A ready-to-deploy MLOps software platform for satellite and NEO detection at meter-class ground-based observatories
Abstract
Ground-based astronomical observations frequently contain streaks produced by artificial satellites, space debris, and potentially Near-Earth Objects (NEOs). While machine-learning models can reliably detect these features, their practical adoption in observatory operations is often limited by the lack of integrated tools for visual inspection, validation, workflow management, and structured data storage. This paper presents the StreakMind Workbench, a framework that applies MLOps practices to bridge research-oriented machine-learning pipelines with routine observatory operations. Rather than introducing new detection algorithms, the Workbench addresses a software-engineering challenge in astronomical computing: maintaining a single authoritative source of scientific processing code while providing astronomers with an operational environment for workflow execution and result inspection. Integrated with the reference StreakMind AI model of Carrillo et al. (2026) and implemented in Python using PyQt5, the Workbench supports the complete workflow from FITS ingestion to database storage, including inference, result inspection, database exploration, training management, and Minor Planet Center formatted observations. Validation on 273 images from La Sagra Observatory demonstrates successful end-to-end workflows while maintaining consistency with the underlying StreakMind scientific code. The platform facilitates operational use of a research ML pipeline in meter-class observatories and moderate-scale campaigns, supporting Space Situational Awareness and planetary defence.
Versions
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BibTeX
@misc{navarro2026readytodeploy,
title = {{A ready-to-deploy MLOps software platform for satellite and NEO detection at meter-class ground-based observatories}},
author = {Rafael Carrillo Navarro and Pablo García-Martín and René Duffard and Juan Luis Orellana Montes de Oca and Oscar Ortega Ríos},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.27676},
eprint = {2609.27676},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.27676},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A ready-to-deploy MLOps software platform for satellite and NEO detection at meter-class ground-based observatories
AU - Rafael Carrillo Navarro
AU - Pablo García-Martín
AU - René Duffard
AU - Juan Luis Orellana Montes de Oca
AU - Oscar Ortega Ríos
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2609.27676
UR - https://arxiv.org/abs/2609.27676
AB - Ground-based astronomical observations frequently contain streaks produced by artificial satellites, space debris, and potentially Near-Earth Objects (NEOs). While machine-learning models can reliably detect these features, their practical adoption in observatory operations is often limited by the lack of integrated tools for visual inspection, validation, workflow management, and structured data storage. This paper presents the StreakMind Workbench, a framework that applies MLOps practices to bridge research-oriented machine-learning pipelines with routine observatory operations. Rather than introducing new detection algorithms, the Workbench addresses a software-engineering challenge in astronomical computing: maintaining a single authoritative source of scientific processing code while providing astronomers with an operational environment for workflow execution and result inspection. Integrated with the reference StreakMind AI model of Carrillo et al. (2026) and implemented in Python using PyQt5, the Workbench supports the complete workflow from FITS ingestion to database storage, including inference, result inspection, database exploration, training management, and Minor Planet Center formatted observations. Validation on 273 images from La Sagra Observatory demonstrates successful end-to-end workflows while maintaining consistency with the underlying StreakMind scientific code. The platform facilitates operational use of a research ML pipeline in meter-class observatories and moderate-scale campaigns, supporting Space Situational Awareness and planetary defence.
ER -