QUASII: QUery-Aware Spatial Incremental Index.

With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can be executed efficiently. Such an indexing step requires substantial computing resources and introduces a considerable and growing data-to-insight gap where scientists need to wait before they can perform any analysis. Moreover, scientists often only use a small fraction of the data - the parts containing interesting phenomena - and indexing it fully does not always pay off. In this paper we develop a novel incremental index for the exploration of spatial data. Our approach, QUASII, builds a data-oriented index as a side-effect of query execution. QUASII distributes the cost of indexing across all queries, while building the index structure only for the subset of data queried. It reduces data-to-insight time and curbs the cost of incremental indexing by gradually and partially sorting the data, while producing a data-oriented hierarchical structure at the same time. As our experiments show, QUASII reduces the data-to-insight time by up to a factor of 11.4x, while its performance converges to that of the state-of-the-art static indexes

Modeling the connection between ultraviolet and infrared galaxy populations across cosmic times

Using a phenomenological approach, we self-consistently model the redshift evolution of the ultraviolet (UV) and infrared (IR) luminosity functions across cosmic time, as well as a range of observed IR properties of UV-selected galaxy population. This model is an extension of the 2SFM (2 star-formation modes) formalism, which is based on the observed "main-sequence" of star-forming galaxies, i.e. a strong correlation between their stellar mass and their star formation rate (SFR), and a secondary population of starbursts with an excess of star formation. The balance between the UV light from young, massive stars and the dust-reprocessed IR emission is modeled following the empirical relation between the attenuation (IRX for IR excess hereafter) and the stellar mass, assuming a scatter of 0.4\,dex around this relation. We obtain a good overall agreement with the measurements of the IR luminosity function up to z~3 and the UV luminosity functions up to z~6, and show that a scatter on the IRX-M relation is mandatory to reproduce these observables. We also naturally reproduce the observed, flat relation between the mean IRX and the UV luminosity at LUV>109.5 L⊙. Finally, we perform predictions of the UV properties and detectability of IR-selected samples and the vice versa, and discuss the results in the context of the UV-rest-frame and sub-millimeter surveys of the next decade

Ultraviolet to infrared emission of z>1 galaxies: Can we derive reliable star formation rates and stellar masses?

We seek to derive star formation rates (SFR) and stellar masses (M_star) in distant galaxies and to quantify the main uncertainties affecting their measurement. We explore the impact of the assumptions made in their derivation with standard calibrations or through a fitting process, as well as the impact of the available data, focusing on the role of IR emission originating from dust. We build a sample of galaxies with z>1, all observed from the UV to the IR (rest frame). The data are fitted with the code CIGALE, which is also used to build and analyse a catalogue of mock galaxies. Models with different SFHs are introduced. We define different set of data, with or without a good sampling of the UV range, NIR, and thermal IR data. The impact of these different cases on the determination of M_star and SFR are analysed. Exponentially decreasing models with a redshift formation of the stellar population z ~8 cannot fit the data correctly. The other models fit the data correctly at the price of unrealistically young ages when the age of the single stellar population is taken to be a free parameter. The best fits are obtained with two stellar populations. As long as one measurement of the dust emission continuum is available, SFR are robustly estimated whatever the chosen model is, including standard recipes. M_star measurement is more subject to uncertainty, depending on the chosen model and the presence of NIR data, with an impact on the SFR-M_star scatter plot. Conversely, when thermal IR data from dust emission are missing, the uncertainty on SFR measurements largely exceeds that of stellar mass. Among all physical properties investigated here, the stellar ages are found to be the most difficult to constrain and this uncertainty acts as a second parameter in SFR measurements and as the most important parameter for M_star measurements.Comment: 14 pages, 14 figures, accepted for publication A&

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio among the joined datasets. More generally, no single proposed method can efficiently join two spatial datasets in a robust manner with respect to their data distributions. Some approaches do well for datasets with contrasting densities while others do better with similar densities. None of them does well when the datasets have locally divergent data distributions. In this paper we develop TRANSFORMERS, an efficient and robust spatial join approach that is indifferent to such variations of distribution among the joined data. TRANSFORMERS achieves this feat by departing from the state-of-the-art through adapting the join strategy and data layout to local density variations among the joined data. It employs a join method based on data-oriented partitioning when joining areas of substantially different local densities, whereas it uses big partitions (as in space-oriented partitioning) when the densities are similar, while seamlessly switching among these two strategies at runtime. We experimentally demonstrate that TRANSFORMERS outperforms state-of-the-art approaches by a factor of between 2 and 8

