Spatial Clustering of Galaxies in Large Datasets

Datasets with tens of millions of galaxies present new challenges for the analysis of spatial clustering. We have built a framework that integrates a database of object catalogs, tools for creating masks of bad regions, and a fast (NlogN) correlation code. This system has enabled unprecedented efficiency in carrying out the analysis of galaxy clustering in the SDSS catalog. A similar approach is used to compute the three-dimensional spatial clustering of galaxies on very large scales. We describe our strategy to estimate the effect of photometric errors using a database. We discuss our efforts as an early example of data-intensive science. While it would have been possible to get these results without the framework we describe, it will be infeasible to perform these computations on the future huge datasets without using this framework.Comment: original documents at http://research.microsoft.com/scripts/pubs/view.asp?TR_ID=MSR-TR-2002-8

Photometric redshift galaxies as tracers of the filamentary network

Galaxy filaments are the dominant feature in the overall structure of the cosmic web. The study of the filamentary web is an important aspect in understanding galaxy evolution and the evolution of matter in the Universe. A map of the filamentary structure is an adequate probe of the web. We propose that photometric redshift galaxies are significantly positively associated with the filamentary structure detected from the spatial distribution of spectroscopic redshift galaxies. The catalogues of spectroscopic and photometric galaxies are seen as point-process realisations in a sphere, and the catalogue of filamentary spines is proposed to be a realisation of a random set in a sphere. The positive association between these sets was studied using a bivariate $J-$function, which is a summary statistics studying clustering. A quotient $D$ was built to estimate the distance distribution of the filamentary spine to galaxies in comparison to the distance distribution of the filamentary spine to random points in $3-$dimensional Euclidean space. This measure gives a physical distance scale to the distances between filamentary spines and the studied sets of galaxies. The bivariate $J-$function shows a statistically significant clustering effect in between filamentary spines and photometric redshift galaxies. The quotient $D$ confirms the previous result that smaller distances exist with higher probability between the photometric galaxies and filaments. The trend of smaller distances between the objects grows stronger at higher redshift. Additionally, the quotient $D$ for photometric galaxies gives a rough estimate for the filamentary spine width of about $1$~Mpc. Photometric redshift galaxies are positively associated with filamentary spines detected from the spatial distribution of spectroscopic galaxies.Comment: Accepted to Astronomy & Astrophysics. 13 pages and 9 figure

Clustering-based Redshift Estimation: Comparison to Spectroscopic Redshifts

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by M\'enard et al. (2013). This technique enables the inference of redshift distributions from measurements of the spatial clustering of arbitrary sources, using a set of reference objects for which redshifts are known. We apply it to a sample of spectroscopic galaxies from the Sloan Digital Sky Survey and show that, after carefully controlling the sampling efficiency over the sky, we can estimate redshift distributions with high accuracy. Probing the full colour space of the SDSS galaxies, we show that we can recover the corresponding mean redshifts with an accuracy ranging from $\delta$z=0.001 to 0.01. We indicate that this mapping can be used to infer the redshift probability distribution of a single galaxy. We show how the lack of information on the galaxy bias limits the accuracy of the inference and show comparisons between clustering redshifts and photometric redshifts for this dataset. This analysis demonstrates, using real data, that clustering-based redshift inference provides a powerful data-driven technique to explore the redshift distribution of arbitrary datasets, without any prior knowledge on the spectral energy distribution of the sources.Comment: 13 pages. Submitted to MNRAS. Comments welcom

