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Record W4311427929 · doi:10.1103/physrevd.108.023510

A Measurement of the CMB Temperature Power Spectrum and Constraints on Cosmology from the SPT-3G 2018 TT/TE/EE Data Set

2022· preprint· en· W4311427929 on OpenAlexfundno aff
L. Balkenhol, D. Dutcher, A. Spurio Mancini, Alexandre Doussot, K. Benabed, S. Galli, P.A.R. Ade, A. J. Anderson, B. Ansarinejad, M. Archipley, A. N. Bender, B. A. Benson, F. Bianchini, L. E. Bleem, F. R. Bouchet, L. Bryant, E. Camphuis, J. E. Carlstrom, T. Cecil, Chu-En Chang, P. Chaubal, P. M. Chichura, T.-L. Chou, A. Coerver, T. M. Crawford, A. Cukierman, C. Daley, T. de Haan, K.R. Dibert, M. Dobbs, W. Everett, C. Feng, Ken Ferguson, A. Foster, A. E. Gambrel, R. W. Gardner, N. Goeckner-Wald, R. Gualtieri, F. Guidi, S. Guns, N. W. Halverson, E. Hivon, G. P. Holder, W. L. Holzapfel, J. C. Hood, N. Huang, L. Knox, M. Korman, C.-L. Kuo, A. T. Lee, A. E. Lowitz, C. Lu, M. Millea, J. Montgomery, Y. Nakato, T. Natoli, G. I. Noble, V. Novosad, Y. Omori, S. Padin, Z. Pan, P. Paschos, K. Prabhu, Wei Quan, M. Rahimi, A. Rahlin, C. L. Reichardt, M. Rouble, J. E. Ruhl, E. Schiappucci, G. Smecher, J. A. Sobrin, A. A. Stark, J. Stephen, A. Suzuki, C. Tandoi, K. L. Thompson, B. Thorne, C. Tucker, C. Umiltà, J. D. Vieira, G. Wang, N. Whitehorn, W.L.K. Wu, V. Yefremenko, M. R. Young, J. A. Zebrowski

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsnot available
FundersFermilabHigh Energy PhysicsInstitut National de Physique Nucléaire et de Physique des ParticulesOffice of ScienceScience and Technology Facilities CouncilAlfred P. Sloan FoundationNatural Sciences and Engineering Research Council of CanadaArgonne National LaboratoryU.S. Department of EnergyEuropean CommissionCentre National d’Etudes SpatialesMcGill UniversityUniversity of ChicagoNational Science Foundation
KeywordsCosmic microwave backgroundPhysicsSpectral densityMultipole expansionAstrophysicsCosmologyLambdaBaryon acoustic oscillationsNeutrinoBaryonParticle physicsDark energyQuantum mechanicsStatisticsAnisotropy

Abstract

fetched live from OpenAlex

We present a sample-variance-limited measurement of the temperature power spectrum ($TT$) of the cosmic microwave background (CMB) using observations of a $\sim\! 1500 \,\mathrm{deg}^2$ field made by SPT-3G in 2018. We report multifrequency power spectrum measurements at 95, 150, and 220GHz covering the angular multipole range $750 \leq \ell < 3000$. We combine this $TT$ measurement with the published polarization power spectrum measurements from the 2018 observing season and update their associated covariance matrix to complete the SPT-3G 2018 $TT/TE/EE$ data set. This is the first analysis to present cosmological constraints from SPT $TT$, $TE$, and $EE$ power spectrum measurements jointly. We blind the cosmological results and subject the data set to a series of consistency tests at the power spectrum and parameter level. We find excellent agreement between frequencies and spectrum types and our results are robust to the modeling of astrophysical foregrounds. We report results for $\Lambda$CDM and a series of extensions, drawing on the following parameters: the amplitude of the gravitational lensing effect on primary power spectra $A_\mathrm{L}$, the effective number of neutrino species $N_{\mathrm{eff}}$, the primordial helium abundance $Y_{\mathrm{P}}$, and the baryon clumping factor due to primordial magnetic fields $b$. We find that the SPT-3G 2018 $T/TE/EE$ data are well fit by $\Lambda$CDM with a probability-to-exceed of $15\%$. For $\Lambda$CDM, we constrain the expansion rate today to $H_0 = 68.3 \pm 1.5\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ and the combined structure growth parameter to $S_8 = 0.797 \pm 0.042$. The SPT-based results are effectively independent of Planck, and the cosmological parameter constraints from either data set are within $<1\,\sigma$ of each other. (abridged)

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.190
Teacher spread0.114 · 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.

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

Citations9
Published2022
Admission routes1
Has abstractyes

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