Periodic Reporting for period 1 - ELATE (Evolution of Galaxies in large-scale structures)
Reporting period: 2022-11-01 to 2024-10-31
The large-scale structure (LSS) of the Universe is a network of galaxies in groups, clusters, superclusters, walls, filaments, separated by voids, shaped by gravity. According to the Lambda-Cold Dark Matter (ΛCDM) cosmological model for galaxy formation, the densest part of the LSS is built over time by accreting smaller clusters and groups of galaxies along the filaments. The properties of galaxies are known to be distinct in dense environments as compared to those in isolation (or the field), which are evident in observations and simulations. Within these dense environments, different mechanisms (e.g. ram-pressure stripping; Gunn & Gott 1972) can affect galaxies such that they undergo several changes in their structure and morphology as a result of gas-removal and subsequent suppression of star-formation, and as explained by the morphology-density relation (Dressler 1980). The infalling galaxies which contribute to the cluster mass can experience environmental mechanisms even before they reach the core of a cluster, otherwise known as "pre-processing" (Fujita 2004). In order to understand pre-processing of galaxies in cosmic filaments, we require a large sample of galaxies located in various parts of supercluster. Extracting such features from real astronomical data also require robust tools and methodologies.
The project aims to enhance our understanding of the role of the environment in shaping galaxies by exploring all regions in the large-scale structure (LSS). The main scientific goals are to robustly define the true location of galaxies in the Fornax-Eridanus complex and probe the influence of the local environment on galaxy morphology, which serves as a precursor for future studies concerning the evolution of galaxies in the cosmic web. We employ the novel machine learning tool box called 1DREAM (1 Dimensional Recovery Extraction and Analysis of Manifolds; Canducci et al. 2022) to extract filaments of the Fornax-Eridanus supercluster based on the galaxy-distance catalogue by Tempel et al. 2016, in order to study their structure and galactic properties in three-dimensional (3D) space.
The project aims to enhance our understanding of the role of the environment in shaping galaxies by exploring all regions in the large-scale structure (LSS). The main scientific goals are to robustly define the true location of galaxies in the Fornax-Eridanus complex and probe the influence of the local environment on galaxy morphology, which serves as a precursor for future studies concerning the evolution of galaxies in the cosmic web. We employ the novel machine learning tool box called 1DREAM (1 Dimensional Recovery Extraction and Analysis of Manifolds; Canducci et al. 2022) to extract filaments of the Fornax-Eridanus supercluster based on the galaxy-distance catalogue by Tempel et al. 2016, in order to study their structure and galactic properties in three-dimensional (3D) space.
Work performed:
1. Clean and process the dataset. Extract the region of interest which is a data cube of side-length 20 Mpc surrounding the Fornax-Eridanus Supercluster.
2. Run the machine learning tool box 1DREAM, which comprises 5 different algorithms. Each algorithm requires different input settings suited to the dataset (explained in detail in Raj et al. 2024)
3. Optimised-process to extract 3D filaments from the dataset.
4. Extract the distance of galaxies to their cosmic filaments.
5. Analyse the properties (stellar mass, morphology) of galaxies with respect to the filamentary axis.
5. Provide a comprehensive view of the Fornax-Eridanus supercluster environment.
Main achievements:
1. We explored the Fornax-Eridanus Supercluster using the novel machine learning toolbox called 1DREAM and gave a comprehensive view of galaxies in this region.
2. We identified 27 filaments, heterogenous in nature, and are connected to the Fornax-Eridanus Supercluster.
3. We corroborated the morphology-density relation of galaxies extends to cosmic filaments and walls by showing that the fraction of the early-type galaxies decreases with increasing distances from the filament spine.
The results and novel methodology adopted are published in Raj et al. 2024.
1. Clean and process the dataset. Extract the region of interest which is a data cube of side-length 20 Mpc surrounding the Fornax-Eridanus Supercluster.
2. Run the machine learning tool box 1DREAM, which comprises 5 different algorithms. Each algorithm requires different input settings suited to the dataset (explained in detail in Raj et al. 2024)
3. Optimised-process to extract 3D filaments from the dataset.
4. Extract the distance of galaxies to their cosmic filaments.
5. Analyse the properties (stellar mass, morphology) of galaxies with respect to the filamentary axis.
5. Provide a comprehensive view of the Fornax-Eridanus supercluster environment.
Main achievements:
1. We explored the Fornax-Eridanus Supercluster using the novel machine learning toolbox called 1DREAM and gave a comprehensive view of galaxies in this region.
2. We identified 27 filaments, heterogenous in nature, and are connected to the Fornax-Eridanus Supercluster.
3. We corroborated the morphology-density relation of galaxies extends to cosmic filaments and walls by showing that the fraction of the early-type galaxies decreases with increasing distances from the filament spine.
The results and novel methodology adopted are published in Raj et al. 2024.
This project highlights the cosmic web around the Fornax cluster, revealing diverse filamentary environments and reaffirming the heterogeneity proposed by Kuchner et al. (2022). Morphological segregation in the Fornax Wall is attributed to pre-processing within embedded groups and galaxy inflow from the Fornax Void. The 1-DREAM tool proved reliable for modelling filamentary structures, essential for studying environmental impacts on galaxies. Future work will focus on the Fornax Wall's structure and galaxy population characteristics within these filaments.
Impact: The Fornax region has been a topic of interest owing to its proximity to us and has a plethora of data. Recently, the Euclid Survey has also studied some areas of region (e.g. Saifollahi et al. 2024) and as such will be exploring surrounding regions of this cluster (see Scaramella et al. 2022). Additionally, the filaments detected as part of this project were used in an MSc thesis to study low-surface brightness galaxies (data from the Dark Energy Survey) located in the Fornax Eridanus Supercluster. Therefore this project sets a benchmark for prospective scientific projects with next-generation telescopes.
Impact: The Fornax region has been a topic of interest owing to its proximity to us and has a plethora of data. Recently, the Euclid Survey has also studied some areas of region (e.g. Saifollahi et al. 2024) and as such will be exploring surrounding regions of this cluster (see Scaramella et al. 2022). Additionally, the filaments detected as part of this project were used in an MSc thesis to study low-surface brightness galaxies (data from the Dark Energy Survey) located in the Fornax Eridanus Supercluster. Therefore this project sets a benchmark for prospective scientific projects with next-generation telescopes.