Restoration of contaminated data in an Intensity Mapping survey using deep neural networks | Paper Citation Record

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This paper cites G., Santos M.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks G., Santos M

Reference 1

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Observation 0b9f1dae-2a80-4f77-8201-51028a48a41d · outbound

This paper cites G., Santos M.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks G., Santos M

Reference 2

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This paper cites J., et al., 2018, @doi [ ] 10.1093/mnras/sty346 , http://adsabs.harvard.edu/abs/2018MNRAS.476.3382A 476, 3382.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks J., et al., 2018, @doi [ ] 10.1093/mnras/sty346 , http://adsabs.harvard.edu/abs/2018MNRAS.476.3382A 476, 3382

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This paper cites 21cm Intensity Mapping cross-correlation with galaxy surveys: current and forecasted cosmological parameters estimation for the SKAO.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks 21cm Intensity Mapping cross-correlation with galaxy surveys: current and forecasted cosmological parameters estimation for the SKAO

Reference 5

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This paper cites K., Iliev I.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks K., Iliev I

Reference 6

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This paper cites A., et al., 2015, @doi [ ] 10.1093/mnras/stv2153 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.454.3240B 454, 3240.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks A., et al., 2015, @doi [ ] 10.1093/mnras/stv2153 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.454.3240B 454, 3240

Reference 7

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Observation 0997f520-39ad-4dee-8442-6d3a0839bd7b · outbound

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

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Detection of Cosmological 21 cm Emission with the Canadian Hydrogen Intensity Mapping Experiment

Reference 9

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This paper cites an unresolved cited work.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 10

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This paper cites B., 2010, @doi [ ] 10.1038/nature09187 , http://adsabs.harvard.edu/abs/2010Natur.466..463C 466, 463.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks B., 2010, @doi [ ] 10.1038/nature09187 , http://adsabs.harvard.edu/abs/2010Natur.466..463C 466, 463

Reference 11

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This paper cites an unresolved cited work.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 13

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This paper cites an unresolved cited work.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 14

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This paper cites an unresolved cited work.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 15

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Observation 317a6595-0578-42b9-9677-5fb7a2e3ac86 · outbound

This paper cites The DESI Experiment Part I: Science,Targeting, and Survey Design.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks The DESI Experiment Part I: Science,Targeting, and Survey Design

Reference 16

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This paper cites S., White S.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks S., White S

Reference 17

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This paper cites R., et al., 2017, @doi [ ] 10.1088/1538-3873/129/974/045001 , https://ui.adsabs.harvard.edu/abs/2017PASP..129d5001D 129, 045001.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks R., et al., 2017, @doi [ ] 10.1088/1538-3873/129/974/045001 , https://ui.adsabs.harvard.edu/abs/2017PASP..129d5001D 129, 045001

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This paper cites Machine Learning and Cosmology.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Machine Learning and Cosmology

Reference 20

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This paper cites an unresolved cited work.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 21

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This paper cites an unresolved cited work.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 22

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This paper cites R., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20582.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.421.3570G 421, 3570.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks R., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20582.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.421.3570G 421, 3570

Reference 23

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This paper cites an unresolved cited work.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 24

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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This paper cites LSST: from Science Drivers to Reference Design and Anticipated Data Products.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks LSST: from Science Drivers to Reference Design and Anticipated Data Products

Reference 26

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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This paper cites H., et al., 2004, @doi [ ] 10.1111/j.1365-2966.2004.08353.x , http://adsabs.harvard.edu/abs/2004MNRAS.355..747J 355, 747.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks H., et al., 2004, @doi [ ] 10.1111/j.1365-2966.2004.08353.x , http://adsabs.harvard.edu/abs/2004MNRAS.355..747J 355, 747

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This paper cites H., et al., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15338.x , http://adsabs.harvard.edu/abs/2009MNRAS.399..683J 399, 683.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks H., et al., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15338.x , http://adsabs.harvard.edu/abs/2009MNRAS.399..683J 399, 683

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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This paper cites L., Lancaster L., Villaescusa-Navarro F., Melchior P., Ho S., Perreault-Levasseur L., Spergel D.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks L., Lancaster L., Villaescusa-Navarro F., Melchior P., Ho S., Perreault-Levasseur L., Spergel D