On the evolution of clustering of 24um-selected galaxies

This paper investigates the clustering properties of a complete sample of 1041 24um-selected sources brighter than F[24um]=400 uJy in the overlapping region between the SWIRE and UKIDSS UDS surveys. We have concentrated on the two (photometric) interval ranges z=[0.6-1.2] (low-z sample) and z>1.6 (high-z sample) as it is in these regions were we expect the mid-IR population to be dominated by intense dust-enshrouded activity such as star formation and black hole accretion. Investigations of the angular correlation function produce a correlation length are r0~15.9 Mpc for the high-z sample and r0~8.5 Mpc for the low-z one. Comparisons with physical models reveal that the high-z sources are exclusively associated with very massive (M>~10^{13} M_sun)haloes, comparable to those which locally host groups-to-clusters of galaxies, and are very common within such (rare) structures. Conversely, lower-z galaxies are found to reside in smaller halos (M_min~10^{12} M_sun) and to be very rare in such systems. While recent studies have determined a strong evolution of the 24um luminosity function between z~2 and z~0, they cannot provide information on the physical nature of such an evolution. Our clustering results instead indicate that this is due to the presence of different populations of objects inhabiting different structures, as active systems at z<~1.5 are found to be exclusively associated with low-mass galaxies, while very massive sources appear to have concluded their active phase before this epoch. Finally, we note that the small-scale clustering data seem to require steep profiles for the distribution of galaxies within their halos. This is suggestive of close encounters and/or mergers which could strongly favour both AGN and star-formation activity.Comment: 13 pages, 8 figures, to appear in MNRA

Measuring galaxy segregation using the mark connection function

(abridged) The clustering properties of galaxies belonging to different luminosity ranges or having different morphological types are different. These characteristics or `marks' permit to understand the galaxy catalogs that carry all this information as realizations of marked point processes. Many attempts have been presented to quantify the dependence of the clustering of galaxies on their inner properties. The present paper summarizes methods on spatial marked statistics used in cosmology to disentangle luminosity, colour or morphological segregation and introduces a new one in this context, the mark connection function. The methods used here are the partial correlation functions, including the cross-correlation function, the normalised mark correlation function, the mark variogram and the mark connection function. All these methods are applied to a volume-limited sample drawn from the 2dFGRS, using the spectral type as the mark. We show the virtues of each method to provide information about the clustering properties of each population, the dependence of the clustering on the marks, the similarity of the marks as a function of the pair distances, and the way to characterise the spatial correlation between the marks. We demonstrate by means of these statistics that passive galaxies exhibit stronger spatial correlation than active galaxies at small scales (r <20 Mpc/h). The mark connection function, introduced here, is particularly useful for understanding the spatial correlation between the marks.Comment: 6 pages, 5 figures, accepted for publication in Astronomy and Astrophysic

The Aemulus Project III: Emulation of the Galaxy Correlation Function

Using the N-body simulations of the AEMULUS Project, we construct an emulator for the non-linear clustering of galaxies in real and redshift space. We construct our model of galaxy bias using the halo occupation framework, accounting for possible velocity bias. The model includes 15 parameters, including both cosmological and galaxy bias parameters. We demonstrate that our emulator achieves ~ 1% precision at the scales of interest, 0.1<r<10 h^{-1} Mpc, and recovers the true cosmology when tested against independent simulations. Our primary parameters of interest are related to the growth rate of structure, f, and its degenerate combination fsigma_8. Using this emulator, we show that the constraining power on these parameters monotonically increases as smaller scales are included in the analysis, all the way down to 0.1 h^{-1} Mpc. For a BOSS-like survey, the constraints on fsigma_8 from r<30 h^{-1} Mpc scales alone are more than a factor of two tighter than those from the fiducial BOSS analysis of redshift-space clustering using perturbation theory at larger scales. The combination of real- and redshift-space clustering allows us to break the degeneracy between f and sigma_8, yielding a 9% constraint on f alone for a BOSS-like analysis. The current AEMULUS simulations limit this model to surveys of massive galaxies. Future simulations will allow this framework to be extended to all galaxy target types, including emission-line galaxies.Comment: 14 pages, 8 figures, 1 table; submitted to ApJ; the project webpage is available at https://aemulusproject.github.io ; typo in Figure 7 and caption updated, results unchange