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DOE OSTI2023

Optimizing foreground mitigation for CMB lensing with combined multifrequency and geometric methods

Here, a key challenge for current and upcoming cosmic microwave background lensing measurements is their sensitivity to biases from extragalactic foregrounds, such as Sunyaev-Zel'dovich signals or cosmic infrared background emission. Several methods have been developed to mitigate these lensing foreground biases, dividing broadly into multifrequency cleaning approaches and modifications to the estimator geometry, but how to optimally combine these methods has not yet been explored in detail. In this paper, we examine which combination of lensing foreground mitigation strategies is best able to reduce the impact of foreground contamination for a Simons Observatory-like experiment while preserving maximal signal-to-noise. Although the optimal combination obtained depends on whether bias reduction or variance reduction is prioritized and on whether polarization data is used, generally, we find that combinations involving both geometric (profile hardening, source hardening, or shear) and multifrequency (symmetric cleaning) methods perform best. For lensing power spectrum measurements from temperature (polarization and temperature), our combined estimator methods are able to reduce the bias below σ/4 or 0.3% (0.1%), a factor of 16 (30) lower than the standard quadratic estimator bias, at a modest signal-to-noise cost of only 18% (12%). In contrast, single-method foreground-mitigation approaches struggle to reduce the bias to a negligible level below σ/2 without incurring a large noise penalty. For upcoming and current experiments, our combined methods therefore represent a promising approach for making lensing measurements with negligible foreground bias.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Measurement of the CMB temperature power spectrum and constraints on cosmology from the SPT-3G 2018 T T , T E , and E E dataset

We present a sample-variance-limited measurement of the temperature power spectrum ( T T ) of the cosmic microwave background using observations of a ∼ 1500 deg 2 field made by the SPT-3G in 2018. We report multifrequency power spectrum measurements at 95, 150, and 220 GHz covering the angular multipole range 750 ≤ ℓ < 3000 . We combine this T T 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 T T / T E / E E dataset. This is the first analysis to present cosmological constraints from SPT T T , T E , and E E power spectrum measurements jointly. We blind the cosmological results and subject the dataset 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 Λ CDM and a series of extensions, drawing on the following parameters: the amplitude of the gravitational lensing effect on primary power spectra A L , the effective number of neutrino species N eff , the primordial helium abundance Y P , and the baryon clumping factor due to primordial magnetic fields b . We find that the SPT-3G 2018 T T / T E / E E data are well fit by Λ CDM with a probability to exceed of 15%. For Λ CDM , we constrain the expansion rate today to H 0 = 68.3 ± 1.5 km s - 1 Mpc - 1 and the combined structure growth parameter to S 8 = 0.797 ± 0.042 . The SPT-based results are effectively independent of Planck, and the cosmological parameter constraints from either dataset are within < 1 σ of each other. The addition of temperature data to the SPT-3G T E / E E power spectra improves constraints by 8–27% for each of the Λ CDM cosmological parameters. When additionally fitting A L , N eff , or N eff + Y P , the posteriors of these parameters tighten by 5–24%. In the case of primordial magnetic fields, complete T T / T E / E E power spectrum measurements are necessary to break the degeneracy between b and n s , the spectral index of primordial density perturbations. We report a 95% confidence upper limit from SPT-3G data of b < 1.0 . The cosmological constraints in this work are the tightest from SPT primary power spectrum measurements to date and the analysis forms a new framework for future SPT analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Exploring the growth index γ L : Insights from different CMB dataset combinations and approaches

In this study we investigate the growth index γ L , which characterizes the growth of linear matter perturbations, while analysing different cosmological datasets. We compare the approaches implemented by two different patches of the cosmological solver : and __. In our analysis we uncover a deviation of the growth index from its expected Λ CDM value of 0.55 when utilizing the Planck dataset, both in the case and in the __ case, but in opposite directions. This deviation is accompanied by a change in the direction of correlations with derived cosmological parameters. However, the incorporation of cosmic microwave background lensing data helps reconcile γ L with its Λ -cold dark matter value in both cases. Conversely, the alternative ground-based telescopes Atacama Cosmology Telescope and South Pole Telescope consistently yield growth index values in agreement with γ L = 0.55 . We conclude that the presence of the A lens problem in the Planck dataset contributes to the observed deviations, underscoring the importance of additional datasets in resolving these discrepancies. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Signal-preserving CMB component separation with machine learning

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained. Published by the American Physical Society 2025

McCarthy, Fiona (ORCID:0000000253893565)↗
DOE OSTI2021

3D printing of High Precision Radiation Filters for next generation Cosmic Background (CMB) studies (Final Report)

The terahertz (THz) spectral range is the part of the electromagnetic spectrum between 0.1–10 THz or 0.03–3 mm wavelength. For a long period, the terahertz band was relatively unexplored due to the unavailability of cost-effective and powerful sources. Due to the evolution of these devices in the mid-1980s, however, terahertz radiation has attracted much more attention. Since this part of the spectrum is between the infrared (IR) and microwave frequency ranges, the development of waveguides, filters, polarizers, lenses, and other optical components. Since terahertz waves to penetrate most dielectric materials offers the possibility of many applications. Since terahertz radiation can pass through organic tissue without causing damage, and it can be safely applied for biomedical applications. Radio astronomy and wireless communication are also fields with great interest in this spectral range. For example, terahertz waves could be used to detect cold bodies and debris in space or to increase data transmission using the larger bandwidth of the terahertz band.

79 ASTRONOMY AND ASTROPHYSICS↗