Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation | Paper Citation Record

Observation 0fbaf037-79cb-47e6-aeeb-825eb41d803d · outbound

This paper cites In our case, PCA is applied to each of the 192 simulated sky patches, with each 643 data cube reshaped into a collection of one-dimensional spectra along the fre- 7 quency axis.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation In our case, PCA is applied to each of the 192 simulated sky patches, with each 643 data cube reshaped into a collection of one-dimensional spectra along the fre- 7 quency axis

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Unresolved cited work

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This paper cites We preprocess the input data cube inde- pendently using PCA and frequency differencing, yield- ing two separate residual maps.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation We preprocess the input data cube inde- pendently using PCA and frequency differencing, yield- ing two separate residual maps

Reference 3

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Observation d9614be6-8582-4d33-ab78-ae8a1aa83d2b · outbound

This paper cites 21-cm foreground removal using AI and frequency-difference technique.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation 21-cm foreground removal using AI and frequency-difference technique

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Observation 888d754a-a654-444e-9699-c1ac4172fcb5 · outbound

This paper cites Bharadwaj, B.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Bharadwaj, B

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Unresolved cited work

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Observation 32f6f075-945f-40d4-ad58-b8bd6c0920dd · outbound

This paper cites 21 cm Intensity Mapping.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation 21 cm Intensity Mapping

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Observation c5f6beab-3c1f-4724-8ef8-9876e2795a79 · outbound

This paper cites The 21cm Power Spectrum After Reionization.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation The 21cm Power Spectrum After Reionization

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Observation 7e5b539d-7b41-4c32-812a-e45d3d091884 · outbound

This paper cites Baryon Acoustic Oscillation Intensity Mapping as a Test of Dark Energy.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Baryon Acoustic Oscillation Intensity Mapping as a Test of Dark Energy

Reference 9

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Observation 2bca87de-e3a1-4790-84c9-21c099993303 · outbound

This paper cites A ground-based 21cm Baryon acoustic oscillation survey.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation A ground-based 21cm Baryon acoustic oscillation survey

Reference 10

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Observation 68a9a3f8-524b-45b5-8c63-68100f9b6893 · outbound

This paper cites Prospects for measuring dark energy with 21 cm intensity mapping experiments: A joint survey strategy.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Prospects for measuring dark energy with 21 cm intensity mapping experiments: A joint survey strategy

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Observation ae738599-e792-46c8-b718-e66fb81ffc25 · outbound

This paper cites Detection of Cosmological 21 cm Emission with the Canadian Hydrogen Intensity Mapping Experiment.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Detection of Cosmological 21 cm Emission with the Canadian Hydrogen Intensity Mapping Experiment

Reference 13

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Observation 6acf599e-4ebb-4389-87f4-e4dda1b5f50a · outbound

This paper cites An Overview of CHIME, the Canadian Hydrogen Intensity Mapping Experiment.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation An Overview of CHIME, the Canadian Hydrogen Intensity Mapping Experiment

Reference 14

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Observation 95e63c6c-929a-48c5-81a2-2d6721794e41 · outbound

This paper cites Chen, Radio detection of dark energy—the Tianlai project, Scientia Sinica Physica, Mechanica & As- tronomica41, 1358 (2011).

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Chen, Radio detection of dark energy—the Tianlai project, Scientia Sinica Physica, Mechanica & As- tronomica41, 1358 (2011)

Reference 15

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Observation 675fcb9f-357d-4028-9389-63bdf0af883d · outbound

This paper cites Forecasts on the Dark Energy and Primordial Non-Gaussianity Observations with the Tianlai Cylinder Array.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Forecasts on the Dark Energy and Primordial Non-Gaussianity Observations with the Tianlai Cylinder Array

Reference 16

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Observation 389e2b8d-d928-4b2a-8662-aa3f7cb7c823 · outbound

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

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation The Tianlai Dish Pathfinder Array: design, operation and performance of a prototype transit radio interferometer

