SMR uses multi-channel map-encoded reinforcement learning to achieve roughly 10% better time utilization than greedy baselines for single-dish radio telescope scheduling.
Cluster Lenses
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
Clusters of galaxies are the most recently assembled, massive, bound structures in the Universe. As predicted by General Relativity, given their masses, clusters strongly deform space-time in their vicinity. Clusters act as some of the most powerful gravitational lenses in the Universe. Light rays traversing through clusters from distant sources are hence deflected, and the resulting images of these distant objects therefore appear distorted and magnified. Lensing by clusters occurs in two regimes, each with unique observational signatures. The strong lensing regime is characterized by effects readily seen by eye, namely, the production of giant arcs, multiple-images, and arclets. The weak lensing regime is characterized by small deformations in the shapes of background galaxies only detectable statistically. Cluster lenses have been exploited successfully to address several important current questions in cosmology: (i) the study of the lens(es) - understanding cluster mass distributions and issues pertaining to cluster formation and evolution, as well as constraining the nature of dark matter; (ii) the study of the lensed objects - probing the properties of the background lensed galaxy population - which is statistically at higher redshifts and of lower intrinsic luminosity thus enabling the probing of galaxy formation at the earliest times right up to the Dark Ages; and (iii) the study of the geometry of the Universe - as the strength of lensing depends on the ratios of angular diameter distances between the lens, source and observer, lens deflections are sensitive to the value of cosmological parameters and offer a powerful geometric tool to probe Dark Energy. In this review, we present the basics of cluster lensing and provide a current status report of the field.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
GIGA-Lens 2.0 scales strong gravitational lens modeling across up to 128 GPU nodes and demonstrates it on 100 simulated systems plus one real DESI lens.
citing papers explorer
-
SMR: Scheduler with Multi-Channel Map-Encoded Reinforcement Learning for Radio Telescopes
SMR uses multi-channel map-encoded reinforcement learning to achieve roughly 10% better time utilization than greedy baselines for single-dish radio telescope scheduling.
-
GIGA-Lens 2.0: Strong-Lens Modeling on Multiple GPU Nodes
GIGA-Lens 2.0 scales strong gravitational lens modeling across up to 128 GPU nodes and demonstrates it on 100 simulated systems plus one real DESI lens.