4.6 Article

A new approach for the statistical denoising of Planck interstellar dust polarization data

Related references

Note: Only part of the references are listed.
Article Astronomy & Astrophysics

Classification of Magnetohydrodynamic Simulations Using Wavelet Scattering Transforms

Andrew K. Saydjari et al.

Summary: This paper demonstrates the sensitivity of wavelet scattering transform (WST) combined with linear discriminant analysis (LDA) to non-Gaussian structures in 2D interstellar medium dust maps. The WST-LDA method shows a high true positive rate in classifying magnetohydrodynamic (MHD) turbulence simulations and is robust to observational artifacts. Further applications in 3D and potential use on all-sky dust maps for extracting hydrodynamic parameters are discussed.

ASTROPHYSICAL JOURNAL (2021)

Article Astronomy & Astrophysics

ForSE: A GAN-based Algorithm for Extending CMB Foreground Models to Subdegree Angular Scales

Nicoletta Krachmalnicoff et al.

Summary: ForSE is a novel Python package that uses generative adversarial neural networks to simulate realistic and non-Gaussian foreground radiation at subdegree angular scales for cosmic microwave background experiments, with important applications in estimating foreground contamination for future CMB experiments.

ASTROPHYSICAL JOURNAL (2021)

Article Astronomy & Astrophysics

A generative model of galactic dust emission using variational autoencoders

Ben Thorne et al.

Summary: This study employs a machine learning approach, specifically using variational autoencoders, to infer the statistical properties of interstellar dust emissions from observational data, demonstrating the ability to simulate new samples, provide fits, and produce constrained realizations. The study finds that variational autoencoders are easier to train than generative adversarial networks and are better suited for Bayesian inference.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2021)

Article Mathematics, Applied

Maximum entropy models from phase harmonic covariances

Sixin Zhang et al.

Summary: The covariance matrix of a stationary process X is diagonalized using a Fourier transform to construct Gaussian maximum entropy models; a family of phase harmonic covariance moments is introduced to capture non-Gaussian properties using complex phases; the use of Fourier transform and complex wavelet transform in capturing frequency dependencies and scale dependencies is discussed.

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS (2021)

Article Astronomy & Astrophysics

Cleaning our own dust: simulating and separating galactic dust foregrounds with neural networks

K. Aylor et al.

Summary: The study utilized DCGAN to create a model for interstellar dust emission intensity, allowing for separation of galactic foreground emission from CMB maps and quantification of uncertainty. By training neural networks with dust maps from the Planck satellite, the underlying CMB signal from dust-contaminated maps was successfully estimated.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2021)

Article Astronomy & Astrophysics

Planck 2018 results: III. High Frequency Instrument data processing and frequency maps

N. Aghanim et al.

ASTRONOMY & ASTROPHYSICS (2020)

Article Astronomy & Astrophysics

Planck 2018 results: XII. Galactic astrophysics using polarized dust emission

N. Aghanim et al.

ASTRONOMY & ASTROPHYSICS (2020)

Article Astronomy & Astrophysics

Planck 2018 results: IV. Diffuse component separation

Y. Akrami et al.

ASTRONOMY & ASTROPHYSICS (2020)

Article Astronomy & Astrophysics

Modeling the magnetized Local Bubble from dust data

V. Pelgrims et al.

ASTRONOMY & ASTROPHYSICS (2020)

Article Astronomy & Astrophysics

Impact of polarized galactic foreground emission on CMB lensing reconstruction and delensing of B-modes

Dominic Beck et al.

JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS (2020)

Article Astronomy & Astrophysics

Statistical description of dust polarized emission from the diffuse interstellar medium: A RWST approach

B. Regaldo-Saint Blancard et al.

ASTRONOMY & ASTROPHYSICS (2020)

Article Astronomy & Astrophysics

Planck 2018 results: XI. Polarized dust foregrounds

Y. Akrami et al.

ASTRONOMY & ASTROPHYSICS (2020)

Article Astronomy & Astrophysics

A new approach to observational cosmology using the scattering transform

Sihao Cheng et al.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2020)

Article Mathematics, Applied

Phase harmonic correlations and convolutional neural networks

Stephane Mallat et al.

INFORMATION AND INFERENCE-A JOURNAL OF THE IMA (2020)

Article Astronomy & Astrophysics

New interpretable statistics for large-scale structure analysis and generation

E. Allys et al.

PHYSICAL REVIEW D (2020)

Article Astronomy & Astrophysics

Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning

Matthew A. Petroff et al.

ASTROPHYSICAL JOURNAL (2020)

Article Astronomy & Astrophysics

The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM

E. Allys et al.

ASTRONOMY & ASTROPHYSICS (2019)

Article Astronomy & Astrophysics

Wiener filtering and pure ε/B decomposition of CMB maps with anisotropic correlated noise

Doogesh Kodi Ramanah et al.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2019)

Article Astronomy & Astrophysics

Mapping the Magnetic Interstellar Medium in Three Dimensions over the Full Sky with Neutral Hydrogen

S. E. Clark et al.

ASTROPHYSICAL JOURNAL (2019)

Article Astronomy & Astrophysics

Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: application to DES SV

N. Jeffrey et al.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2018)

Article Astronomy & Astrophysics

Fast sampling from Wiener posteriors for image data with dataflow engines

N. Jeffrey et al.

ASTRONOMY AND COMPUTING (2018)

Article Astronomy & Astrophysics

Mitigating Complex Dust Foregrounds in Future Cosmic Microwave Background Polarization Experiments

Brandon S. Hensley et al.

ASTROPHYSICAL JOURNAL (2018)

Article Astronomy & Astrophysics

Cosmological parameters, shear maps and power spectra from CFHTLenS using Bayesian hierarchical inference

Justin Alsing et al.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2017)

Article Computer Science, Artificial Intelligence

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2017)

Article Physics, Multidisciplinary

Joint Analysis of BICEP2/Keck Array and Planck Data

P. A. R. Ade et al.

PHYSICAL REVIEW LETTERS (2015)

Article Astronomy & Astrophysics

Matrix-free large-scale Bayesian inference in cosmology

Jens Jasche et al.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2015)

Article Astronomy & Astrophysics

Efficient Wiener filtering without preconditioning

F. Elsner et al.

ASTRONOMY & ASTROPHYSICS (2013)

Article Mathematics, Applied

Image Denoising Methods. A New Nonlocal Principle

A. Buades et al.

SIAM REVIEW (2010)

Article Astronomy & Astrophysics

Intermittency of interstellar turbulence: parsec-scale coherent structure of intense, velocity shear

P. Hily-Blant et al.

ASTRONOMY & ASTROPHYSICS (2009)

Article Astronomy & Astrophysics

DENSITY STUDIES OF MHD INTERSTELLAR TURBULENCE: STATISTICAL MOMENTS, CORRELATIONS AND BISPECTRUM

Blakesley Burkhart et al.

ASTROPHYSICAL JOURNAL (2009)

Article Astronomy & Astrophysics

Dissipative structures of diffuse molecular gas - III. Small-scale intermittency of intense velocity-shears

P. Hily-Blant et al.

ASTRONOMY & ASTROPHYSICS (2008)

Article Computer Science, Artificial Intelligence

Image denoising by sparse 3-D transform-domain collaborative filtering

Kostadin Dabov et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2007)

Article Astronomy & Astrophysics

Global, exact cosmic microwave background data analysis using Gibbs sampling

BD Wandelt et al.

PHYSICAL REVIEW D (2004)