Mapping of cosmic dark matter with gravitational lensing analyses and applications of deep learning networks
DOI:
https://doi.org/10.51094/jxiv.65Keywords:
Cosmic dark matter, Gravitational lensing, Deep learning, Generative models, Generative adversarial networksAbstract
An array of large-scale astronomical observations has firmly confirmed the existence of invisible mass components in our Universe. Such invisible materials are referred to as cosmic dark matter. The observational studies have pointed out that dark matter can exist anywhere from the past to the present, but its existence can not be explained by the Standard Model of particle physics. Mapping of spatial distribution in dark matter density plays an essential role to identify the nature of cosmic dark matter. Gravitational lensing analyses are among the most powerful approaches to provide a large-scale map of cosmic dark matter from observations. The General Relativity predicts that intervening cosmic matter distribution causes a coherent distortion of images of background galaxies at large separations. This interesting phenomena is known as the gravitational lensing effect. Modern galaxy imaging surveys aim at inferring the dark matter density at dif- ferent line-of-sight directions by a precise measurement of gravitational lensing effects of billions of galaxies in a wide sky coverage. In this article, we begin with a brief summary of modern cosmology, and summarize basics of gravitational lensing analyses. We then de- scribe a recent progress in applications of deep learning to the gravitational-lensing-based mapping of dark matter, including our latest analysis.
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