Presents a fully photometric framework to measure individual cluster splashback radii and masses from SDSS data and constructs the first observational splashback mass function.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
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.
No significant environmental dependence is found for the galaxy size-mass relation at low redshift across clusters, voids, cluster masses, and cluster-centric distances.
AnaCal recovers input shear with low bias in high-shear cluster regimes under LSST-like conditions, producing 0.24% mean mass bias.
citing papers explorer
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The Splashback Mass Function of Galaxy Clusters from Photometric Data
Presents a fully photometric framework to measure individual cluster splashback radii and masses from SDSS data and constructs the first observational splashback mass function.
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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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From the Densest Clusters to the Emptiest Voids: No Evidence For Environmental Effects on the Galaxy Size-Mass Relation at Low Redshift
No significant environmental dependence is found for the galaxy size-mass relation at low redshift across clusters, voids, cluster masses, and cluster-centric distances.
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Accurate Galaxy Cluster Shear and Mass Calibration for LSST with AnaCal
AnaCal recovers input shear with low bias in high-shear cluster regimes under LSST-like conditions, producing 0.24% mean mass bias.