Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models | Paper Citation Record

Observation afe09e89-3363-4a8a-97da-340cee1a7208 · outbound

This paper cites Kaplinghat, L.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Kaplinghat, L

Reference 1

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Observation ca047484-69bc-4ebb-85ab-0848e6c4fe3a · outbound

This paper cites Cosmological constraints from thermal Sunyaev Zeldovich power spectrum revisited.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Cosmological constraints from thermal Sunyaev Zeldovich power spectrum revisited

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Observation 7c8c1cb3-85c5-4d24-b55b-95e35853867a · outbound

This paper cites SPT Clusters with DES and HST Weak Lensing. II. Cosmological Constraints from the Abundance of Massive Halos.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models SPT Clusters with DES and HST Weak Lensing. II. Cosmological Constraints from the Abundance of Massive Halos

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Observation c4b5d447-3aa2-42ce-a0ba-902d68a691cb · outbound

This paper cites The Atacama Cosmology Telescope: DR6 Gravitational Lensing Map and Cosmological Parameters.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The Atacama Cosmology Telescope: DR6 Gravitational Lensing Map and Cosmological Parameters

Reference 4

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Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Unresolved cited work

Reference 5

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Observation 093a51ba-e875-4e74-84c5-cdcfb3a03795 · outbound

This paper cites The Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and its Implications for Structure Growth.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and its Implications for Structure Growth

Reference 6

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Observation f97ff138-1fbf-4496-b968-8d154f4c73e9 · outbound

This paper cites An Improved Measurement of the Secondary Cosmic Microwave Background Anisotropies from the SPT-SZ + SPTpol Surveys.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models An Improved Measurement of the Secondary Cosmic Microwave Background Anisotropies from the SPT-SZ + SPTpol Surveys

Reference 7

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Observation 70be5c39-ce88-4f39-8a35-d15e904103d5 · outbound

This paper cites Improved constraints on reionisation from CMB observations: A parameterisation of the kSZ effect.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Improved constraints on reionisation from CMB observations: A parameterisation of the kSZ effect

Reference 8

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Observation 4b04b5e9-99c9-49b8-b120-c5e158352967 · outbound

This paper cites Raghunathan, P.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Raghunathan, P

Reference 9

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Observation 6e56cc2c-6df0-44f6-91a6-7f7328f51fb4 · outbound

This paper cites Galaxy Clusters Discovered via the Sunyaev-Zel'dovich Effect in the 2500-square-degree SPT-SZ survey.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Galaxy Clusters Discovered via the Sunyaev-Zel'dovich Effect in the 2500-square-degree SPT-SZ survey

Reference 10

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Observation 9ed772e1-1f03-417d-94a0-f532b7a6a9bd · outbound

This paper cites Planck 2015 results. XXIV. Cosmology from Sunyaev-Zeldovich cluster counts.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Planck 2015 results. XXIV. Cosmology from Sunyaev-Zeldovich cluster counts

Reference 11

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Observation 788fc179-faff-4442-812e-9b737f8344fb · outbound

This paper cites The SPTpol Extended Cluster Survey.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The SPTpol Extended Cluster Survey

Reference 12

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Observation 6c6bd539-c0a5-4ea3-a454-e44fac2e6931 · outbound

This paper cites The Atacama Cosmology Telescope: A Catalog of > 4000 Sunyaev-Zel'dovich Galaxy Clusters.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The Atacama Cosmology Telescope: A Catalog of > 4000 Sunyaev-Zel'dovich Galaxy Clusters

Reference 13

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Observation d97bd614-95b7-4cf0-870b-5aec2e673ac9 · outbound

This paper cites The SPT-Deep Cluster Catalog: Sunyaev-Zel'dovich Selected Clusters from Combined SPT-3G and SPTpol Measurements over 100 Square Degrees.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The SPT-Deep Cluster Catalog: Sunyaev-Zel'dovich Selected Clusters from Combined SPT-3G and SPTpol Measurements over 100 Square Degrees

Reference 14

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Observation ba35e993-2438-4a9b-9ff7-da68827a4c40 · outbound

This paper cites The Atacama Cosmology Telescope: Combined kinematic and thermal Sunyaev-Zel'dovich measurements from BOSS CMASS and LOWZ halos.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The Atacama Cosmology Telescope: Combined kinematic and thermal Sunyaev-Zel'dovich measurements from BOSS CMASS and LOWZ halos

Reference 15

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Observation 36f79afd-f4fc-4a89-8803-a9aec41cceb6 · outbound

This paper cites Weak lensing combined with the kinetic Sunyaev Zel'dovich effect: A study of baryonic feedback.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Weak lensing combined with the kinetic Sunyaev Zel'dovich effect: A study of baryonic feedback

Reference 16

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Observation d9aebea3-5e70-49d8-a2b1-1db5922ada05 · outbound

This paper cites Evidence for large baryonic feedback at low and intermediate redshifts from kinematic Sunyaev-Zel'dovich observations with ACT and DESI photometric galaxies.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Evidence for large baryonic feedback at low and intermediate redshifts from kinematic Sunyaev-Zel'dovich observations with ACT and DESI photometric galaxies

