High-Resolution CMB Lensing Reconstruction with Deep Learning

Li, Peikai; Onur, Ipek Ilayda; Dodelson, Scott; Chaudhari, Shreyas

doi:10.48550/arxiv.2205.07368

Cited by 3 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the methods of ML have been applied successfully to the CMB component separation (Petroff et al 2020;Casas et al 2022;Wang et al 2022;Yan et al 2023). In addition, previous works (Caldeira et al 2019;Guzman & Meyers 2021Heinrich et al 2022;Li et al 2022) also employ the ML approach for the reconstruction of CMB lensing and cosmic polarization rotation maps. Caldeira et al 2019 also use the ML method to derive delensing E maps for observed CMB Q/U maps.…”

Section: Introductionmentioning

confidence: 99%

Delensing of Cosmic Microwave Background Polarization with Machine Learning

Yan

Wang

et al. 2023

ApJS

View full text Add to dashboard Cite

Primordial B-mode detection is one of the main goals of next-generation cosmic microwave background (CMB) experiments. Primordial B-modes are a unique signature of primordial gravitational waves (PGWs). However, the gravitational interaction of CMB photons with large-scale structures will distort the primordial E modes, adding a lensing B-mode component to the primordial B-mode signal. Removing the lensing effect (“delensing”) from observed CMB polarization maps will be necessary to improve the constraint of PGWs and obtain a primordial E-mode signal. Here, we introduce a deep convolutional neural network model named multi-input multi-output U-net (MIMO-UNet) to perform CMB delensing. The networks are trained on simulated CMB maps with size 20° × 20°. We first use MIMO-UNet to reconstruct the unlensing CMB polarization (Q and U) maps from observed CMB maps. The recovered E-mode power spectrum exhibits excellent agreement with the primordial EE power spectrum. The recovery of the primordial B-mode power spectrum for noise levels of 0, 1, and 2 μK-arcmin is greater than 98% at the angular scale of ℓ < 150. We additionally reconstruct the lensing B map from observed CMB maps. The recovery of the lensing B-mode power spectrum is greater than roughly 99% at the scales of ℓ > 200. We delens the observed B-mode power spectrum by subtracting the reconstructed lensing B-mode spectrum. The recovery of tensor B-mode power spectrum for noise levels of 0, 1, and 2 μK-arcmin is greater than 98% at the angular scales of ℓ < 120. Even at ℓ = 160, the recovery of tensor B-mode power spectrum is still around 71%.

show abstract

Section: Introductionmentioning

confidence: 99%

Delensing of Cosmic Microwave Background Polarization with Machine Learning

Yan

Wang

et al. 2023

ApJS

View full text Add to dashboard Cite

show abstract

“…Their results, however, are noisedependent, and the power spectrum of the κ field is difficult to accurately reconstruct in the presence of nonnegligible noise. Li et al (2022) employed a generative adversarial network (GAN) to precisely reconstruct the κ power spectrum despite the presence of noise. However, the GAN model's reconstruction of the κ map contains more noise than the ResUnet model.…”

Section: Introductionmentioning

confidence: 99%

Lensing Reconstruction from the Cosmic Microwave Background Polarization with Machine Learning

Yan

Wang

et al. 2023

ApJ

View full text Add to dashboard Cite

The lensing effect of the cosmic microwave background (CMB) is a powerful tool for our study of the distribution of matter in the universe. The quadratic estimator (QE) method, which is widely used to reconstruct lensing potential, has been known to be suboptimal for the low noise level polarization data from next-generation CMB experiments. To improve the performance of the reconstruction, other methods, such as the maximum-likelihood estimator and machine-learning algorithms, have been developed. In this work, we present a deep convolutional neural network model named the Residual Dense Local Feature U-net (RDLFUnet) for reconstructing the CMB lensing convergence field. By simulating lensed CMB data with different noise levels to train and test network models, we find that for noise levels less than 5 μK-arcmin, RDLFUnet can recover the input gravitational potential with a higher signal-to-noise ratio than the previous deep-learning and traditional QE methods at almost the entire observation scale.

show abstract

Denoising diffusion delensing: reconstructing the non-Gaussian CMB lensing potential with diffusion models

Flöss,

Coulton,

Duivenvoorden

et al. 2024

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

Optimal extraction of cosmological information from observations of the Cosmic Microwave Background critically relies on our ability to accurately undo the distortions caused by weak gravitational lensing. In this work, we demonstrate the use of denoising diffusion models in performing Bayesian lensing reconstruction. We show that score-based generative models can produce accurate, uncorrelated samples from the CMB lensing convergence map posterior, given noisy CMB observations. To validate our approach, we compare the samples of our model to those obtained using established Hamiltonian Monte Carlo methods, which assume a Gaussian lensing potential. We then go beyond this assumption of Gaussianity, and train and validate our model on non-Gaussian lensing data, obtained by ray-tracing N-body simulations. We demonstrate that in this case, samples from our model have accurate non-Gaussian statistics beyond the power spectrum. The method provides an avenue towards more efficient and accurate lensing reconstruction, that does not rely on an approximate analytic description of the posterior probability. The reconstructed lensing maps can be used as an unbiased tracer of the matter distribution, and to improve delensing of the CMB, resulting in more precise cosmological parameter inference.

show abstract

High-Resolution CMB Lensing Reconstruction with Deep Learning

Cited by 3 publications

References 29 publications

Delensing of Cosmic Microwave Background Polarization with Machine Learning

Delensing of Cosmic Microwave Background Polarization with Machine Learning

Lensing Reconstruction from the Cosmic Microwave Background Polarization with Machine Learning

Denoising diffusion delensing: reconstructing the non-Gaussian CMB lensing potential with diffusion models

Contact Info

Product

Resources

About