Speaker
Description
I present a multi-disciplinary study of the filamentary network around the Fornax-Eridanus Complex to probe the influence of the large-scale environment on galaxy morphology. To extract filaments from real data, we employ the novel machine-learning tool, 1-DREAM (1-Dimensional, Recovery, Extraction, and Analysis of Manifolds). We then use the morphology-density relation of galaxies to examine a galaxy's dependence with regard to its local environment in filaments. The detected filaments showcase a variety of environments and are heterogeneous in nature. In this context, we reveal a well-known structure -- the Fornax Wall, that passes through the Dorado group, Fornax cluster, and Eridanus supergroup. With regard to the morphology of galaxies, we find that early-type galaxies are prevalent in high-density filaments and high-density regions of the Fornax Wall. The next step is to incorporate deep photometry (e.g., truncation radius, colour gradients, asymmetries) from wide-field and deep images to comprehend the mechanisms responsible for the galaxies' morphological changes. Our study provides insights into pre-processing of galaxies in LSS by connecting their detected filaments, observational properties, and their faint outskirts. While there have been studies on the evolution of galaxies in large-scale structures, none have made comparisons of the same to the low-surface brightness (LSB) features of galaxies in the LSS which are only detectable in deep images. Such comparisons are vital – to understanding the role of the environment in shaping galaxies. Using 1DREAM, segmentation tools (e.g. Max-Tree Objects), and deep photometry, I will also briefly discuss the possibility of including LSB galaxies in filament-detection with Euclid.
