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SPT-3G D1: Compton- $y$ maps using data from the SPT-3G and Planck surveys

This paper presents high-resolution, low-noise thermal Sunyaev-Zel'dovich Compton- $y$ maps derived from two years of SPT-3G data combined with Planck observations, validating their robustness through statistical analyses and establishing them as a powerful cosmological tool for studying the thermodynamic state of baryons and large-scale structure.

Original authors: A. S. Maniyar, F. Bianchini, W. L. K. Wu, S. Raghunathan, A. J. Anderson, B. Ansarinejad, M. Archipley, L. Balkenhol, D. R. Barron, P. S. Barry, K. Benabed, A. N. Bender, B. A. Benson, L. E. Bleem, S.

Published 2026-02-13

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: A. S. Maniyar, F. Bianchini, W. L. K. Wu, S. Raghunathan, A. J. Anderson, B. Ansarinejad, M. Archipley, L. Balkenhol, D. R. Barron, P. S. Barry, K. Benabed, A. N. Bender, B. A. Benson, L. E. Bleem, S. Bocquet, F. R. Bouchet, L. Bryant, E. Camphuis, M. G. Campitiello, J. E. Carlstrom, J. Carron, C. L. Chang, P. Chaubal, P. M. Chichura, A. Chokshi, T. -L. Chou, A. Coerver, T. M. Crawford, C. Daley, T. de Haan, K. R. Dibert, M. A. Dobbs, M. Doohan, A. Doussot, D. Dutcher, W. Everett, C. Feng, K. R. Ferguson, N. C. Ferree, K. Fichman, A. Foster, S. Galli, A. E. Gambrel, A. K. Gao, R. W. Gardner, F. Ge, N. Goeckner-Wald, R. Gualtieri, F. Guidi, S. Guns, N. W. Halverson, E. Hivon, A. Y. Q. Ho, G. P. Holder, W. L. Holzapfel, J. C. Hood, A. Hryciuk, N. Huang, T. Jhaveri, F. Kéruzoré, A. R. Khalife, L. Knox, M. Korman, K. Kornoelje, C. -L. Kuo, K. Levy, Y. Li, A. E. Lowitz, C. Lu, G. P. Lynch, T. J. Maccarone, E. S. Martsen, F. Menanteau, M. Millea, J. Montgomery, Y. Nakato, T. Natoli, G. I. Noble, Y. Omori, A. Ouellette, Z. Pan, P. Paschos, K. A. Phadke, A. W. Pollak, K. Prabhu, W. Quan, M. Rahimi, A. Rahlin, C. L. Reichardt, M. Rouble, J. E. Ruhl, E. Schiappucci, A. C. Silva Oliveira, A. Simpson, J. A. Sobrin, A. A. Stark, J. Stephen, C. Tandoi, B. Thorne, C. Trendafilova, C. Umilta, J. D. Vieira, A. G. Vieregg, A. Vitrier, Y. Wan, N. Whitehorn, M. R. Young, J. A. Zebrowski

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: Cleaning Up the Cosmic Static

Imagine the universe is a giant, noisy radio station. For decades, astronomers have been trying to tune into a specific, faint signal: the Cosmic Microwave Background (CMB). This is the "afterglow" of the Big Bang, the oldest light in the universe.

However, tuning into this station is like trying to hear a whisper in a crowded stadium. There are all kinds of "noise" drowning out the signal:

Galactic Dust: Like static from a nearby storm.
Radio Sources: Like people shouting in the stands.
The Cosmic Infrared Background (CIB): Like a constant hum from a billion distant, dusty galaxies.
The Thermal Sunyaev-Zel'dovich (tSZ) Effect: This is the signal we actually want to find. It's not a whisper; it's a specific "echo" created when hot gas in galaxy clusters bumps into the ancient light, giving it a little kick.

The Goal of this Paper:
The team behind the SPT-3G (a super-powerful telescope at the South Pole) and the Planck satellite wanted to create a super-clear map of these "echoes" (the tSZ effect). They wanted to see where the hot gas is, which helps us understand how galaxy clusters form and where the "missing" normal matter (baryons) in the universe is hiding.

The Problem: Two Telescopes, Two Strengths

To get a clear picture, you need two different tools, but neither is perfect on its own:

Planck (The Satellite): Think of this as a wide-angle lens. It can see the entire sky, so it's great at seeing the big, fuzzy, large-scale structures. But its vision is a bit blurry (low resolution), and it misses the tiny details.
SPT-3G (The South Pole Telescope): Think of this as a high-powered microscope. It zooms in on a specific patch of the southern sky with incredible sharpness (high resolution). But it can only see a small area, and it has a blind spot: it can't see the very largest, slow-moving waves of data because of how it scans the sky.

The Solution:
The researchers combined data from both. It's like taking a high-resolution photo of a face (SPT-3G) and stitching it into a panoramic landscape photo (Planck). The result is a map that is both sharp and covers a wide area.