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks W., et al., 2013, @doi [ ] 10.1088/2041-8205/763/1/L20 , http://adsabs.harvard.edu/abs/2013ApJ...763L..20M 763, L20

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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This paper cites A LOFAR RFI detection pipeline and its first results.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks A LOFAR RFI detection pipeline and its first results

Reference 38

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 40

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This paper cites R., et al., 2010, @doi [ ] 10.1088/0004-6256/139/4/1468 , https://ui.adsabs.harvard.edu/abs/2010AJ....139.1468P 139, 1468.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks R., et al., 2010, @doi [ ] 10.1088/0004-6256/139/4/1468 , https://ui.adsabs.harvard.edu/abs/2010AJ....139.1468P 139, 1468

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This paper cites B., Chang T.-C., 2009, @doi [ ] 10.1111/j.1745-3933.2008.00581.x , http://adsabs.harvard.edu/abs/2009MNRAS.394L...6P 394, L6.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks B., Chang T.-C., 2009, @doi [ ] 10.1111/j.1745-3933.2008.00581.x , http://adsabs.harvard.edu/abs/2009MNRAS.394L...6P 394, L6

Reference 42

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This paper cites G., Cooray A., Knox L., 2005, , 625, 575.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks G., Cooray A., Knox L., 2005, , 625, 575

Reference 43

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This paper cites F., et al., 2006, @doi [ ] 10.1086/498708 , https://ui.adsabs.harvard.edu/abs/2006AJ....131.1163S 131, 1163.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks F., et al., 2006, @doi [ ] 10.1086/498708 , https://ui.adsabs.harvard.edu/abs/2006AJ....131.1163S 131, 1163

Reference 44

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 45

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This paper cites Resolution-robust Large Mask Inpainting with Fourier Convolutions.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Resolution-robust Large Mask Inpainting with Fourier Convolutions

Reference 46

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This paper cites R., et al., 2013, @doi [ ] 10.1093/mnrasl/slt074 , http://adsabs.harvard.edu/abs/2013MNRAS.434L..46S 434, L46.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks R., et al., 2013, @doi [ ] 10.1093/mnrasl/slt074 , http://adsabs.harvard.edu/abs/2013MNRAS.434L..46S 434, L46

Reference 47

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 48

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

Reference 49

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This paper cites G., Knox L., 2006, @doi [ ] 10.1086/506597 , http://adsabs.harvard.edu/abs/2006ApJ...650..529W 650, 529.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks G., Knox L., 2006, @doi [ ] 10.1086/506597 , http://adsabs.harvard.edu/abs/2006ApJ...650..529W 650, 529

Reference 50

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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This paper cites B., et al., 2018, @doi [ ] 10.1017/pasa.2018.37 , https://ui.adsabs.harvard.edu/abs/2018PASA...35...33W 35, e033.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks B., et al., 2018, @doi [ ] 10.1017/pasa.2018.37 , https://ui.adsabs.harvard.edu/abs/2018PASA...35...33W 35, e033

Reference 52

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks B., Blake C., Shaw J

Reference 53

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks L., et al., 2010, @doi [ ] 10.1088/0004-6256/140/6/1868 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1868W 140, 1868

Reference 54

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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This paper cites G., et al., 2000, @doi [ ] 10.1086/301513 , http://adsabs.harvard.edu/abs/2000AJ....120.1579Y 120, 1579.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks G., et al., 2000, @doi [ ] 10.1086/301513 , http://adsabs.harvard.edu/abs/2000AJ....120.1579Y 120, 1579

Reference 56

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks Unresolved cited work

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This paper cites F., Karakci A., Korotkov A., Sutter P.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks F., Karakci A., Korotkov A., Sutter P

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This paper cites P., et al., 2013, @doi [ ] 10.1051/0004-6361/201220873 , https://ui.adsabs.harvard.edu/abs/2013A&A...556A...2V 556, A2.

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks P., et al., 2013, @doi [ ] 10.1051/0004-6361/201220873 , https://ui.adsabs.harvard.edu/abs/2013A&A...556A...2V 556, A2

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Restoration of contaminated data in an Intensity Mapping survey using deep neural networks write newline

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