# β-Skeleton analysis of the cosmic web

@article{Fang2018SkeletonAO, title={$\beta$-Skeleton analysis of the cosmic web}, author={Feng Fang and Jaime E. Forero-Romero and Graziano Rossi and Xiao-Dong Li and Longlong Feng}, journal={Monthly Notices of the Royal Astronomical Society}, year={2018} }

The $\beta$-skeleton is a mathematical method to construct graphs from a set of points that has been widely applied in the areas of image analysis, machine learning, visual perception, and pattern recognition. In this work, we apply the $\beta$-skeleton to study the cosmic web. We use this tool on observed and simulated data to identify the filamentary structures and characterize the statistical properties of the skeleton. In particular, we compare the $\beta$-skeletons built from SDSS-III…

## 7 Citations

### The Four Cosmic Tidal Web Elements from the β-skeleton

- Physics, Computer ScienceThe Astrophysical Journal
- 2021

A fast Machine Learning-based approach to infer the underlying dark matter tidal cosmic web environment of a galaxy distribution from its β-skeleton graph, and makes one of the highest ranking random forest models available on a public repository for future reference and reuse.

### Using the Mark Weighted Correlation Functions to Improve the Constraints on Cosmological Parameters

- PhysicsThe Astrophysical Journal
- 2020

We used the mark weighted correlation functions (MCFs), W(s), to study the large-scale structure of the universe. We studied five types of MCFs with the weighting scheme ρα, where ρ is the local…

### The cosmic web through the lens of graph entropy

- Physics, Computer Science
- 2020

The information theory entropy of a graph as a scalar is explored and it is argued that this entropy can be used as a discrete analogue of scalars used to quantify the connectivity in continuous density fields.

### Constraining cosmology with big data statistics of cosmological graphs

- Computer Science, PhysicsMonthly Notices of the Royal Astronomical Society
- 2020

Three simple graph-topological measures can effectively discriminate among the five model universes produced by cosmological N-body simulations, and are directly related with the usual n-points correlation functions of the cosmic density field.

### Cosmological parameter estimation from large-scale structure deep learning

- Computer ScienceScience China Physics, Mechanics & Astronomy
- 2020

This work shows that CNN can be more promising than people expected in deriving tight cosmological constraints from the cosmic large scale structure and exhibits robustness against smoothing, masking, random noise, global variation, rotation, reflection, and simulation resolution.

### Cosmic Velocity Field Reconstruction Using AI

- PhysicsThe Astrophysical Journal
- 2021

We develop a deep-learning technique to infer the nonlinear velocity field from the dark matter density field. The deep-learning architecture we use is a “U-net” style convolutional neural network,…

### Large-scale structures in the ΛCDM Universe: network analysis and machine learning

- Computer Science
- 2020

An analysis of the Cosmic Web as a complex network, which is built on a $\Lambda$CDM cosmological simulation, shows that it is not possible to give a good prediction of the topology of Cosmic Web based only on coordinates and velocities of nodes, yet network metrics can give a hint about the topological landscape of matter distribution.

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