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Register a new userBeyond two-point statistics: using the minimum spanning tree as a tool for cosmology
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Cosmological studies of large-scale structure have relied on two-point statistics, not fully exploiting the rich structure of the cosmic web. In this paper we show how to capture some of this cosmic web information by using the minimum spanning tree (MST), for the first time using it to estimate cosmological parameters in simulations. Discrete tracers of dark matter such as galaxies, $N$-body particles or haloes are used as nodes to construct a unique graph, the MST, that traces skeletal structure. We study the dependence of the MST on cosmological parameters using haloes from a suite of COLA simulations with a box size of $250 h^{-1}{rm Mpc}$, varying the amplitude of scalar fluctuations $left(A_{rm s}right)$, matter density $left(Omega_{rm m}right)$, and neutrino mass $left(sum m_{ u}right)$. The power spectrum $P$ and bispectrum $B$ are measured for wavenumbers between $0.125$ and $0.5$ $h{rm Mpc}^{-1}$, while a corresponding lower cut of $sim12.6$ $h^{-1}{rm Mpc}$ is applied to the MST. The constraints from the individual methods are fairly similar but when combined we see improved $1sigma$ constraints of $sim 17%$ ($sim 12%$) on $Omega_{rm m}$ and $sim 12%$ ($sim 10%$) on $A_{rm s}$ with respect to $P$ ($P+B$) thus showing the MST is providing additional information. The MST can be applied to current and future spectroscopic surveys (BOSS, DESI, Euclid, PSF, WFIRST, and 4MOST) in 3D and photometric surveys (DES and LSST) in tomographic shells to constrain parameters and/or test systematics.
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The minimum spanning tree clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose two minimum spanning trees based clustering algorithm. The first algorithm produces k clusters with center and guaranteed intra-cluster similarity. The radius and diameter of k clusters are computed to find the tightness of k clusters. The variance of the k clusters are also computed to find the compactness of the clusters. The second algorithm is proposed to create a dendrogram using the k clusters as objects with guaranteed inter-cluster similarity. The algorithm is also finds central cluster from the k number of clusters. The first algorithm uses divisive approach, where as the second algorithm uses agglomerative approach. In this paper we used both the approaches to find Informative Meta similarity clusters.
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