Breaking the degeneracy in stellar spectral classification from single wide-band images

TL;DR

This paper introduces a novel spectral energy distribution classification method that uses a preliminary PSF model to break degeneracy issues, significantly improving accuracy in stellar spectral classification from wide-band images, which is vital for precise PSF modelling in weak lensing.

Contribution

The paper presents a new SED classification approach that incorporates PSF models to improve spectral classification accuracy, overcoming previous degeneracy limitations.

Findings

Achieved 91% top-two classification accuracy.

Outperformed existing machine-learning methods without spectral PSF consideration.

Analyzed the impact of PSF modeling errors on classification accuracy.

Abstract

The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of view. This is particularly important for weak gravitational lensing studies, where precise PSF modelling is essential to get accurate shear measurements. Previous research has demonstrated that the SED of stars can be inferred from low-resolution observations using machine-learning classification algorithms. However, a degeneracy exists between the PSF size, which can vary significantly across the field of view, and the spectral type of stars, leading to strong limitations of such methods. We propose a new SED classification method that incorporates stellar spectral information by using a preliminary PSF model, thereby breaking this degeneracy and enhancing the classification accuracy. Our method involves calculating a set of similarity features between an observed star and a preliminary PSF model at different wavelengths and applying a support vector machine to these similarity features to classify the observed star into a specific stellar class. The proposed approach achieves a 91\% top-two accuracy, surpassing machine-learning methods that do not consider the spectral variation of the PSF. Additionally, we examined the impact of PSF modelling errors on the spectral classification accuracy.