and inverse-variance information, combining spectral shape with a wavelength-dependent reliability profile. After resampling onto a common logarithmic wavelength grid, the flux and inverse-variance vectors are standardized and separately compressed using principal component analysis. The resulting components are concatenated and used to train
This paper leverages advanced signal processing and machine learning techniques, representing current trends in AI application across scientific domains. It builds on the availability of large astronomical datasets and computational methods.
This development enhances the automation and accuracy of astronomical data classification, accelerating discovery and reducing manual expert-intensive work for discerning cosmic objects. It demonstrates practical applications of AI in scientific research.
The efficiency and reliability of classifying astronomical phenomena will improve, shifting workload from manual analysis to automated systems and potentially enabling new insights from massive datasets.
- · Astronomical research institutions
- · Machine learning researchers
- · Space observatories
Faster and more accurate categorization of celestial objects from spectral data.
Increased pace of astronomical discovery and a deeper understanding of cosmic evolution.
Potential for new AI-driven telescopes or observatories that integrate real-time classification at the data acquisition stage.
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Read at arXiv cs.LG