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Record W1846089102 · doi:10.2172/1335446

Testing Inflation with Large Scale Structure: Connecting Hopes with Reality

2014· report· en· W1846089102 on OpenAlexaffabout
Marcello Alvarez, Tobias Baldauf, J. Richard Bond, Neal Dalal, R. de Putter, O. Doré, Daniel Green, Chris Hirata, Zhiqi Huang, Dragan Huterer, Donghui Jeong, Matthew C. Johnson, Elisabeth Krause, Marilena Loverde, Joel Meyers, Daniel Meeburg, Leonardo Senatore, Sarah Shandera, Eva Silverstein, Anže Slosar, Kendrick M. Smith, Matías Zaldarriaga, Valentin Assassi, Jonathan Braden, Amir Hajian, Takeshi Kobayashi, George Stein, Alexander van Engelen

Bibliographic record

Venuenot available
Typereport
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsCanadian Institute for Advanced ResearchYork UniversityPerimeter InstituteUniversity of Toronto
FundersU.S. Department of Energy
KeywordsCosmic microwave backgroundEquilateral triangleInflation (cosmology)Scale (ratio)PhysicsGalaxyCurvatureAstrophysicsGeographyTheoretical physicsMathematicsCartographyOpticsGeometry

Abstract

fetched live from OpenAlex

The statistics of primordial curvature fluctuations are our window into the period of inflation, where these fluctuations were generated. To date, the cosmic microwave background has been the dominant source of information about these perturbations. Large-scale structure is, however, from where drastic improvements should originate. In this paper, we explain the theoretical motivations for pursuing such measurements and the challenges that lie ahead. In particular, we discuss and identify theoretical targets regarding the measurement of primordial non-Gaussianity. We argue that when quantified in terms of the local (equilateral) template amplitude f$loc\atop{NL}$ (f$eq\atop{NL}$), natural target levels of sensitivity are Δf$loc, eq\atop{NL}$ ≃ 1. We highlight that such levels are within reach of future surveys by measuring 2-, 3- and 4-point statistics of the galaxy spatial distribution. This paper summarizes a workshop held at CITA (University of Toronto) on October 23-24, 2014.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.680

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 head0.017
GPT teacher head0.277
Teacher spread0.260 · 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 designObservational
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

Citations103
Published2014
Admission routes2
Has abstractyes

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