Investigating the Redshift Evolution of Lensing Galaxy Density Slopes via Model-Independent Distance Ratios
Shuaibo Geng, Margherita Grespan, Hareesh Thuruthipilly, Sreekanth, Harikumar, Agnieszka Pollo, Marek Biesiada

TL;DR
This study introduces a model-independent method using artificial neural networks to analyze the redshift evolution of galaxy density slopes in strong lensing systems, providing insights into galaxy evolution and cosmology.
Contribution
The paper presents a novel, dark energy-model-independent approach employing ANNs and an extended power-law model to study the evolution of lensing galaxy density slopes.
Findings
Negative trend in density slope with redshift ($eta = -0.20 \
Robust estimates achieved through multi-level analysis.
Forecasts suggest high precision in constraining slope evolution with upcoming surveys.
Abstract
Strong lensing systems, expected to be abundantly discovered by next-generation surveys, offer a powerful tool for studying cosmology and galaxy evolution. The connection between galaxy structure and cosmology through distance ratios highlights the need to examine the evolution of lensing galaxy mass density profiles. We propose a novel, dark energy-model-independent method to investigate the mass density slopes of lensing galaxies and their redshift evolution using an extended power-law (EPL) model. We employ a non-parametric approach based on Artificial Neural Networks (ANNs) trained on Type Ia Supernovae (SNIa) data to reconstruct distance ratios of strong lensing systems. These ratios are compared with theoretical predictions to estimate the evolution of EPL model parameters. Analyses conducted at three levels, including the combined sample, individual lenses, and binned groups,…
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Taxonomy
TopicsAdaptive optics and wavefront sensing · Remote Sensing and LiDAR Applications · Advanced Optical Sensing Technologies
