ﻻ يوجد ملخص باللغة العربية
I propose a new approach to free-form cluster lens modeling that is inspired by the JPEG image compression method. This approach is motivated specifically by the need for accurate modeling of high-magnification regions in galaxy clusters. Existing modeling methods may struggle in these regions due to their limited flexibility in the parametrization of the lens, even for a wide variety of free-form methods. This limitation especially hinders the characterization of faint galaxies at high redshifts, which have important implications for the formation of the first galaxies and even for the nature of dark matter. JPEG images are extremely accurate representations of their original, uncompressed counterparts but use only a fraction of number of parameters to represent that information. Its relevance is immediately obvious to cluster lens modeling. Using this technique, it is possible to construct flexible models that are capable of accurately reproducing the true mass distribution using only a small number of free parameters. Transferring this well-proven technology to cluster lens modeling, I demonstrate that this `JPEG parametrization is indeed flexible enough to accurately approximate an N-body simulated cluster.
We develop a novel statistical strong lensing approach to probe the cosmological parameters by exploiting multiple redshift image systems behind galaxies or galaxy clusters. The method relies on free-form mass inversion of strong lenses and does not
Hubble Frontier Fields (HFF) imaging of the most powerful lensing clusters provides access to the most magnified distant galaxies. The challenge is to construct lens models capable of describing these complex massive, merging clusters so that individ
Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with convolutional neural n
We examine the massive colliding cluster El Gordo, one of the most massive clusters at high redshift. We use a free-form lensing reconstruction method that avoids making assumptions about the mass distribution. We use data from the RELICS program and
We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling and reconstruction with quantization and entropy coding. The compression performance, in t