Towards constraining cosmological parameters with SPT-3G observations of 25% of the sky

TL;DR

SPT-3G Ext-10k forecasts show that analyzing 13 patch fields yields minimal information loss for $\Lambda$CDM constraints while enabling strong tests of Hubble-tension solutions, including Early Dark Energy and varying electron mass, especially when combined with Planck data. The authors develop a realistic, differentiable TT/TE/EE likelihood and a dedicated lensing likelihood, with a patch-based covariance that accounts for mask effects and TOD filtering. Results indicate that Ext-10k can outperform Planck for several parameters and dramatically improve extended-model constraints, with FoMs increasing by orders of magnitude in some cases. The work demonstrates the value of high-resolution, regionally tailored CMB observations and provides publicly available likelihood tools to facilitate future cosmological analyses.

Abstract

The South Pole Telescope (SPT), using its third-generation camera, SPT-3G, is conducting observations of the cosmic microwave background (CMB) in temperature and polarization across approximately 10 000 deg$^2$ of the sky at 95, 150, and 220 GHz. This comprehensive dataset should yield stringent constraints on cosmological parameters. In this work, we explore its potential to address the Hubble tension by forecasting constraints from temperature, polarization, and CMB lensing on Early Dark Energy (EDE) and the variation in electron mass in spatially flat and curved universes. For this purpose, we investigate first whether analyzing the distinct SPT-3G observation fields independently, as opposed to as a single, unified region, results in a loss of information relevant to cosmological parameter estimation. We develop a realistic temperature and polarization likelihood pipeline capable of analyzing these fields in these two ways, and subsequently forecast constraints on cosmological parameters. Our findings indicate that any loss of constraining power from analyzing the fields separately is primarily concentrated at low multipoles ($\ell$ < 50) and the overall impact on the relative uncertainty on standard $Λ$CDM parameters is minimal (< 3%). Our forecasts suggest that SPT-3G data should improve by more than a factor of 300 and 3000 the Figure of Merit (FoM) of the EDE and the varying electron mass models, respectively, when combined with Planck data. The likelihood pipeline developed and used in this work is made publicly available online.

Figures (10)

• Figure 1: Observed sky area with joint (left) and separated (right) masks used to perform either a global or a separate analysis of the Ext-10k survey, which spans declinations from $-20^\circ$ to $-80^\circ$. The Main field (in green) is denoted by M, the three Summer fields (in yellow) are Sa, Sb, and Sc, and the nine Wide fields (in blue) are named from Wa to Wi. The masks are shown in equatorial coordinates and are superposed on the Commander dust map produced in the component separation of the Planck analysis planck18-4. The dashed lines are $30^\circ$ intervals between meridians and between parallels. The vertical and horizontal solid lines correspond to $\text{RA}=0$ and $\text{dec}=0$, respectively.
• Figure 2: Expected temperature noise power spectra for the Wide, Summer c, and Main fields for the three auto-frequencies $95\,\text{GHz}\times95\,\text{GHz}$, $150\,\text{GHz}\times150\,\text{GHz}$, and $220\,\text{GHz}\times220\,\text{GHz}$. They include the transfer function shown in Figure \ref{['fig_tf']} and the pixel window function.
• Figure 3: Transfer function used in our TT/TE/EE analysis. This function describes the effect of the high-pass and low-pass filtering of the data on the power spectrum and the covariance matrix.
• Figure 4: Relative difference in the diagonal of the band-power covariance matrix between Ext-10k separate and joint analyses. We show TT and EE $95\,\text{GHz}\times95\,\text{GHz}$ auto-frequency with a bin size $\Delta\ell=400$ for $\ell_{\text{min}}=350$. The two diagonals differ by $\sim5\%$ which corresponds to the 4.8% difference in sky fraction between the two cases due to the apodization of the individual masks in the $\text{Ext}10\text{k}_\mathrm{sep}$ analysis.
• Figure 5: Left: Relative difference in the parameter covariance matrix between Ext-10k separate and joint analyses. Analyzing the fields individually contributes to a $\sim5\%$ increase of parameter variance. We do not show $\tau$ since it is prior driven. Right: Parameter correlation matrix for the Ext-10k separate analysis obtained from Fisher forecasting.