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We investigate the spatial distribution of Ly α-emitting galaxies (LAEs) at z ≈ 2.67, selected from the NOAO Deep Wide-Field Survey, using two-point statistics and topological diagnostics adopted from network science. We measure the clustering length, r 0 ≈ 4  h −1 Mpc, and the bias,   |$b_{\mathrm{ LAE}} = 2.2^{+0.2}_{-0.1}$|⁠. Fitting the clustering with halo occupation distribution (HOD) models results in two disparate possibilities: (1) where the fraction of central galaxies is <1 per cent in haloes of mass >1012 M⊙ and (2) where the fraction is ≈20 per cent. We refer to these two scenarios as the 'Dusty Core Scenario' for Model#1, since most of the central galaxies in massive haloes are dead in Ly α emission, and the 'Pristine Core Scenario' for Model#2, since the central galaxies are bright in Ly α emission. Traditional two-point statistics cannot distinguish between these disparate models given the current data sets. To overcome this degeneracy, we generate mock catalogues for each HOD model using a high-resolution N -body simulation and adopt a network statistics approach, which provides excellent topological diagnostics for galaxy point distributions. We find three topological anomalies from the spatial distribution of observed LAEs, which are not reproduced by the HOD mocks. We find that Model#2 matches better all network statistics than Model#1, suggesting that the central galaxies in >1012  h −1 M⊙ haloes at z ≈ 2.67 need to be less dusty to be bright as LAEs, potentially implying some replenishing channels of pristine gas such as the cold mode accretion. [ABSTRACT FROM AUTHOR]

We apply simple analyses techniques developed for the study of complex networks to the study of the cosmic web, the large-scale galaxy distribution. In this paper, we measure three network centralities (ranks of topological importance): degree centrality (DC), closeness centrality (CL), and betweenness centrality (BC) from a network built from the Cosmological Evolution Survey (COSMOS) catalogue. We define eight galaxy populations according to the centrality measures: void, wall, and cluster by DC; main branch and dangling leaf by BC; and kernel, backbone, and fracture by CL. We also define three populations by Voronoi tessellation density to compare these with the DC selection. We apply the topological selections to galaxies in the (photometric) redshift range 0.91 < z < 0.94 from the COSMOS survey, and explore whether the red and blue galaxy populations show differences in colour, star formation rate, and stellar mass in the different topological regions. Despite the limitations and uncertainties associated with using photometric redshift and indirect measurements of galactic parameters, the preliminary results illustrate the potential of network analysis. Future surveys will provide better statistical samples to test and improve this 'network cosmology'. [ABSTRACT FROM AUTHOR]

We estimate the redshift-dependent, anisotropic clustering signal in the Dark Energy Spectroscopic Instrument (DESI) Year 1 Survey created by tidal alignments of Luminous Red Galaxies (LRGs) and a selection-induced galaxy orientation bias. To this end, we measured the correlation between LRG shapes and the tidal field with DESI's Year 1 redshifts, as traced by LRGs and Emission-Line Galaxies. We also estimate the galaxy orientation bias of LRGs caused by DESI's aperture-based selection, and find it to increase by a factor of seven between redshifts 0.4−1.1 due to redder, fainter galaxies falling closer to DESI's imaging selection cuts. These effects combine to dampen measurements of the quadrupole of the correlation function (ξ2) caused by structure growth on scales of 10–80 h −1 Mpc by about 0.15 per cent for low redshifts (0.4 < z < 0.6) and 0.8 per cent for high (0.8 < z < 1.1), a significant fraction of DESI's error budget. We provide estimates of the ξ2 signal created by intrinsic alignments that can be used to correct this effect, which is necessary to meet DESI's forecasted precision on measuring the growth rate of structure. While imaging quality varies across DESI's footprint, we find no significant difference in this effect between imaging regions in the Legacy Imaging Survey. [ABSTRACT FROM AUTHOR]

