Paper Citation Record · LEDGER

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation

As of 17 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2412.10580.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.10580 v3

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Outbound references

Observation 55604187-f54b-4981-be60-cac7bff89f55 · outbound

This paper cites An All-Sky Analysis of Polarization in the Microwave Background.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation An All-Sky Analysis of Polarization in the Microwave Background

Reference 1

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This paper cites Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Parameter Results.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Parameter Results

Reference 2

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This paper cites Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Final Maps and Results.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Final Maps and Results

Reference 3

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Observation fc89b350-7ae5-425b-a6bf-23fb3c7dd965 · outbound

This paper cites Planck 2018 results. I. Overview and the cosmological legacy of Planck.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Planck 2018 results. I. Overview and the cosmological legacy of Planck

Reference 4

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Observation a7968d99-eef9-4101-89e0-ec3becd2a9ea · outbound

This paper cites Planck 2018 results. V. CMB power spectra and likelihoods.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Planck 2018 results. V. CMB power spectra and likelihoods

Reference 5

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Observation d426c8e5-c723-4916-8155-e65318a99870 · outbound

This paper cites The Atacama Cosmology Telescope: DR4 Maps and Cosmological Parameters.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation The Atacama Cosmology Telescope: DR4 Maps and Cosmological Parameters

Reference 6

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Observation efcb8d5a-2675-4353-9ed8-16b164521759 · outbound

This paper cites The Atacama Cosmology Telescope: A Measurement of the Cosmic Microwave Background Power Spectra at 98 and 150 GHz.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation The Atacama Cosmology Telescope: A Measurement of the Cosmic Microwave Background Power Spectra at 98 and 150 GHz

Reference 7

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This paper cites Measurements of the Temperature and E-Mode Polarization of the CMB from 500 Square Degrees of SPTpol Data.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Measurements of the Temperature and E-Mode Polarization of the CMB from 500 Square Degrees of SPTpol Data

Reference 8

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Observation c017b5bc-50eb-49b5-800c-d5bc7f6930e2 · outbound

This paper cites Hazumi, P.A.R.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Hazumi, P.A.R

Reference 9

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Observation b670e8c2-cd84-4b5a-9757-e232fc3dc6c7 · outbound

This paper cites PICO: Probe of Inflation and Cosmic Origins.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation PICO: Probe of Inflation and Cosmic Origins

Reference 10

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This paper cites SPT-3G: A Next-Generation Cosmic Microwave Background Polarization Experiment on the South Pole Telescope.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation SPT-3G: A Next-Generation Cosmic Microwave Background Polarization Experiment on the South Pole Telescope

Reference 11

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Observation 9c272efb-1608-41b7-b8da-9a874bc96451 · outbound

This paper cites The Simons Observatory: Science goals and forecasts.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation The Simons Observatory: Science goals and forecasts

Reference 12

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Observation 67b608da-740b-4931-995c-fd8252ff36b9 · outbound

This paper cites CMB-S4 Science Case, Reference Design, and Project Plan.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation CMB-S4 Science Case, Reference Design, and Project Plan

Reference 13

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Observation 2f78b8c1-6e61-4cfe-8019-55efde74a841 · outbound

This paper cites QUBIC I: Overview and ScienceProgram.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation QUBIC I: Overview and ScienceProgram

Reference 14

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Observation 19c636b2-96b0-4755-b274-027928ac8068 · outbound

This paper cites QUBIC II: Spectro-Polarimetry with Bolometric Interferometry.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation QUBIC II: Spectro-Polarimetry with Bolometric Interferometry

Reference 15

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Observation 813f3a34-ea0d-4bfd-8dc5-c0c11b1bb504 · outbound

This paper cites QUBIC: Exploring the primordial Universe with the Q\&U Bolometric Interferometer.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation QUBIC: Exploring the primordial Universe with the Q\&U Bolometric Interferometer

Reference 16

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Observation 19f29736-9c7d-48df-8928-a4a2b2b4cb45 · outbound

This paper cites How to measure CMB power spectra without losing information.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation How to measure CMB power spectra without losing information

Reference 17

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This paper cites Wiener,Extrapolation, interpolation, and smoothing of stationary time series.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Wiener,Extrapolation, interpolation, and smoothing of stationary time series

Reference 18

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Observation d3def541-f82c-43f0-98fd-dd25e89f5371 · outbound

This paper cites Wiener Reconstruction of The Large Scale Structure.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Wiener Reconstruction of The Large Scale Structure

Reference 19

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This paper cites Cosmography and Power Spectrum Estimation: a Unified Approach.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Cosmography and Power Spectrum Estimation: a Unified Approach

Reference 20

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Observation 9cd61f1b-a1e1-4e9f-9c27-51e3a4d85f11 · outbound