Reference 17

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Observation 697dc9af-1177-42a6-a045-595073873f95 · outbound

This paper cites Ried Guachalla, E.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Ried Guachalla, E

Reference 18

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Observation a73c7d78-423f-48cf-94b9-f3bc2927da1b · outbound

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Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Unresolved cited work

Reference 19

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Observation 69136821-eda0-41d3-b76d-cff02ea99ac9 · outbound

This paper cites A Cross-Internal Linear Combination Approach to Probe the Secondary CMB Anisotropies: Kinematic Sunyaev-Zel{'}dovich Effect and CMB Lensing.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models A Cross-Internal Linear Combination Approach to Probe the Secondary CMB Anisotropies: Kinematic Sunyaev-Zel{'}dovich Effect and CMB Lensing

Reference 20

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Observation 244e1c96-1e6d-4de7-b5e8-b89f0ae41c97 · outbound

This paper cites CMB-HD: An Ultra-Deep, High-Resolution Millimeter-Wave Survey Over Half the Sky.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models CMB-HD: An Ultra-Deep, High-Resolution Millimeter-Wave Survey Over Half the Sky

Reference 21

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Observation 2a9b7162-bb5e-422a-95d4-b9843de53545 · outbound

This paper cites Stein, M.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Stein, M

Reference 22

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Observation ce7ffb03-11ad-4d80-a935-7cd32a8e1df6 · outbound

This paper cites Omori, Monthly Notices of the Royal Astronomical Society530, 5030 (2024), https://academic.oup.com/mnras/article-pdf/530/4/5030/57527656/stae1031.pdf.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Omori, Monthly Notices of the Royal Astronomical Society530, 5030 (2024), https://academic.oup.com/mnras/article-pdf/530/4/5030/57527656/stae1031.pdf

Reference 23

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Observation b11d4eb0-e4e5-484c-9841-6a2d336b1e17 · outbound

This paper cites MillimeterDL: Deep Learning Simulations of the Microwave Sky.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models MillimeterDL: Deep Learning Simulations of the Microwave Sky

Reference 24

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Observation dddbcbbd-0e0a-48e4-b1ae-c052b6dc4f26 · outbound

This paper cites Wavelet Flow For Extragalactic Foreground Simulations.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Wavelet Flow For Extragalactic Foreground Simulations

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:01:40.511716Z

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Observation 6e1183bc-e22f-4b58-a824-7a73e5009c56 · outbound

This paper cites Fl¨ oss, W.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Fl¨ oss, W

Reference 26

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Observation 7eec3738-8386-45ca-ac10-0f95ccc7ce3f · outbound

This paper cites Denoising weak lensing mass maps with diffusion model: systematic comparison with generative adversarial network.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Denoising weak lensing mass maps with diffusion model: systematic comparison with generative adversarial network

Reference 27

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Observation 33a20369-5f28-4e42-8ac5-47f81bfde2d9 · outbound

This paper cites Can denoising diffusion probabilistic models generate realistic astrophysical fields?.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Can denoising diffusion probabilistic models generate realistic astrophysical fields?

Reference 28

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Observation 3d07a70e-38bf-4978-900d-0e98ec18abd7 · outbound

This paper cites Sohl-Dickstein, E.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Sohl-Dickstein, E

Reference 29

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Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Unresolved cited work

Reference 30

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Observation d267c5d2-8e96-456d-85b3-2d761be1d2cf · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 31

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Observation 2dba2e86-907a-4167-ae70-ccb92f024815 · outbound

This paper cites Klypin, G.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Klypin, G

Reference 32

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Observation 705f8628-64a6-4ee4-847f-67760cfce075 · outbound

This paper cites CMB Lensing Power Spectrum Biases from Galaxies and Clusters using High-angular Resolution Temperature Maps.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models CMB Lensing Power Spectrum Biases from Galaxies and Clusters using High-angular Resolution Temperature Maps

Reference 33

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Observation 99b11e0a-3ef4-46f6-ab54-d6e3719e9f53 · outbound

This paper cites Mitigating Foreground Biases in CMB Lensing Reconstruction Using Cleaned Gradients.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Mitigating Foreground Biases in CMB Lensing Reconstruction Using Cleaned Gradients

Reference 34

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Observation 64bf4058-c272-4840-8f2e-9d7b5e5b7c17 · outbound

This paper cites Lower bias, lower noise CMB lensing with foreground-hardened estimators.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Lower bias, lower noise CMB lensing with foreground-hardened estimators

Reference 35

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Observation 71b8edcb-661e-4dc0-a3cf-670bc84a8bbc · outbound

This paper cites The Atacama Cosmology Telescope: Mitigating the impact of extragalactic foregrounds for the DR6 CMB lensing analysis.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The Atacama Cosmology Telescope: Mitigating the impact of extragalactic foregrounds for the DR6 CMB lensing analysis