GAMA/H-ATLAS: common star-formation rate indicators and their dependence on galaxy physical parameters

We compare common star-formation rate (SFR) indicators in the local Universe in the GAMA equatorial fields (around 160 sq. deg.), using ultraviolet (UV) photometry from GALEX, far-infrared (FIR) and sub-millimetre (sub-mm) photometry from H-ATLAS, and Halpha spectroscopy from the GAMA survey. With a high-quality sample of 745 galaxies (median redshift 0.08), we consider three SFR tracers: UV luminosity corrected for dust attenuation using the UV spectral slope beta (SFRUV,corr), Halpha line luminosity corrected for dust using the Balmer decrement (BD) (SFRHalpha,corr), and the combination of UV and IR emission (SFRUV+IR). We demonstrate that SFRUV,corr can be reconciled with the other two tracers after applying attenuation corrections by calibrating IRX (i.e. the IR to UV luminosity ratio) and attenuation in the Halpha (derived from BD) against beta. However, beta on its own is very unlikely to be a reliable attenuation indicator. We find that attenuation correction factors depend on parameters such as stellar mass, z and dust temperature (Tdust), but not on Halpha equivalent width (EW) or Sersic index. Due to the large scatter in the IRX vs beta correlation, when compared to SFRUV+IR, the beta-corrected SFRUV,corr exhibits systematic deviations as a function of IRX, BD and Tdust

Single parameter galaxy classification: The Principal Curve through the multi-dimensional space of galaxy properties

We propose to describe the variety of galaxies from SDSS by using only one affine parameter. To this aim, we build the Principal Curve (P-curve) passing through the spine of the data point cloud, considering the eigenspace derived from Principal Component Analysis of morphological, physical and photometric galaxy properties. Thus, galaxies can be labeled, ranked and classified by a single arc length value of the curve, measured at the unique closest projection of the data points on the P-curve. We find that the P-curve has a "W" letter shape with 3 turning points, defining 4 branches that represent distinct galaxy populations. This behavior is controlled mainly by 2 properties, namely u-r and SFR. We further present the variations of several galaxy properties as a function of arc length. Luminosity functions variate from steep Schechter fits at low arc length, to double power law and ending in Log-normal fits at high arc length. Galaxy clustering shows increasing autocorrelation power at large scales as arc length increases. PCA analysis allowed to find peculiar galaxy populations located apart from the main cloud of data points, such as small red galaxies dominated by a disk, of relatively high stellar mass-to-light ratio and surface mass density. The P-curve allows not only dimensionality reduction, but also provides supporting evidence for relevant physical models and scenarios in extragalactic astronomy: 1) Evidence for the hierarchical merging scenario in the formation of a selected group of red massive galaxies. These galaxies present a log-normal r-band luminosity function, which might arise from multiplicative processes involved in this scenario. 2) Connection between the onset of AGN activity and star formation quenching, which appears in green galaxies when transitioning from blue to red populations. (Full abstract in downloadable version)Comment: Full abstract in downloadable versio

Clustering Properties of restframe UV selected galaxies I: the correlation length derived from GALEX data in the local Universe

We present the first measurements of the angular correlation function of galaxies selected in the far (1530 A) and near (2310 A) Ultraviolet from the GALEX survey fields overlapping SDSS DR5 in low galactic extinction regions. The area used covers 120 sqdeg (GALEX - MIS) down to magnitude AB = 22, yielding a total of 100,000 galaxies. The mean correlation length is ~ 3.7 \pm 0.6 Mpc and no significant trend is seen for this value as a function of the limiting apparent magnitude or between the GALEX bands. This estimate is close to that found from samples of blue galaxies in the local universe selected in the visible, and similar to that derived at z ~ 3 for LBGs with similar rest frame selection criteria. This result supports models that predict anti-biasing of star forming galaxies at low redshift, and brings an additional clue to the downsizing of star formation at z<1.Comment: Accepted for publication in GALEX Special ApJs, December 200