Cosmic string searches in new observational windows
Bibliographic record
Abstract
Placing observational limits on cosmic strings would provide important confirmation of or constraints on early universe models.Cosmic strings imprint the cosmic microwave background (CMB) with a distinct position space signature, leaving line discontinuities in the temperature maps due to a combination of gravitational lensing and the Doppler effect.To improve theoretical observational constraints, I wrote sky map simulations with and without cosmic strings, edge detection and counting algorithms, and programs to differentiate statistically between the ambient edges due to the inflationary background and the string signals.Our application of position space algorithms, specifically the Canny edge detection algorithm, was highly successful and allowed us to establish improved limits, by more than an order of magnitude, on the contribution of cosmic strings to the total fluctuation spectrum from simulated data.We extended our analysis of the Canny algorithm to distinguish between abelian cosmic strings and cosmic superstrings through the presence of threestring junctions in cosmic superstring maps.To this end, I wrote the first simulations of maps with junctions and found a disparity in the density of edges in maps of string networks with and without junctions.This work resulted in a statistic to differentiate between different cosmic string models including string theory models and models with different numbers of cosmic strings.I also modeled the position space polarization signal induced by cosmic string wakes.Due to the gravity of a moving string, matter falls into overdense two dimensional sheets of matter, wakes, behind the string.As photons propagate through the wakes, they encounter this overdensity of ionized matter which can polarize them.In our work we placed the first limits on the CMB polarization signature of cosmic strings including their signature nonlinear structures, wakes, and found they would produce an observable signal
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".