Reference 17

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Observation 44a78e74-6bf0-40ca-a874-30227699d5ac · outbound

This paper cites The Tianlai dish array low-z surveys forecasts.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation The Tianlai dish array low-z surveys forecasts

Reference 18

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Observation 6e4c2349-4500-4c5c-b829-0f5bae92f968 · outbound

This paper cites HI intensity mapping : a single dish approach.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation HI intensity mapping : a single dish approach

Reference 19

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Observation 127df30f-d2ec-4bac-9cef-8b0b7d4487d1 · outbound

This paper cites The BINGO Project I: Baryon Acoustic Oscillations from Integrated Neutral Gas Observations.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation The BINGO Project I: Baryon Acoustic Oscillations from Integrated Neutral Gas Observations

Reference 20

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Observation e8c3571f-fe0a-4442-8a88-2eb0d8a78751 · outbound

This paper cites MeerKLASS: MeerKAT Large Area Synoptic Survey.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation MeerKLASS: MeerKAT Large Area Synoptic Survey

Reference 21

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Observation 76836f2b-e6b8-4413-8014-d46bb9020395 · outbound

This paper cites HI intensity mapping with MeerKAT: Calibration pipeline for multi-dish autocorrelation observations.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation HI intensity mapping with MeerKAT: Calibration pipeline for multi-dish autocorrelation observations

Reference 22

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Unresolved cited work

Reference 23

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Observation 622bd995-9c4c-4ec5-a0dc-34ae75d5232d · outbound

This paper cites Cosmology with a SKA HI intensity mapping survey.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Cosmology with a SKA HI intensity mapping survey

Reference 24

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Observation 4d08ce51-87f0-4a48-a3c6-cdee61cf8705 · outbound

This paper cites Chang, U.-L.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Chang, U.-L

Reference 25

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Observation 31991849-3961-4ab3-a560-2c327fd92add · outbound

This paper cites Measurement of 21 cm brightness fluctuations at z ~ 0.8 in cross-correlation.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Measurement of 21 cm brightness fluctuations at z ~ 0.8 in cross-correlation

Reference 26

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Observation a32c4bd2-a545-4a66-b9d7-75bae2bd8878 · outbound

This paper cites HI constraints from the cross-correlation of eBOSS galaxies and Green Bank Telescope intensity maps.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation HI constraints from the cross-correlation of eBOSS galaxies and Green Bank Telescope intensity maps

Reference 27

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Observation d05a32ba-3707-4b7d-97de-7dcbccce25c8 · outbound

This paper cites Lack of clustering in low-redshift 21-cm intensity maps cross-correlated with 2dF galaxy densities.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Lack of clustering in low-redshift 21-cm intensity maps cross-correlated with 2dF galaxy densities

Reference 28

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Observation 8b9cd980-0784-4eb4-b3aa-718621254907 · outbound

This paper cites HI intensity mapping with MeerKAT: power spectrum detection in cross-correlation with WiggleZ galaxies.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation HI intensity mapping with MeerKAT: power spectrum detection in cross-correlation with WiggleZ galaxies

Reference 29

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Observation f2ab15d6-3c77-462b-85f1-497005a9449e · outbound

This paper cites HI Intensity Mapping with the MIGHTEE Survey: First Results of the HI Power Spectrum.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation HI Intensity Mapping with the MIGHTEE Survey: First Results of the HI Power Spectrum

Reference 30

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Observation 38394ff6-d095-4f2a-9d5b-60d8e5f373ce · outbound

This paper cites Radio Foregrounds for the 21cm Tomography of the Neutral Intergalactic Medium at High Redshifts.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Radio Foregrounds for the 21cm Tomography of the Neutral Intergalactic Medium at High Redshifts

Reference 31

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Observation fdd9cf4a-0012-4a76-b4e2-2ec0bbd58d11 · outbound

This paper cites Foregrounds for 21cm Observations of Neutral Gas at High Redshift.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Foregrounds for 21cm Observations of Neutral Gas at High Redshift