This paper cites Press, S.A.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Press, S.A

Reference 21

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Observation 026e0925-b70c-45b0-86bf-f13a4d6a293e · outbound

This paper cites Global, Exact Cosmic Microwave Background Data Analysis Using Gibbs Sampling.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Global, Exact Cosmic Microwave Background Data Analysis Using Gibbs Sampling

Reference 22

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Observation a3b2f90d-0702-442a-a9a6-b7617b672f54 · outbound

This paper cites Detection of Gravitational Lensing in the Cosmic Microwave Background.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Detection of Gravitational Lensing in the Cosmic Microwave Background

Reference 23

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This paper cites Efficient Wiener filtering without preconditioning.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Efficient Wiener filtering without preconditioning

Reference 24

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This paper cites Wiener filter reloaded: fast signal reconstruction without preconditioning.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Wiener filter reloaded: fast signal reconstruction without preconditioning

Reference 25

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Observation d4c32e38-0998-43fd-9bda-223c55a90df0 · outbound

This paper cites Classifying the Large Scale Structure of the Universe with Deep Neural Networks.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Classifying the Large Scale Structure of the Universe with Deep Neural Networks

Reference 26

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Observation ee212679-11c0-433d-a85e-f32075e0d90f · outbound

This paper cites Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning

Reference 27

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Observation ec180013-a931-4091-9a21-21f0d4c6cb54 · outbound

This paper cites Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks

Reference 28

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This paper cites Towards optimal extraction of cosmological information from nonlinear data.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Towards optimal extraction of cosmological information from nonlinear data

Reference 29

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This paper cites Efficient Optimal Reconstruction of Linear Fields and Band-powers from Cosmological Data.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Efficient Optimal Reconstruction of Linear Fields and Band-powers from Cosmological Data

Reference 30

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Observation 24eaaf66-4df7-4a5c-81e9-d4d22083abac · outbound

This paper cites E/B decomposition of finite pixelized CMB maps.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation E/B decomposition of finite pixelized CMB maps

Reference 31

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation A general solution to the E-B mixing problem

Reference 32

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Observation f4f8f881-25c8-4a8c-8e75-c067f8164934 · outbound

This paper cites Reconstruction of full sky CMB $\bf{E}$ and $\bf{B}$ modes spectra removing $\bf{E}$-to-$\bf{B}$ leakage from partial sky using deep learning.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Reconstruction of full sky CMB $\bf{E}$ and $\bf{B}$ modes spectra removing $\bf{E}$-to-$\bf{B}$ leakage from partial sky using deep learning

Reference 33

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Observation aa8040cf-c715-4715-a007-d024063263f3 · outbound

This paper cites Pure E and B polarization maps via Wiener filtering.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Pure E and B polarization maps via Wiener filtering

Reference 34

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Fast Wiener filtering of CMB maps with Neural Networks

Reference 35

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation A unified pseudo-$C_\ell$ framework

Reference 36

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 37

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This paper cites HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere

Reference 38

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Zonca, L

Reference 39

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Efficient Computation of CMB anisotropies in closed FRW models

Reference 40

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This paper cites Planck 2013 results. XVI. Cosmological parameters.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Planck 2013 results. XVI. Cosmological parameters

Reference 41

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Kogut, D.N

Reference 42

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 43

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This paper cites Chen,Matrix Preconditioning Techniques and Applications, Cambridge Monographs on Applied and Computational Mathematics, Cambridge University Press (2005).

DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Chen,Matrix Preconditioning Techniques and Applications, Cambridge Monographs on Applied and Computational Mathematics, Cambridge University Press (2005)

Reference 44

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Observation e1703e03-f1af-4e93-b7ca-f09f83c6b3a0 · outbound

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Statistics of Cosmic Microwave Background Polarization

Reference 45

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Weak Lensing of the CMB: A Harmonic Approach

Reference 46

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DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation Costanza, C.G

Reference 47

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Pith citing papers

Observation a480ff14-e3e5-4a8a-ade2-7df85340be83 · inbound

Searching for Inflationary Physics with the CMB Trispectrum: 1. Primordial Theory & Optimal Estimators cites this paper.

Searching for Inflationary Physics with the CMB Trispectrum: 1. Primordial Theory & Optimal Estimators DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation

Reference 230

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Observation 8975b0f7-51ce-4417-8efa-9e966a0deb64 · inbound

Searching for Inflationary Physics with the CMB Trispectrum: 3. Constraints from Planck cites this paper.

Searching for Inflationary Physics with the CMB Trispectrum: 3. Constraints from Planck DeepWiener: Neural Networks for CMB polarization maps and power spectrum computation

Reference 115

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