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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First near-IR weak-lensing analysis of CANDELS fields detects 12 shear-selected overdensities with masses 0.2-2.2 x 10^14 solar masses at redshifts 0.22-0.9 and mean z=0.68.
Galaxy clusters have an average projected ellipticity of 0.310 with axis ratio 0.527, showing no significant mass or redshift dependence, measured via weak lensing on DES Y3 data and verified with mocks.
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.
ARCH introduces a staged optimization pipeline for joint shear-flexion gravitational lensing reconstruction that recovers cluster and subcluster masses consistent with prior weak+strong lensing results on Abell 2744 and El Gordo.
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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Near-IR Weak-lensing (NIRWL) Measurements in the CANDELS Fields. II. Mass Mapping and Overdensity Characterization
First near-IR weak-lensing analysis of CANDELS fields detects 12 shear-selected overdensities with masses 0.2-2.2 x 10^14 solar masses at redshifts 0.22-0.9 and mean z=0.68.
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Constraining Galaxy Cluster Triaxiality via Weak Lensing -- I. Preparation for the Rubin Data Beyond Leading Order
Galaxy clusters have an average projected ellipticity of 0.310 with axis ratio 0.527, showing no significant mass or redshift dependence, measured via weak lensing on DES Y3 data and verified with mocks.
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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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Adaptive Reconstruction of Cluster Halos (ARCH): Integrating Shear and Flexion for Substructure Detection
ARCH introduces a staged optimization pipeline for joint shear-flexion gravitational lensing reconstruction that recovers cluster and subcluster masses consistent with prior weak+strong lensing results on Abell 2744 and El Gordo.