The Method: The "Smart Mixer"

The hardest part of the job was separating the "echo" (tSZ) from the noise. The team used a technique called Linear Combination (LC).

Imagine you are a DJ mixing a song. You have several audio tracks:

Track A: The tSZ signal (what you want).
Track B: The CMB (the background music).
Track C: The CIB (the annoying hum).
Track D: Instrument noise (static).

The team wrote a computer algorithm that acts like a smart mixer. It assigns a "volume knob" (a weight) to each frequency channel.

It turns up the volume on the frequencies where the tSZ signal is loud.
It turns down (or mutes) the frequencies where the noise and other signals are loud.

They created three different "mixes" (maps) for different purposes:

Minimum-Variance (MV): The "Best Overall" mix. It tries to get the clearest signal possible, even if a tiny bit of noise leaks in.
CMB-Deprojected: The "No Background Music" mix. It aggressively mutes the CMB to ensure we aren't accidentally hearing the Big Bang's echo instead of the galaxy clusters.
CIB-Deprojected: The "No Hum" mix. It aggressively mutes the Cosmic Infrared Background (the dusty galaxies) to ensure we aren't seeing dust instead of hot gas.

The Results: A Crystal Clear Map

The paper presents these new maps, which are the highest-resolution maps of their kind ever made for this part of the sky.

What did they find?

Galaxy Clusters: The maps are dotted with bright red spots. These are massive galaxy clusters. The hot gas inside them is so dense it's clearly visible in the new maps.
Validation: They tested the maps by stacking them over known cluster locations. The signal was strong and consistent, proving the maps work.
The Trade-off: They found that if you try to remove too much noise (like the CIB), you sometimes accidentally remove a little bit of the real signal or introduce new mathematical quirks. So, they recommend using different maps for different jobs:
- Use the MV map if you want to study individual clusters in detail (lowest noise).
- Use the CIB-deprojected map if you want to compare the gas to other galaxy surveys (less contamination from dust).

Why Does This Matter?

Think of the universe as a giant, invisible ocean of hot gas. For a long time, we could only see the "islands" (galaxies) floating in it.

These new maps allow astronomers to see the water itself.

Missing Baryons: We know there is more "normal" matter in the universe than we can account for. This map helps find that missing matter, which is hiding in the hot gas between galaxies.
Cosmic History: By measuring the pressure of this gas, we can learn how the universe has heated up and evolved over billions of years.
Future Tools: These maps are now public. Other scientists can use them to cross-reference with new telescopes (like the upcoming Euclid or SPHEREx missions) to build a 3D model of the universe's structure.

In a Nutshell

This paper is about cleaning up a cosmic photo. By combining a wide-angle satellite view with a high-powered ground-based microscope, and using a smart computer algorithm to filter out the static, the team has created the sharpest, most detailed map yet of the hot gas in our universe. It's a new tool that will help us understand where the universe's matter is hiding and how it has changed since the beginning of time.

1. Problem Statement

The primary goal of the paper is to isolate and reconstruct the thermal Sunyaev-Zel'dovich (tSZ) effect from Cosmic Microwave Background (CMB) maps. The tSZ effect, caused by the inverse Compton scattering of CMB photons off hot electrons in galaxy clusters and the intergalactic medium, is a crucial tracer of the thermodynamic state of baryons and large-scale structure.

However, extracting a clean tSZ signal (parameterized as the Compton- $y$ parameter) is challenging due to:

Foreground Contamination: The tSZ signal overlaps spectrally and spatially with the primary CMB, the Cosmic Infrared Background (CIB), Galactic dust, and radio sources.
Data Limitations:
- SPT-3G: Offers high angular resolution and sensitivity on small scales but lacks coverage of large angular scales (low multipoles, $\ell < 300$ ) due to high-pass filtering in map-making and has limited frequency coverage.
- Planck: Provides full-sky coverage and fills in large-scale modes but has lower angular resolution and higher noise on small scales.
Modeling Complexity: The spectral energy distribution (SED) of the CIB is complex and difficult to model with a single template, leading to potential residuals in component separation.

2. Methodology

The authors employ a Harmonic-Space Linear Combination (LC) pipeline to combine data from the South Pole Telescope (SPT-3G) and the Planck satellite.

