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Record W2532431995 · doi:10.1088/1475-7516/2017/03/050

Prospects for cosmological collider physics

2017· article· en· W2532431995 on OpenAlexaff
P. Daniel Meerburg, Moritz Münchmeyer, Julián B. Muñoz, Xingang Chen

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsPerimeter InstituteUniversity of Toronto
Fundersnot available
KeywordsPhysicsBispectrumNon-GaussianityInflation (cosmology)ObservablePhysics beyond the Standard ModelUniverseParticle physicsColliderCosmologyExtra dimensionsLimit (mathematics)Dark matterTheoretical physicsAstrophysicsSpectral densityQuantum mechanicsCosmic microwave backgroundStatistics

Abstract

fetched live from OpenAlex

It is generally expected that heavy fields are present during inflation, which can leave their imprint in late-time cosmological observables. The main signature of these fields is a small amount of distinctly shaped non-Gaussianity, which if detected, would provide a wealth of information about the particle spectrum of the inflationary Universe. Here we investigate to what extent these signatures can be detected or constrained using futuristic 21-cm surveys. We construct model-independent templates that extract the squeezed-limit behavior of the bispectrum, and examine their overlap with standard inflationary shapes and secondary non-Gaussianities. We then use these templates to forecast detection thresholds for different masses and couplings using a 3D reconstruction of modes during the dark ages ( z ∼ 30–100). We consider interactions of several broad classes of models and quantify their detectability as a function of the baseline of a dark ages interferometer. Our analysis shows that there exists the tantalizing possibility of discovering new particles with different masses and interactions with future 21-cm surveys.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.066
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0070.011
Open science0.0010.004
Research integrity0.0120.009
Insufficient payload (model declined to judge)0.0660.010

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 head0.023
GPT teacher head0.304
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations124
Published2017
Admission routes1
Has abstractyes

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