Paper Citation Record · LEDGER

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach

As of 3 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2509.00139.

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

pith.paper-citation-record.v1
2509.00139 v2

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External citation measurements

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Outbound references

Observation 996fed91-9ba0-4c13-b4fe-5eadc0e414f0 · outbound

This paper cites Durrer, The cosmic microwave background, Cambridge University Press (2020).

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Durrer, The cosmic microwave background, Cambridge University Press (2020)

Reference 1

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

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Planck 2018 results. I. Overview and the cosmological legacy of Planck

Reference 2

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Observation d71c99eb-a26a-4527-8d9b-efdf58e526bf · outbound

This paper cites Bennett, G.F.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Bennett, G.F

Reference 3

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Observation d11c8db0-3bce-48f4-941d-70dc0b7cd9c4 · outbound

This paper cites On Foreground Removal from the Wilkinson Microwave Anisotropy Probe Data by an Internal Linear Combination Method: Limitations and Implications.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach On Foreground Removal from the Wilkinson Microwave Anisotropy Probe Data by an Internal Linear Combination Method: Limitations and Implications

Reference 4

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Observation d7a1a799-40fd-48c1-9904-b208daf01941 · outbound

This paper cites Partially Constrained Internal Linear Combination: a method for low-noise CMB foreground mitigation.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Partially Constrained Internal Linear Combination: a method for low-noise CMB foreground mitigation

Reference 5

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Observation d7eabfe1-c135-40ea-8f6d-1f7f699bd934 · outbound

This paper cites A needlet ILC analysis of WMAP 9-year polarisation data: CMB polarisation power spectra.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach A needlet ILC analysis of WMAP 9-year polarisation data: CMB polarisation power spectra

Reference 6

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Observation 2ed46aac-e5ea-4c72-8269-7e7fd83ade8a · outbound

This paper cites BeyondPlanck XI. Bayesian CMB analysis with sample-based end-to-end error propagation.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach BeyondPlanck XI. Bayesian CMB analysis with sample-based end-to-end error propagation

Reference 7

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Observation f68752e0-0ca6-4f5b-8cb2-58074838cbed · outbound

This paper cites Hierarchical Bayesian CMB Component Separation with the No-U-Turn Sampler.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Hierarchical Bayesian CMB Component Separation with the No-U-Turn Sampler

Reference 8

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Observation 33f24a3d-7f90-464e-89e6-2d84e4678188 · outbound

This paper cites Application of beam deconvolution technique to power spectrum estimation for CMB measurements.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Application of beam deconvolution technique to power spectrum estimation for CMB measurements

Reference 9

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Observation b48b225e-1d6a-4dfc-956a-1b7acde3ea6a · outbound

This paper cites Kembhavi and R.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Kembhavi and R

Reference 10

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This paper cites an unresolved cited work.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Unresolved cited work

Reference 11

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This paper cites ForSE: a GAN based algorithm for extending CMB foreground models to sub-degree angular scales.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach ForSE: a GAN based algorithm for extending CMB foreground models to sub-degree angular scales

Reference 12

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Observation e359488d-5462-4d05-bf3e-975703656100 · outbound

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

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning

Reference 13

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Observation 1256abb8-bb19-448e-987e-137de65da457 · outbound

This paper cites Cleaning our own Dust: Simulating and Separating Galactic Dust Foregrounds with Neural Networks.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Cleaning our own Dust: Simulating and Separating Galactic Dust Foregrounds with Neural Networks

Reference 14

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Observation 305fc3a0-2026-43a8-b6e8-3a4a9e5145d4 · outbound

This paper cites The Python Sky Model: software for simulating the Galactic microwave sky.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach The Python Sky Model: software for simulating the Galactic microwave sky

Reference 15

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Observation 131a9cb8-f75c-43be-bf14-6c4cee6eacd0 · outbound

This paper cites Efficient Computation of CMB anisotropies in closed FRW models.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Efficient Computation of CMB anisotropies in closed FRW models

Reference 16

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Observation f5314300-969c-4f72-a42b-f3d5aeaa1004 · outbound

This paper cites CMB power spectrum parameter degeneracies in the era of precision cosmology.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach CMB power spectrum parameter degeneracies in the era of precision cosmology

Reference 17

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

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere

Reference 18

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Observation 63e9cc3c-4fed-4866-8722-1190ddd5996c · outbound

