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Record W2505362211 · doi:10.1088/1475-7516/2017/06/004

Searching for cosmic strings in CMB anisotropy maps using wavelets and curvelets

2017· article· en· W2505362211 on OpenAlexaff
L. T. Hergt, A. Amara, Robert Brandenberger, Tomasz Kacprzak, Alexandre Réfrégier

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsCosmic microwave backgroundCosmic stringString (physics)PlanckAstrophysicsAnisotropyWaveletSouth Pole TelescopeCosmic background radiationTheoretical physicsOptics

Abstract

fetched live from OpenAlex

We use wavelet and curvelet transforms to extract signals of cosmic strings from cosmic microwave background (CMB) temperature anisotropy maps, and to study the limits on the cosmic string tension which various ongoing CMB temperature anisotropy experiments will be able to achieve. We construct sky maps with size and angular resolution corresponding to various experiments. These maps contain the signals of a scaling solution of long string segments with a given string tension $G \mu$, the contribution of the dominant Gaussian primordial cosmological fluctuations, and pixel by pixel white noise with an amplitude corresponding to the instrumental noise of the various experiments. In the case that we include white noise, we find that the curvelets are more powerful than wavelets. For maps with Planck specification, we obtain bounds on the string tension comparable to what was obtained by the Planck collaboration. Experiments with better angular resolution such as the South Pole Telescope third generation (SPT-3G) survey will be able to yield stronger limits. For maps with a specification of SPT-3G we find that string signals will be visible down to a string tension of $G \mu = 1.4 \times 10^{-7}$.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.322
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2017
Admission routes1
Has abstractyes

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