Probing Diverse Phenomena through Data-Intensive Astronomy
Bibliographic record
Abstract
The era of data-intensive astronomy is being ushered in with the increasing size and complexity of observational data across wavelength and time domains, the development of algorithms to extract information from this complexity, and the computational power to apply these algorithms to the growing repositories of data. Data-intensive approaches are pushing the boundaries of nearly all fields of astronomy, from exoplanet science to cosmology, and they are becoming a critical modality for how we understand the universe. The success of these approaches range from the discovery of rare or unexpected phenomena, to characterizing processes that are now accessible with precision astrophysics and a deep statistical understanding of the datasets, to developing algorithms that maximize the science that can be extracted from any set of observations. In this white paper, we propose a number of initiatives to maximize Canada's ability to compete in this data-intensive era. We propose joining international collaborations and leveraging Canadian facilities for legacy data potential. We propose continuing to build a more agile computing infrastructure that's responsive to the needs of tackling larger and more complex data, as well as enabling quick prototyping and scaling of algorithms. We recognize that developing the fundamental skills of the field will be critical for Canadian astronomers, and discuss avenues through with the appropriate computational and statistical training could occur. Finally, we note that the transition to data-intensive techniques is not limited to astronomy, and we should coordinate with other disciplines to develop and make use of best practises in methods, infrastructure, and education.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".