# Closely-Spaced Object Classification Using MuyGPyS

arXiv: https://arxiv.org/abs/2311.10904 · HTML: https://mlsanity.com/paper/2311.10904/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation](https://mlsanity.com/paper/2104.14581/) (2021)
- [Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)](https://mlsanity.com/paper/2208.14592/) (2022)

## Built on by (most cited first, 2 of 2)

- [DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing](https://mlsanity.com/paper/2505.19148/) (2025)
- [DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation](https://mlsanity.com/paper/2609.18773/) (2026)

## Abstract (arXiv)

Accurately detecting rendezvous and proximity operations (RPO) is crucial for understanding how objects are behaving in the space domain. However, detecting closely-spaced objects (CSO) is challenging for ground-based optical space domain awareness (SDA) algorithms as two objects close together along the line-of-sight can appear blended as a single object within the point-spread function (PSF) of the optical system. Traditional machine learning methods can be useful for differentiating between singular objects and closely-spaced objects, but many methods require large training sample sizes or high signal-to-noise conditions. The quality and quantity of realistic data make probabilistic classification methods a superior approach, as they are better suited to handle these data inadequacies. We present CSO classification results using the Gaussian process python package, MuyGPyS, and examine classification accuracy as a function of angular separation and magnitude difference between the simulated satellites. This orbit-independent analysis is done on highly accurate simulated SDA images that emulate realistic ground-based commercial-of-the-shelf (COTS) optical sensor observations of CSOs. We find that MuyGPyS outperforms traditional machine learning methods, especially under more challenging circumstances.