# Stellar Blend Image Classification Using Computationally Efficient Gaussian Processes Source: [Stellar Blend Image Classification Using Computationally Efficient Gaussian Processes]() Collection index: [llms.txt]() Data updated: 2025-10-25T06:24:25.759Z Authors: Chinedu Eleh, Yunli Zhang, Rafael Bidese, Benjamin W. Priest, Amanda L. Muyskens, Roberto Molinari, Nedret Billor Published: 2024-07-27T16:45:58.000Z ## Abstract Stellar blends, where two or more stars appear blended in an image, pose a significant visualization challenge in astronomy. Traditionally, distinguishing these blends from single stars has been costly and resource-intensive, involving sophisticated equipment and extensive expert analysis. This is especially problematic for analyzing the vast data volumes from surveys, such as Legacy Survey of Space and Time (LSST), Sloan Digital Sky Survey (SDSS), Dark Energy Spectroscopic Instrument (DESI), Legacy Imaging Survey and the Zwicky Transient Facility (ZTF). To address these challenges, we apply different normalizations and data embeddings on low resolution images of single stars and stellar blends, which are passed as inputs into machine learning methods and to a computationally efficient Gaussian process model (MuyGPs). MuyGPs consistently outperforms the benchmarked models, particularly on limited training data. Moreover, MuyGPs with $r^\\text{th}$ root local min-max normalization achieves 83.8% accuracy. Furthermore, MuyGPs' ability to produce confidence bands ensures that predictions with low confidence can be redirected to a specialist for efficient human-assisted labeling. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()