Navigating Exoplanetary Systems in Augmented Reality: Preliminary Insights on ExoAR
Bibliographic record
Abstract
With thousands of exoplanets now confirmed by space missions such as NASA’s Kepler and TESS, scientific interest and public curiosity about these distant worlds continue to grow. However, current visualization tools for exploring exoplanetary systems often lack sufficient scientific accuracy or interactive features, limiting their educational effectiveness and analytical utility. To help address this gap, we developed ExoAR, an augmented reality tool designed to offer immersive, scientifically sound visualizations of all known exoplanetary systems using data directly sourced from NASA’s Exoplanet Archive. By leveraging augmented reality’s strengths, ExoAR enables users to immerse themselves in interactive, dynamic 3D models of these planetary systems with data-driven representations of planets and their host stars. The application also allows users to adjust various visualization scales independently, a capability designed to aid comprehension of comparative astronomical properties such as orbital mechanics, planetary sizes, and stellar classifications. To begin assessing ExoAR’s potential as an educational and analytical tool and inform future iterations, a pilot user study was conducted. Its findings indicate that participants found ExoAR improved user engagement and spatial understanding compared to NASA’s Eyes on Exoplanets application, a non-immersive exoplanetary system visualization tool. This work-in-progress paper presents these early insights, acknowledges current system limitations, and outlines future directions for more rigorously evaluating and further improving ExoAR’s capabilities for both educational and scientific communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".