The clustering of massive galaxies at z~0.5 from the first semester of BOSS data

We calculate the real- and redshift-space clustering of massive galaxies at z~0.5 using the first semester of data by the Baryon Oscillation Spectroscopic Survey (BOSS). We study the correlation functions of a sample of 44,000 massive galaxies in the redshift range 0.4<z<0.7. We present a halo-occupation distribution modeling of the clustering results and discuss the implications for the manner in which massive galaxies at z~0.5 occupy dark matter halos. The majority of our galaxies are central galaxies living in halos of mass 10^{13}Msun/h, but 10% are satellites living in halos 10 times more massive. These results are broadly in agreement with earlier investigations of massive galaxies at z~0.5. The inferred large-scale bias (b~2) and relatively high number density (nbar=3e-4 h^3 Mpc^{-3}) imply that BOSS galaxies are excellent tracers of large-scale structure, suggesting BOSS will enable a wide range of investigations on the distance scale, the growth of large-scale structure, massive galaxy evolution and other topics.Comment: 11 pages, 12 figures, matches version accepted by Ap

Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output

Luminous Red Galaxies in Clusters: Central Occupation, Spatial Distributions, and Mis-centering

Luminous Red Galaxies (LRG) from the Sloan Digital Sky Survey are considered among the best understood samples of galaxies, and they are employed in a broad range of cosmological studies. Because they form a relatively homogeneous population, with high stellar masses and red colors, they are expected to occupy halos in a relatively simple way. In this paper, we study how LRGs occupy massive halos via direct counts in clusters and we reveal several unexpected trends suggesting that the connection between LRGs and dark matter halos may not be straightforward. Using the redMaPPer cluster catalog, we derive the central occupation of LRGs as a function richness, Ncen({\lambda}). Assuming no correlation between cluster mass and central galaxy luminosity at fixed richness, we show that clusters contain a significantly lower fraction of central LRGs than predicted from the two-point correlation function. At halo masses of 10^14.5 Msun, we find Ncen=0.73, compared to Ncen of 0.89 from correlation studies. Our central occupation function for LRGs converges to 0.95 at large halo masses. A strong anti-correlation between central luminosity and cluster mass at fixed richness is required to reconcile our results with those based on clustering studies. We also derive P_BNC, the probability that the brightest cluster member is not the central galaxy. We find P_BNC ~ 20-30% which is a factor of ~2 lower than the value found by Skibba et al. 2011. Finally, we study the radial offsets of bright non-central LRGs from cluster centers and show that bright non-central LRGs follow a different radial distribution compared to red cluster members, which follow a Navarro-Frank-White profile. This work demonstrates that even the most massive clusters do not always have an LRG at the center, and that the brightest galaxy in a cluster is not always the central galaxy.Comment: 18 pages, 9 figures, 4 tables, submitted to MNRAS, included the referee comment

Sloan Digital Sky Survey III Photometric Quasar Clustering: Probing the Initial Conditions of the Universe using the Largest Volume

The Sloan Digital Sky Survey has surveyed 14,555 square degrees of the sky, and delivered over a trillion pixels of imaging data. We present the large-scale clustering of 1.6 million quasars between z = 0.5 and z = 2.5 that have been classified from this imaging, representing the highest density of quasars ever studied for clustering measurements. This data set spans ~11,000 square degrees and probes a volume of 80(Gpc/h)^3. In principle, such a large volume and medium density of tracers should facilitate high-precision cosmological constraints. We measure the angular clustering of photometrically classified quasars using an optimal quadratic estimator in four redshift slices with an accuracy of ~25% over a bin width of l ~10 - 15 on scales corresponding to matter-radiation equality and larger (l ~ 2 - 30). Observational systematics can strongly bias clustering measurements on large scales, which can mimic cosmologically relevant signals such as deviations from Gaussianity in the spectrum of primordial perturbations. We account for systematics by employing a new method recently proposed by Agarwal et al. (2014) to the clustering of photometrically classified quasars. We carefully apply our methodology to mitigate known observational systematics and further remove angular bins that are contaminated by unknown systematics. Combining quasar data with the photometric luminous red galaxy (LRG) sample of Ross et al. (2011) and Ho et al. (2012), and marginalizing over all bias and shot noise-like parameters, we obtain a constraint on local primordial non-Gaussianity of fNL = -113+/-154 (1\sigma error). [Abridged]Comment: 35 pages, 15 figure

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application