The large-scale distribution of galaxies is generally analysed using the two-point correlation function. However, this statistic does not capture the topology of the distribution, and it is necessary to resort to higher order correlations to break degeneracies. We demonstrate that an alternate approach using network analysis can discriminate between topologically different distributions that have similar two-point correlations. We investigate two galaxy point distributions, one produced by a cosmological simulation and the other by a Lévy walk. For the cosmological simulation, we adopt the redshift z = 0.58 slice from Illustris and select galaxies with stellar masses greater than 108M☉. The two-point correlation function of these simulated galaxies follows a single power law, ξ (r) ∼ r-1.5. Then, we generate Lévy walks matching the correlation function and abundance with the simulated galaxies. We find that, while the two simulated galaxy point distributions have the same abundance and two-point correlation function, their spatial distributions are very different; most prominently, filamentary structures, absent in Lévy fractals. To quantify these missing topologies, we adopt network analysis tools and measure diameter, giant component, and transitivity from networks built by a conventional friends-of-friends recipe with various linking lengths. Unlike the abundance and two-point correlation function, these network quantities reveal a clear separation between the two simulated distributions; therefore, the galaxy distribution simulated by Illustris is not a Lévy fractal quantitatively. We find that the described network quantities offer an efficient tool for discriminating topologies and for comparing observed and theoretical distributions. [ABSTRACT FROM AUTHOR]

We evaluate the impact of imaging systematics on the clustering of luminous red galaxies (LRG), emission-line galaxies (ELG), and quasars (QSO) targeted for the upcoming Dark Energy Spectroscopic Instrument (DESI) survey. Using Data Release 7 of the DECam Legacy Survey, we study the effects of astrophysical foregrounds, stellar contamination, differences between north galactic cap and south galactic cap measurements, and variations in imaging depth, stellar density, galactic extinction, seeing, airmass, sky brightness, and exposure time before presenting survey masks and weights to mitigate these effects. With our sanitized samples in hand, we conduct a preliminary analysis of the clustering amplitude and evolution of the DESI main targets. From measurements of the angular correlation functions, we determine power law fits |$r_0 = 7.78 \pm 0.26\, h^{-1}$| Mpc, γ  = 1.98 ± 0.02 for LRGs and |$r_0 = 5.45 \pm 0.1\, h^{-1}$| Mpc, γ  = 1.54 ± 0.01 for ELGs. Additionally, from the angular power spectra, we measure the linear biases and model the scale-dependent biases in the weakly non-linear regime. Both sets of clustering measurements show good agreement with survey requirements for LRGs and ELGs, attesting that these samples will enable DESI to achieve precise cosmological constraints. We also present clustering as a function of magnitude, use cross-correlations with external spectroscopy to infer dN/dz and measure clustering as a function of luminosity, and probe higher order clustering statistics through counts-in-cells moments. [ABSTRACT FROM AUTHOR]

By utilizing large-scale graph analytic tools implemented in the modern big data platform, apache spark , we investigate the topological structure of gravitational clustering in five different universes produced by cosmological N -body simulations with varying parameters: (1) a WMAP 5-yr compatible ΛCDM cosmology, (2) two different dark energy equation of state variants, and (3) two different cosmic matter density variants. For the big data calculations, we use a custom build of standalone Spark/Hadoop cluster at Korea Institute for Advanced Study and Dataproc Compute Engine in Google Cloud Platform with sample sizes ranging from 7 to 200 million. We find that among the many possible graph-topological measures, three simple ones: (1) the average of number of neighbours (the so-called average vertex degree) α, (2) closed-to-connected triple fraction (the so-called transitivity) |$\tau _\Delta$|⁠ , and (3) the cumulative number density n s ≥ 5 of subgraphs with connected component size s ≥ 5, can effectively discriminate among the five model universes. Since these graph-topological measures are directly related with the usual n -points correlation functions of the cosmic density field, graph-topological statistics powered by big data computational infrastructure opens a new, intuitive, and computationally efficient window into the dark Universe. [ABSTRACT FROM AUTHOR]