Abstract Details
| Name: Prateek Gupta Affiliation: Thüringer Landessternwarte Karl-Schwarzschild- Observatorium Conference ID: ASI2024_1011 Title : Grouping with the measure of increased tie with gravity order (MITRO): An adaptive Friends-of-Friends algorithm Authors and Co-Authors : Prateek Gupta,1,2, Surajit Paul,1,3 Abstract Type : Poster Abstract Category : Galaxies and Cosmology Abstract : The Universe at the present epoch is found to be a network of matter overdense and underdense regions. Usually, the overdense regions are dominated by the dark-matter (DM) filaments where massive gravitationally bound structures such as groups and clusters of galaxies form at the nodes. To date, this picture of the Universe is best revealed through cosmological large-volume simulations and large-scale galaxy redshift surveys, in which the most important step is the appropriate identification of structures. So far, these structures are identified using various group finding codes, mostly based on the friend of friends (FoF) or spherical overdensity (SO) algorithms. Although the main purpose is to identify gravitationally bound structures, surprisingly, the mass information of the elements has hardly been used effectively by these codes. Moreover, the methods used so far either constrain the overdensity or use the real unstructured geometry only. Even though these are key factors in the accurate determination of structures-mass information that can precisely constrain the cosmological models of the Universe. In this work, we present our proposed algorithm which takes care of all the above-mentioned relevant features and ensures the bound structures by means of physical quantities, mainly mass and the total energy information. We introduced a novel concept of physically relevant arm length for each element depending on their individual masses leading to a distinct linking length for each unique pair of elements. Thus fundamentally, not only able to catch the gravitationally bound, real unstructured geometry very well, it does identify it roughly within a predefined physically motivated density threshold. Such a thing could not be simultaneously achieved before by any of the usual FoF or SO-based methods. We also demonstrate the unique ability of the algorithm in identifying structures, both from large volume cosmological simulations as well as from galaxy redshift surveys. |