This paper cites Borrill, S.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Borrill, S

Reference 19

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This paper cites The Python Sky Model 3 software.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach The Python Sky Model 3 software

Reference 20

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Observation 29a2ee2c-3e81-4a82-bafa-30f6dcfa15eb · outbound

This paper cites Tauber, N.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Tauber, N

Reference 21

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This paper cites Leakage of power from dipole to higher multipoles due to non-symmetric WMAP beam.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Leakage of power from dipole to higher multipoles due to non-symmetric WMAP beam

Reference 22

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This paper cites Dipole leakage and low CMB multipoles.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Dipole leakage and low CMB multipoles

Reference 23

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This paper cites Planck Early Results: The Planck mission.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Planck Early Results: The Planck mission

Reference 24

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This paper cites DeepSphere: a graph-based spherical CNN.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach DeepSphere: a graph-based spherical CNN

Reference 25

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This paper cites Generative Adversarial Networks.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Generative Adversarial Networks

Reference 26

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This paper cites A Style-Based Generator Architecture for Generative Adversarial Networks.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach A Style-Based Generator Architecture for Generative Adversarial Networks

Reference 27

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Adversarial Feature Matching for Text Generation

Reference 28

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Reference 29

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This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 30

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks

Reference 31

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Delensing of Cosmic Microwave Background Polarization with machine learning

Reference 32

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 33

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Maas, A.Y

Reference 34

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 35

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Understanding Batch Normalization

Reference 36

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Deep Learning using Rectified Linear Units (ReLU)

Reference 37

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Adam: A Method for Stochastic Optimization

Reference 38

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Hajian and T

Reference 39

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Bennett, R.S

Reference 40

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This paper cites SIToolBox : A package for Bayesian estimation of the isotropy violation in the CMB sky.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach SIToolBox : A package for Bayesian estimation of the isotropy violation in the CMB sky

Reference 41

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This paper cites Bayesian inference on the sphere beyond statistical isotropy.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Bayesian inference on the sphere beyond statistical isotropy

Reference 42

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This paper cites Orthogonal BipoSH measures : Scrutinizing sources of isotropy violation.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Orthogonal BipoSH measures : Scrutinizing sources of isotropy violation

Reference 43

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This paper cites Estimating SI violation in CMB due to non-circular beam and complex scan in minutes.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Estimating SI violation in CMB due to non-circular beam and complex scan in minutes

Reference 44

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This paper cites Yan, S.-Y.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Yan, S.-Y

Reference 45

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This paper cites Foreground model recognition through Neural Networks for CMB B-mode observations.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Foreground model recognition through Neural Networks for CMB B-mode observations

Reference 46

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach A perceptron based ILC method to obtain accurate CMB B-mode angular power spectrum

Reference 47

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This paper cites Casas, L.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Casas, L

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:06:50.231250Z

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Observation 73540060-17b4-4c03-ac00-6ad7256d5311 · outbound

This paper cites Casas, L.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Casas, L

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T20:12:51.298895Z

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This paper cites Statistical isotropy violation in WMAP CMB maps resulting from non-circular beams.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Statistical isotropy violation in WMAP CMB maps resulting from non-circular beams

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:06:50.220169Z

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This paper cites The Tianlai Dish Pathfinder Array: design, operation and performance of a prototype transit radio interferometer.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach The Tianlai Dish Pathfinder Array: design, operation and performance of a prototype transit radio interferometer

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:06:50.271724Z

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This paper cites Data Processing Pipeline For Tianlai Experiment.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Data Processing Pipeline For Tianlai Experiment

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:06:50.204096Z

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This paper cites AlgoSCR: An algorithm for Solar Contamination Removal from radio interferometric data.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach AlgoSCR: An algorithm for Solar Contamination Removal from radio interferometric data

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:06:50.198578Z

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Observation 8712380a-e5e4-43ff-a917-87c9897803db · outbound

This paper cites Using Neural Networks for Data Cleaning in Weather Datasets.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Using Neural Networks for Data Cleaning in Weather Datasets

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:06:50.225949Z

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Pith citing papers

Observation a7c621a1-daa4-4779-967d-361aff491359 · inbound

Single Frequency CMB Foreground Removal with Inter-scale Machine Learning cites this paper.

Single Frequency CMB Foreground Removal with Inter-scale Machine Learning Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach

Reference 58

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