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Unresolved cited work

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Observation 36cd8b22-09f4-4104-a744-8f2607adc2b6 · outbound

This paper cites A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz

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Observation b035f141-54bb-42a2-bb70-483554dc5d2a · outbound

This paper cites Data Analysis for Precision 21 cm Cosmology.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Data Analysis for Precision 21 cm Cosmology

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Observation b97eb747-bc5c-4d61-a896-49dded2e3752 · outbound

This paper cites A Method for 21cm Power Spectrum Estimation in the Presence of Foregrounds.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation A Method for 21cm Power Spectrum Estimation in the Presence of Foregrounds

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Observation fc8faf23-b1d3-4a06-a9b1-f0b26d13c37d · outbound

This paper cites Interpreting the unresolved intensity of cosmologically redshifted line radiation.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Interpreting the unresolved intensity of cosmologically redshifted line radiation

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This paper cites A Semi-blind PCA-based Foreground Subtraction Method for 21 cm Intensity Mapping.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation A Semi-blind PCA-based Foreground Subtraction Method for 21 cm Intensity Mapping

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Foreground Removal using FastICA: A Showcase of LOFAR-EoR

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Observation a5b3d7c6-1f68-454f-a57f-f835b1409b00 · outbound

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation 21cm foregrounds and polarization leakage: a user's guide on cleaning and mitigation strategies

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This paper cites The Scale of the Problem : Recovering Images of Reionization with GMCA.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation The Scale of the Problem : Recovering Images of Reionization with GMCA

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Observation 1fa83a60-6698-4da3-9a65-7f0baa6376cc · outbound

This paper cites Extracting HI cosmological signal with Generalized Needlet Internal Linear Combination.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Extracting HI cosmological signal with Generalized Needlet Internal Linear Combination

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Observation b59cbaba-d5db-4ea9-851d-544826b0f206 · outbound

This paper cites Marins, F.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Marins, F

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Observation a4509dd7-5358-47a5-91e5-3f197e25494e · outbound

This paper cites Gaussian Process Foreground Subtraction and Power Spectrum Estimation for 21 cm Cosmology.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Gaussian Process Foreground Subtraction and Power Spectrum Estimation for 21 cm Cosmology

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Observation 973e37c5-3f89-4c91-bb5c-86d2c9d96a5f · outbound

This paper cites All-Sky Interferometry with Spherical Harmonic Transit Telescopes.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation All-Sky Interferometry with Spherical Harmonic Transit Telescopes

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Observation 9553ca21-32ec-483f-b273-bd98cd2d6706 · outbound

This paper cites A Bayesian analysis of redshifted 21-cm HI signal and foregrounds: Simulations for LOFAR.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation A Bayesian analysis of redshifted 21-cm HI signal and foregrounds: Simulations for LOFAR

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Observation e73cd873-bd98-4186-a14a-1270d94393f4 · outbound

This paper cites Bayesian semi-blind component separation for foreground removal in interferometric 21-cm observations.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Bayesian semi-blind component separation for foreground removal in interferometric 21-cm observations

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Observation e8cc7e87-dcec-44e5-8fa8-3409cd46a1fd · outbound

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Joint estimation of the Epoch of Reionization power spectrum and foregrounds

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Observation b6891c81-93d2-4784-b1a0-2c9cd459ab0c · outbound

This paper cites All Sky Modelling Requirements for Bayesian 21 cm Power Spectrum Estimation with BayesEoR.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation All Sky Modelling Requirements for Bayesian 21 cm Power Spectrum Estimation with BayesEoR

Reference 49

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Observation 4326ea88-ff5a-4016-bd54-e4a513d807a1 · outbound

This paper cites Correlation-based Beam Calibration of 21cm Intensity Mapping.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Correlation-based Beam Calibration of 21cm Intensity Mapping

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Observation 5e73a2a4-ec36-486e-a2b1-d40c28e5c18e · outbound

