AuroraX, PyAuroraX, and aurora-asi-lib: A user-friendly auroral all-sky imager analysis framework
Bibliographic record
Abstract
Within the context of the Heliophysics System Observatory, optical images of the aurora are emerging as an important resource for exploring multi-scale geospace processes. This capability has never been more critical as we are on the cusp of a new era of geospace research, by which we mean studying the overall system as a system of systems . Historically, the patchwork of ground-based instrumentation has required customized solutions for accessing data, assessing data relevance, and then ultimately using each individual network alongside other assets. Here we introduce a new and comprehensive approach for data discovery and utilization for one type of data, namely auroral images. The AuroraX project ( https://aurorax.space/ ) is a cyberinfrastructure platform for the discovery of scientific opportunities with access to optical auroral data. The program has broad objectives, so we focus on one key thread. In particular, we focus on describing the AuroraX platform and its API and web-based tools for all-sky imager (ASI) data. As a practical example, we demonstrate how to identify conjunctions using the AuroraX conjunction finder or PyAuroraX, a Python library that interfaces with the AuroraX platform. We then demonstrate how aurora-asi-lib, a Python library for interacting with and analyzing high-resolution ASI data, can be used for detailed conjunction analysis on a personal computer. Together, these tools enable a rapid and streamlined end-to-end exploration of auroral data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".