44 research outputs found

    MulGuisin, a Topological Network Finder and its Performance on Galaxy Clustering

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    We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and network-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm find networks most efficiently and it defines galaxy networks in a way that most closely resembles human vision.Comment: 15 pages,12 figure

    Graph Database Solution for Higher Order Spatial Statistics in the Era of Big Data

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    We present an algorithm for the fast computation of the general NN-point spatial correlation functions of any discrete point set embedded within an Euclidean space of Rn\mathbb{R}^n. Utilizing the concepts of kd-trees and graph databases, we describe how to count all possible NN-tuples in binned configurations within a given length scale, e.g. all pairs of points or all triplets of points with side lengths <rmax<r_{max}. Through bench-marking we show the computational advantage of our new graph based algorithm over more traditional methods. We show that all 3-point configurations up to and beyond the Baryon Acoustic Oscillation scale (∼\sim200 Mpc in physical units) can be performed on current SDSS data in reasonable time. Finally we present the first measurements of the 4-point correlation function of ∼\sim0.5 million SDSS galaxies over the redshift range 0.43<z<0.70.43<z<0.7.Comment: 9 pages, 8 figures, submitte

    The Universe is worth 64364^3 pixels: Convolution Neural Network and Vision Transformers for Cosmology

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    We present a novel approach for estimating cosmological parameters, Ωm\Omega_m, σ8\sigma_8, w0w_0, and one derived parameter, S8S_8, from 3D lightcone data of dark matter halos in redshift space covering a sky area of 40∘×40∘40^\circ \times 40^\circ and redshift range of 0.3<z<0.80.3 < z < 0.8, binned to 64364^3 voxels. Using two deep learning algorithms, Convolutional Neural Network (CNN) and Vision Transformer (ViT), we compare their performance with the standard two-point correlation (2pcf) function. Our results indicate that CNN yields the best performance, while ViT also demonstrates significant potential in predicting cosmological parameters. By combining the outcomes of Vision Transformer, Convolution Neural Network, and 2pcf, we achieved a substantial reduction in error compared to the 2pcf alone. To better understand the inner workings of the machine learning algorithms, we employed the Grad-CAM method to investigate the sources of essential information in activation maps of the CNN and ViT. Our findings suggest that the algorithms focus on different parts of the density field and redshift depending on which parameter they are predicting. This proof-of-concept work paves the way for incorporating deep learning methods to estimate cosmological parameters from large-scale structures, potentially leading to tighter constraints and improved understanding of the Universe.Comment: 23 pages, 10 figure
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