Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

Canducci, Marco; Taghribi, Abolfazl; Rijcke, S. De; Peletier, Reynier F.; Bunte, Kerstin; Tiňo, Peter

doi:10.1007/978-3-030-91608-4_49

Cited by 2 publications

(1 citation statement)

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“…In other words, the distribution of particles forming a given noisy manifold will be attributed a likelihood to belong to the manifold since the structure will be modelled as a mixture of Gaussian probability distributions. In this work, the manifolds are considered to be one-dimensional, however previous work demonstrated the efficiency of this modelling technique for higher dimensional manifolds with unknown topology (Canducci et al 2022b) or given spherical topology (Canducci et al 2021;Taghribi et al 2022b).…”

Section: Sgtmmentioning

confidence: 99%

Swarm Intelligence-based Extraction and Manifold Crawling Along the Large-Scale Structure

Petra¹,

Peletier²,

Canducci³

et al. 2023

Preprint

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The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to describe its properties and the properties of groups of galaxies as a function of their environment. In this work we analyze the previously introduced framework: 1-Dimensional Recovery, Extraction, and Analysis of Manifolds (1-DREAM) on N-body cosmological simulation data of the Cosmic Web. The 1-DREAM toolbox consists of five Machine Learning methods, whose aim is the extraction and modelling of 1-dimensional structures in astronomical big data settings. We show that 1-DREAM can be used to extract structures of different density ranges within the Cosmic Web and to create probabilistic models of them. For demonstration, we construct a probabilistic model of an extracted filament and move through the structure to measure properties such as local density and velocity. We also compare our toolbox with a collection of methodologies which trace the Cosmic Web. We show that 1-DREAM is able to split the network into its various environments with results comparable to the state-of-the-art methodologies. A detailed comparison is then made with the public code DisPerSE, in which we find that 1-DREAM is robust against changes in sample size making it suitable for analyzing sparse observational data, and finding faint and diffuse manifolds in low density regions.

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Section: Sgtmmentioning

confidence: 99%