Online Archive of University of Virginia Scholarship
Real-Time Automation of Space Telescope Data Analysis; The Sociotechnical Implications of AI in Astronomical Research11 views
Author
Vardhanapu, Raina, School of Engineering and Applied Science, University of Virginia
Advisors
Norton, Peter, EN-Engineering and Society, University of Virginia
Morrison, Briana, EN-Comp Science Dept, University of Virginia
Abstract
To convert massive telescope data into intelligible scientific knowledge, astronomers must combine the capabilities of artificial intelligence (AI) with human judgment and accountability.
The technical project proposes a design of a computer-vision system that detects and classifies celestial events from telescope imagery in real time. By integrating convolutional neural networks (CNNs) with Vision Transformers (ViTs), the system captures both local and global spatial features of astronomical images. It produces labeled detections, bounding boxes, and searchable metadata that speed observation and reduce error. Test results show that the hybrid model improves detection accuracy and inference speed, suggesting that automated image analysis can enhance discovery when paired with clear documentation and reproducible methods.
The sociotechnical research paper examines how such automation reshapes authority, collaboration, and accountability in astronomy. Drawing on the theory of co-production within a sociology of scientific knowledge framework, the study finds that AI strengthens research only when paired with equitable access, transparent governance, and review processes open to dissent. Without these safeguards, automation risks concentrating power and obscuring responsibility. Together, these projects show that progress in AI-assisted astronomy depends as much on institutional ethics as on technical design.
Degree
BS (Bachelor of Science)
Keywords
Real-Time Image Analysis; Space Telescope Data; CNN–ViT Hybrid Models; Astronomical Event Detection; AI in Astronomy
Notes
School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Briana Morrison
STS Advisor: Peter Norton
Language
English
Rights
All rights reserved by the author (no additional license for public reuse)
Vardhanapu, Raina. Real-Time Automation of Space Telescope Data Analysis; The Sociotechnical Implications of AI in Astronomical Research. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2025-12-11, https://doi.org/10.18130/9n7g-7n26.