A Measurement of the CMB Temperature Power Spectrum and Constraints on Cosmology from the SPT-3G 2018 TT/TE/EE Data Set
Bibliographic record
Abstract
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)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".