Delensing of Cosmic Microwave Background Polarization with Machine Learning

Yan, Ye-Peng; Wang, Guo-Jian; Li, Siyu; Xia, Jun-Qing

doi:10.3847/1538-4365/acd2ce

Cited by 3 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reducing statistical uncertainties by subtracting off the lensing B-mode polarization or equivalent methods -a process usually referred to as "delensing" -will hence be critical for improving the constraints on r (e.g., refs. [28][29][30][31][32][33][34][35][36][37][38][39][40]).…”

Section: Introductionmentioning

confidence: 99%

LiteBIRD science goals and forecasts: improving sensitivity to inflationary gravitational waves with multitracer delensing

Namikawa,

Lonappan,

Baccigalupi

et al. 2024

J. Cosmol. Astropart. Phys.

View full text Add to dashboard Cite

We estimate the efficiency of mitigating the lensing B-mode polarization, the so-called delensing, for the LiteBIRD experiment with multiple external data sets of lensing-mass tracers. The current best bound on the tensor-to-scalar ratio, r, is limited by lensing rather than Galactic foregrounds. Delensing will be a critical step to improve sensitivity to r as measurements of r become more and more limited by lensing. In this paper, we extend the analysis of the recent LiteBIRD forecast paper to include multiple mass tracers, i.e., the CMB lensing maps from LiteBIRD and CMB-S4-like experiment, cosmic infrared background, and galaxy number density from Euclid- and LSST-like survey. We find that multi-tracer delensing will further improve the constraint on r by about 20%. In LiteBIRD, the residual Galactic foregrounds also significantly contribute to uncertainties of the B-modes, and delensing becomes more important if the residual foregrounds are further reduced by an improved component separation method.

show abstract

Section: Introductionmentioning

confidence: 99%

LiteBIRD science goals and forecasts: improving sensitivity to inflationary gravitational waves with multitracer delensing

Namikawa,

Lonappan,

Baccigalupi

et al. 2024

J. Cosmol. Astropart. Phys.

View full text Add to dashboard Cite

show abstract

“…Reducing statistical uncertainties by subtracting off the lensing B-mode polarization or equivalent methods -a process usually referred to as "delensing" -will hence be critical for improving the constraints on r (e.g., Refs. [28][29][30][31][32][33][34][35][36][37][38][39][40]).…”

mentioning

confidence: 99%

Simons Observatory: Constraining inflationary gravitational waves with multitracer B -mode delensing

Namikawa¹,

Lizancos²,

Robertson³

et al. 2022

Phys. Rev. D

View full text Add to dashboard Cite

We introduce and validate a delensing framework for the Simons Observatory (SO), which will be used to improve constraints on inflationary gravitational waves by reducing the lensing noise in measurements of the B modes in CMB polarization. SO will initially observe CMB by using three small aperture telescopes and one large-aperture telescope. While polarization maps from small-aperture telescopes will be used to constrain inflationary gravitational waves, the internal CMB lensing maps used to delens will be reconstructed from data from the large-aperture telescope. Since lensing maps obtained from the SO data will be noise dominated on subdegree scales, the SO lensing framework constructs a template for lensinginduced B modes by combining internal CMB lensing maps with maps of the cosmic infrared background from Planck as well as galaxy density maps from the LSST survey. We construct a likelihood for constraining the tensor-to-scalar ratio r that contains auto and cross spectra between observed B modes and the lensing B-mode template. We test our delensing analysis pipeline on map-based simulations containing survey nonidealities, but that, for this initial exploration, does not include contamination from Galactic and extragalactic foregrounds. We find that the SO survey masking and inhomogeneous and atmospheric noise have very little impact on the delensing performance, and the r constraint becomes σðrÞ ≈ 0.0015 which is close to that obtained from the idealized forecasts in the absence of the Galactic foreground and is nearly a factor of 2 tighter than without delensing. We also find that uncertainties in the external large-scale structure tracers used in our multitracer delensing pipeline lead to bias much smaller than the 1σ statistical uncertainties.

show abstract

CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network

Yan,

Li,

Wang

et al. 2024

ApJS

View full text Add to dashboard Cite

In our previous study, we introduced a machine learning technique, namely Cosmic Microwave Background Foreground Subtraction with Convolutional Neural Networks (CMBFSCNN), for the removal of foreground contamination in cosmic microwave background (CMB) polarization data. This method was successfully employed on actual observational data from the Planck mission. In this study, we extend our investigation by considering the CMB lensing effect in simulated data and utilizing the CMBFSCNN approach to recover the CMB lensing B-mode power spectrum from multifrequency observational maps. Our method is first applied to simulated data with the performance of the CMB-S4 experiment. We achieve reliable recovery of the noisy CMB Q (or U) maps with a mean absolute difference of 0.016 ± 0.008 μK (or 0.021 ± 0.002 μK) for the CMB-S4 experiment. To address the residual instrumental noise in the foreground-cleaned map, we employ a “half-split maps” approach, where the entire data set is divided into two segments sharing the same sky signal but having uncorrelated noise. Using cross-correlation techniques between two recovered half-split maps, we effectively reduce instrumental noise effects at the power spectrum level. As a result, we achieve precise recovery of the CMB EE and lensing B-mode power spectra. Furthermore, we also extend our pipeline to full-sky simulated data with the performance of the LiteBIRD experiment. As expected, various foregrounds are cleanly removed from the foregrounds contamination observational maps, and recovered EE and lensing B-mode power spectra exhibit excellent agreement with the true results. Finally, we discuss the dependency of our method on the foreground models.

show abstract

Delensing of Cosmic Microwave Background Polarization with Machine Learning

Cited by 3 publications

References 49 publications

LiteBIRD science goals and forecasts: improving sensitivity to inflationary gravitational waves with multitracer delensing

LiteBIRD science goals and forecasts: improving sensitivity to inflationary gravitational waves with multitracer delensing

Simons Observatory: Constraining inflationary gravitational waves with multitracer B -mode delensing

CMBFSCNN: Cosmic Microwave Background Polarization Foreground Subtraction with a Convolutional Neural Network

Contact Info

Product

Resources

About