Convolutional neural networks can infer galaxy cluster virial masses and scale radii from 2D projected position and line-of-sight velocity distributions with nearly unbiased results and reduced scatter when richness is added or training is limited to relaxed systems.
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Optical follow-up of 32 Planck SZ candidates yields photometric redshifts and richness estimates confirming 18 (7) as at least half as rich as expected at z>0.5 (z>0.8), highlighting Eddington bias and projection effects.
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Inferring Halo Mass and Scale Radius of Galaxy Clusters Using Convolutional Neural Networks and Uchuu-UniverseMachine Catalogs
Convolutional neural networks can infer galaxy cluster virial masses and scale radii from 2D projected position and line-of-sight velocity distributions with nearly unbiased results and reduced scatter when richness is added or training is limited to relaxed systems.
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Optical follow-up study of 32 high-redshift galaxy cluster candidates from Planck with the William Herschel Telescope
Optical follow-up of 32 Planck SZ candidates yields photometric redshifts and richness estimates confirming 18 (7) as at least half as rich as expected at z>0.5 (z>0.8), highlighting Eddington bias and projection effects.