Canny Algorithm, Cosmic Strings and the Cosmic Microwave Background
Bibliographic record
Abstract
We describe a new code to search for signatures of cosmic strings in cosmic microwave anisotropy maps. The code implements the Canny Algorithm, an edge detection algorithm designed to search for the lines of large gradients in maps. Such a gradient signature which is coherent in position space is produced by cosmic strings via the Kaiser-Stebbins effect. We test the power of our new code to set limits on the tension of the cosmic strings by analyzing simulated data with and without cosmic strings. We compare maps with a pure Gaussian scale-invariant power spectrum with maps which have a contribution of a distribution of cosmic strings obeying a scaling solution. The maps have angular scale and angular resolution comparable to what current and future ground-based small-scale cosmic microwave anisotropy experiments will achieve. We present tests of the codes, indicate the limits on the string tension which could be set with the current code, and describe various ways to refine the analysis. Our results indicate that when applied to the data of ongoing cosmic microwave experiments such as the South Pole Telescope project, the sensitivity of our method to the presence of cosmic strings will be more than an order of magnitude better than the limits from existing analyses.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".