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Project Supported by
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NSF, National Science Foundation
KICP, Kavli Institute for Cosmological Physics
USAP, United States Antarctic Program
Antarctic Support Contract
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 | Overview |
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This page provides data products associated with the thermal Sunyaev-Zeldovich (tSZ) power spectrum measurement using observations from the SPTpol/SPT-3G/Herschel-SPIRE surveys over roughly 100 square degrees described in Raghunathan et al. 2026 (arXiv: 2602.10107). The tSZ power spectra are presented over the multipole range 500 < ℓ <= 5000.
If you have any questions regarding this data set or its use, please contact Srini Raghunathan (sriniraghuna_at_gmail_dot_com).
Please check this jupyter notebook to help read the maps and the bandpowers:
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| Maps |
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Below are links to FITS files containing maps of the tSZ effect reconstructed using observations from multiple frequency bands using SPT and Herschel-SPIRE surveys. Specifically, the observations include 150 GHz data from SPTpol (2012-2016 observing season); 95, 150, and 220 GHz from SPT-3G (2019-20 observing season); and 600 and 857 GHz data from Herschel-SPIRE survey.
The patch size is 100 sqaure degrees and the white noise levels correspond to 4.5, 3, and 16 uK-arcmin at 95, 150, and 220 GHz bands. The tSZ maps are reconstructed using the harmonic space linear combination technique which combines information from the all the above freqeuncy bands to either reduce (a) the overall variacne from noise and foreground variance called the minimum-variance (MV) technique and (b) the contamination from a given foreground signal which in our case is the cosmic infrarred background (CIB) minimised (CIB-min) map.
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(Fig. 1 of Raghuanthan et al. 2026.) The above image shows the MV tSZ map reconstruction. The top right inset panel shows the stack of galaxy clusters and the bottom right inset panel shows a zoom-in of one of the locations with multiple galaxy clusters seen as red dots.
Compton-y maps and masks:
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| tSZ bandpowers |
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Fig. 4 of Raghuanthan et al. 2026 showing the yy bandpowers from the CIB-min map show as green along with other measurements from ACT/Planck, and SPT as well as the curves from FLAMINGO simulations (dotted: LS8 and dashed: LS8+fgas-8σ). In the below files, we release the bandpowers along with the covariance and the bandpower window function.
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Bandpowers window function:
The spectra are binned with uniform weighting in Cℓ:
To bin theory Cl, you can do: cl_theory_binned = np.dot( bpwf, cl_theory_unbinned)
Bandpowers: Columns are ℓ, Dℓ[1012], δDℓ[1012]. Note that none of these have the noise bias.
Bandpower covariance matrices: Sum of both statistical and systematic errros.
To get the bandpower errors δDℓ[1012], you can do: np.sqrt( np.diag( dl_yy_cov ) )
Effective beam and filter. Please look into read_maps_masks_bandpowers_cov.html for details about beam and the filters.
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If you have any questions regarding these data products, please contact Srini Raghunathan (sriniraghuna_at_gmail_dot_com).
v1.0: February 10, 2026 - Initial version released with the paper.
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hyde.uchicago.edu