scholarly journals The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: anisotropic clustering analysis in configuration space

2018 ◽  
Vol 480 (2) ◽  
pp. 2521-2534 ◽  
Author(s):  
Jiamin Hou ◽  
Ariel G Sánchez ◽  
Román Scoccimarro ◽  
Salvador Salazar-Albornoz ◽  
Etienne Burtin ◽  
...  
Keyword(s):  
Configuration Space ◽  
Clustering Analysis ◽  
Anisotropic Clustering

scholarly journals Cosmological constraints from the anisotropic clustering analysis using BOSS DR9

2014 ◽  
Vol 89 (6) ◽  
Author(s):  
Eric V. Linder ◽  
Minji Oh ◽  
Teppei Okumura ◽  
Cristiano G. Sabiu ◽  
Yong-Seon Song
Keyword(s):  
Clustering Analysis ◽  
Cosmological Constraints ◽  
Anisotropic Clustering

scholarly journals The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: BAO and RSD measurements from anisotropic clustering analysis of the quasar sample in configuration space between redshift 0.8 and 2.2

2020 ◽  
Vol 500 (1) ◽  
pp. 1201-1221 ◽  
Author(s):  
Jiamin Hou ◽  
Ariel G Sánchez ◽  
Ashley J Ross ◽  
Alex Smith ◽  
Richard Neveux ◽  
...  
Keyword(s):  
Growth Rate ◽  
Configuration Space ◽  
Shape Analysis ◽  
Acoustic Oscillation ◽  
Matter Field ◽  
Sloan Digital Sky Survey ◽  
Linear Growth Rate ◽  
Angular Diameter Distance ◽  
Final Sample ◽  
Anisotropic Clustering

ABSTRACT We measure the anisotropic clustering of the quasar sample from Data Release 16 (DR16) of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey (eBOSS). A sample of 343 708 spectroscopically confirmed quasars between redshift 0.8 < z < 2.2 are used as tracers of the underlying dark matter field. In comparison with DR14 sample, the final sample doubles the number of objects as well as the survey area. In this paper, we present the analysis in configuration space by measuring the two-point correlation function and decomposing it using the Legendre polynomials. For the full-shape analysis of the Legendre multipole moments, we measure the baryon acoustic oscillation (BAO) distance and the growth rate of the cosmic structure. At an effective redshift of zeff = 1.48, we measure the comoving angular diameter distance DM(zeff)/rdrag = 30.66 ± 0.88, the Hubble distance DH(zeff)/rdrag = 13.11 ± 0.52, and the product of the linear growth rate and the rms linear mass fluctuation on scales of $8 \, h^{-1}\, {\rm Mpc}$, fσ8(zeff) = 0.439 ± 0.048. The accuracy of these measurements is confirmed using an extensive set of mock simulations developed for the quasar sample. The uncertainties on the distance and growth rate measurements have been reduced substantially (∼45 and ∼30 per cent) with respect to the DR14 results. We also perform a BAO-only analysis to cross check the robustness of the methodology of the full-shape analysis. Combining our analysis with the Fourier-space analysis, we arrive at $D^{{\bf c}}_{\rm M}(z_{\rm eff})/r_{\rm drag} = 30.21 \pm 0.79$, $D^{{\bf c}}_{\rm H}(z_{\rm eff})/r_{\rm drag} = 13.23 \pm 0.47$, and $f\sigma _8^{{\bf c}}(z_{\rm eff}) = 0.462 \pm 0.045$.

2249-PUB: Clustering Analysis as a Tool to Define Endotypes in Type 1 Diabetes (T1D)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2249-PUB
Author(s):  
ALEJANDRO F. SILLER ◽  
XIANGJUN GU ◽  
MUSTAFA TOSUR ◽  
MARCELA ASTUDILLO ◽  
ASHOK BALASUBRAMANYAM ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Clustering Analysis

A Level Inference Method for Aggregated Information of Objects Based on Clustering Analysis

2012 ◽  
Vol 34 (6) ◽  
pp. 1432-1437 ◽  
Author(s):  
Li-feng Cao ◽  
Xing-yuan Chen ◽  
Xue-hui Du ◽  
Chun-tao Xia
Keyword(s):  
Clustering Analysis ◽  
Inference Method

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Methylation Level ◽  
Optimal Number ◽  
Generative Model ◽  
Methylation Data ◽  
Sequencing Data ◽  
Number Of Clusters ◽  
Rna Methylation ◽  
Clustering Effect ◽  
Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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