Reference 36

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Observation ad899482-327c-4c17-8e1d-21c7ecd22287 · outbound

This paper cites Quantifying Bias due to non-Gaussian Foregrounds in an Optimal Reconstruction of CMB Lensing and Temperature Power Spectra.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Quantifying Bias due to non-Gaussian Foregrounds in an Optimal Reconstruction of CMB Lensing and Temperature Power Spectra

Reference 37

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Observation a40da57e-e2fd-4a66-a160-caae7e8eef46 · outbound

This paper cites CMB lensing reconstruction biases in cross-correlation with large-scale structure probes.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models CMB lensing reconstruction biases in cross-correlation with large-scale structure probes

Reference 38

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Observation 421204ea-daa4-4e12-a2a9-98d5d74f83b9 · outbound

This paper cites Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck I: Construction of CMB Lensing Maps and Modeling Choices.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Joint analysis of DES Year 3 data and CMB lensing from SPT and Planck I: Construction of CMB Lensing Maps and Modeling Choices

Reference 39

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Observation 1c89991a-9593-44fa-aa2a-5197c5d1a0ff · outbound

This paper cites The Design and Integrated Performance of SPT-3G.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The Design and Integrated Performance of SPT-3G

Reference 40

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Observation 1ec020c9-adb6-491e-818e-24825e4996e8 · outbound

This paper cites The BAHAMAS project: Calibrated hydrodynamical simulations for large-scale structure cosmology.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The BAHAMAS project: Calibrated hydrodynamical simulations for large-scale structure cosmology

Reference 41

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Observation 12a2f85a-aec5-4e8c-b123-b9f5fc58e3aa · outbound

This paper cites A hydrodynamical halo model for weak-lensing cross correlations.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models A hydrodynamical halo model for weak-lensing cross correlations

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:40.161130Z

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Observation 5cd85dbc-3a2b-40a2-abf3-0a6acce164ab · outbound

This paper cites Behroozi, R.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Behroozi, R

Reference 43

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Observation 0f6f9106-62ba-4385-9249-bdf5a572122f · outbound

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

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere

Reference 44

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Observation 379eebe1-8d8d-432e-b5ca-07066b7b7c6f · outbound

This paper cites CMB-S4: Forecasting Constraints on Primordial Gravitational Waves.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models CMB-S4: Forecasting Constraints on Primordial Gravitational Waves

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:40.171004Z

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Observation a6a386f5-c646-4bb5-b634-f175a00bb6f8 · outbound

This paper cites Assessing the Importance of Noise from Thermal Sunyaev-Zel{'}dovich Signals for CMB Cluster Surveys and Cluster Cosmology.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Assessing the Importance of Noise from Thermal Sunyaev-Zel{'}dovich Signals for CMB Cluster Surveys and Cluster Cosmology

Reference 46

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Observation 3fad656d-bcbb-4b30-ad70-ab7d2ccccad9 · outbound

This paper cites denoising-diffusion-pytorch,.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models denoising-diffusion-pytorch,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:01:40.983432Z

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Observation 5ffd7880-e85f-4ea1-bff2-a700de2921ce · outbound

This paper cites Ronneberger, P.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Ronneberger, P

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:01:40.973716Z

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Observation 3a52f29b-2f1b-4cf5-a54f-22826283ffe3 · outbound

This paper cites Vaswani, N.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Vaswani, N

Reference 49

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Observation 162e8873-95f5-44aa-826b-cf9d3c9882fe · outbound

This paper cites Stamatelopoulos and T.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Stamatelopoulos and T

Reference 50

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Observation cc96ec35-716b-418e-9a9d-d0827f719dcf · outbound

This paper cites Minkowski Functionals in Cosmology.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Minkowski Functionals in Cosmology

Reference 51

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Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Unresolved cited work

Reference 52

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Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Unresolved cited work

Reference 53

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This paper cites The Two-Halo Term in Stacked Thermal Sunyaev-Zel'dovich Measurements: Implications for Self-Similarity.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The Two-Halo Term in Stacked Thermal Sunyaev-Zel'dovich Measurements: Implications for Self-Similarity

Reference 54

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Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Unresolved cited work

Reference 55

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This paper cites MUSE: Marginal Unbiased Score Expansion and Application to CMB Lensing.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models MUSE: Marginal Unbiased Score Expansion and Application to CMB Lensing

Reference 56

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This paper cites Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models

Reference 57

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This paper cites Diffusion Models Beat GANs on Image Synthesis.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Diffusion Models Beat GANs on Image Synthesis

Reference 58

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This paper cites Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models

Reference 59

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

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models The Python Sky Model: software for simulating the Galactic microwave sky

Reference 60

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This paper cites Diffusion Normalizing Flow.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Diffusion Normalizing Flow

Reference 61

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This paper cites Salimans and J.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Salimans and J

Reference 62

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This paper cites Adam: A Method for Stochastic Optimization.

Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models Adam: A Method for Stochastic Optimization

Reference 63

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