Data Sources:
- SPT-3G D1: Two years of data (2019–2020) covering $\sim1500$ deg $^2$ in the southern sky at 95, 150, and 220 GHz.
- Planck PR3: Full-sky data at 100, 143, 217, and 353 GHz (High Frequency Instrument).
Algorithm:
- 2D Harmonic-Space Weights: Unlike traditional methods that average over azimuthal modes ( $m$ ), this work computes weights $w_{\ell m}$ for each harmonic mode. This allows the algorithm to adapt to anisotropic noise (e.g., scan-direction noise in SPT) and localized foregrounds.
- Covariance Matrix: Constructed using Agora simulations (which include tSZ, kSZ, CIB, and radio sources) rather than the data itself. This approach avoids the "ILC bias" (where signal and noise correlations artificially suppress the true signal) but strictly classifies the method as a Linear Combination (LC) rather than an Internal Linear Combination (ILC).
- Frequency Response Modeling:
  - CMB/kSZ: Modeled as having a unit response.
  - tSZ: Modeled with the standard relativistic correction formula.
  - CIB: Modeled as a modified blackbody. The authors performed a grid search on simulations to determine optimal dust temperature ( $T_d$ ) and emissivity index ( $\beta_d$ ). They selected $T_d = 10.0$ K and $\beta_d = 2.0$ based on physical consistency with external CIB maps, prioritizing this over strict simulation minimization.
Map Variants: Three types of Compton- $y$ $y$ maps were produced, all smoothed to a $1'.4$ $1^{'} .4$ beam:
1. Minimum-Variance (MV): Optimized for lowest noise.
2. CMB-deprojected: Constrains the solution to have zero response to the CMB.
3. CIB-deprojected: Constrains the solution to have zero response to the CIB (using the modified blackbody model).
Missing Modes Handling: A harmonic-space low-pass filter is applied to modes with $m \lesssim 220$ and high $\ell$ to suppress noise where SPT data is missing and Planck resolution is insufficient.

3. Key Contributions

High-Resolution Compton- $y$ Maps: The release of the highest-resolution Compton- $y$ maps to date over the SPT-3G survey area ( $N_{side}=8192$ , $\ell_{max}=13000$ ).
Complementary Data Fusion: Successfully demonstrated a method to merge high-resolution ground-based data (SPT-3G) with full-sky space-based data (Planck) to recover both large-scale structure and small-scale cluster details.
Robust Validation: Validated the maps using multiple independent statistical techniques:
- Auto- and cross-power spectra.
- Cross-correlation with external CIB maps (Planck 545/857 GHz).
- Cross-correlation with galaxy density maps (unWISE).
- Stacked aperture photometry on galaxy clusters.
Bias Analysis: Provided a detailed analysis of the trade-offs between noise minimization and foreground suppression, specifically highlighting the challenges in modeling the CIB SED.

4. Results

Power Spectra:
- The MV maps show the lowest noise but retain significant foreground residuals.
- The CIB-deprojected maps show significantly reduced correlation with external CIB maps (Planck 545 GHz), confirming effective foreground suppression.
- Cross-spectra between split data (e.g., CMB-deprojected $\times$ CIB-deprojected) provide a robust estimate of the true tSZ power spectrum by removing noise bias and suppressing the two largest contaminants (CMB and CIB).
Cross-Correlations with unWISE:
- The MV and CMB-deprojected maps show strong correlation with high-redshift (red) unWISE galaxies, indicating residual CIB contamination (as CIB peaks at $z \sim 1-2$ ).
- The CIB-deprojected maps show reduced correlation with red galaxies but maintain correlation with low-redshift (blue) galaxies, consistent with the expected tSZ signal originating from low-redshift massive halos.
Cluster Stacking:
- Stacked profiles around 4,568 SPT-3G detected clusters show positive signals across all radii.
- The inclusion of Planck data successfully restored the large-scale modes missing in SPT-only maps (which showed negative tails due to filtering).
- High-pass filtered profiles ( $\ell > 2000$ ) show that all map variants (MV, CMB-deproj, CIB-deproj) converge to the same small-scale tSZ signal, confirming the robustness of the cluster detection.

5. Significance and Future Applications

Cosmological Tool: These maps serve as a critical resource for measuring the tSZ power spectrum and bispectrum, which constrain cosmological parameters and the thermal history of the universe.
Baryon Physics: The maps enable detailed studies of the "missing baryons" in filaments and the thermodynamic state of gas in galaxy groups and clusters.
Cross-Correlation Science: The maps are ready for cross-correlation with upcoming large-scale structure surveys (e.g., Euclid, SPHEREx, Dark Energy Survey) to study the interplay between dark matter, baryons, and galaxy formation.
Recommendations for Users:
- For tSZ power spectrum estimation: Use the CMB-deprojected $\times$ CIB-deprojected split cross-spectrum.
- For cross-correlations with LSS tracers (especially high-z or CIB-sensitive): Use the CIB-deprojected map.
- For individual object analysis (e.g., cluster pressure profiles): Use the MV map for the lowest total variance.

The paper concludes that while CIB deprojection is challenging due to SED modeling uncertainties, the developed pipeline provides a suite of complementary maps that significantly advance the precision of tSZ studies. Future work will extend these methods to the full SPT-3G dataset and upcoming experiments like the Simons Observatory.