This paper cites HIR4: cosmology from a simulated neutral hydrogen full sky using Horizon Run 4.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation HIR4: cosmology from a simulated neutral hydrogen full sky using Horizon Run 4

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Observation a2a54159-4f4b-4d6d-bd53-3bf1f47cc5c9 · outbound

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation HIR4: Cosmological signatures imprinted on the cross correlation between 21-cm map and galaxy clustering

Reference 52

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Observation 39c9504f-31af-4b59-9b11-1a24d5474a23 · outbound

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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method

Reference 53

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Observation 2714fdb8-fb63-4977-9250-90b55c5d0fef · outbound

This paper cites deep21: a Deep Learning Method for 21cm Foreground Removal.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation deep21: a Deep Learning Method for 21cm Foreground Removal

Reference 54

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Observation 6445c019-09d9-490b-8337-28b88cbe86b4 · outbound

This paper cites Recovering the Wedge Modes Lost to 21-cm Foregrounds.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Recovering the Wedge Modes Lost to 21-cm Foregrounds

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Observation 84b073ab-9200-43cc-9288-0dda1a379e25 · outbound

This paper cites Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning

Reference 56

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Observation 339787c9-30ee-4373-9131-f7dc238f5c14 · outbound

This paper cites Machine-learning recovery of foreground wedge-removed 21-cm light cones for high-$z$ galaxy mapping.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Machine-learning recovery of foreground wedge-removed 21-cm light cones for high-$z$ galaxy mapping

Reference 57

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Observation f7c72e08-529d-450d-8678-a34ddeb660b2 · outbound

This paper cites Deep learning approach for identification of HII regions during reionization in 21-cm observations -- II. foreground contamination.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Deep learning approach for identification of HII regions during reionization in 21-cm observations -- II. foreground contamination

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:53.909054Z

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Observation 7ca8ec58-9030-47f1-b3c5-f4930b54012e · outbound

This paper cites Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.002428Z

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Observation 264c7f56-3c59-4fa1-8cfe-ee5ed81d8f74 · outbound

This paper cites A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.089346Z

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Observation 0f3df9c6-1dcc-4c68-b96f-ea5a7fc61e0b · outbound

This paper cites Chen, K.-F.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Chen, K.-F

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.177633Z

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Observation c8270351-8817-4844-93eb-43ee95e59ac9 · outbound

This paper cites Fast simulations for intensity mapping experiments.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Fast simulations for intensity mapping experiments

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.263064Z

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Observation 82b8df25-5785-40e5-ab46-b70661964e3e · outbound

This paper cites an unresolved cited work.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Unresolved cited work

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.384736Z

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Observation 09659df0-2172-4ea8-8d20-6cfdfb8569e2 · outbound

This paper cites The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation The pre-launch Planck Sky Model: a model of sky emission at submillimetre to centimetre wavelengths

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.485560Z

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Observation 035ea748-2bc2-461c-9e53-acfc9fc613cd · outbound

This paper cites An improved source-subtracted and destriped 408 MHz all-sky map.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation An improved source-subtracted and destriped 408 MHz all-sky map

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.610947Z

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Observation 433c25da-1381-46b7-b6ef-02522b898737 · outbound

This paper cites an unresolved cited work.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.730943Z

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Observation 6c4c3938-8334-4c11-85c3-e564c7636dd5 · outbound

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

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.853027Z

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Observation 56be1d50-f97b-4806-8c83-0ab0a882c100 · outbound

This paper cites Hi intensity mapping with MeerKAT: Primary beam effects on foreground cleaning.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Hi intensity mapping with MeerKAT: Primary beam effects on foreground cleaning

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:54.955871Z

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Observation 75a50b5f-6b6d-4beb-8884-f078373f1067 · outbound

This paper cites Toward a more stringent test of gravity with redshift space power spectrum: simultaneous probe of growth and amplitude of large-scale structure.

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation Toward a more stringent test of gravity with redshift space power spectrum: simultaneous probe of growth and amplitude of large-scale structure

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-03T21:30:55.070941Z

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