We present photometry and derived redshifts from up to eleven bandpasses for 9927 galaxies in the Hubble Ultra Deep field (UDF), covering an observed wavelength range from the near-ultraviolet (NUV) to the near-infrared (NIR) with Hubble Space Telesc
ope observations. Our Wide Field Camera 3 (WFC3)/UV F225W, F275W, and F336W image mosaics from the ultra-violet UDF (UVUDF) imaging campaign are newly calibrated to correct for charge transfer inefficiency, and use new dark calibrations to minimize background gradients and pattern noise. Our NIR WFC3/IR image mosaics combine the imaging from the UDF09 and UDF12 campaigns with CANDELS data to provide NIR coverage for the entire UDF field of view. We use aperture-matched point-spread function corrected photometry to measure photometric redshifts in the UDF, sampling both the Lyman break and Balmer break of galaxies at z~0.8-3.4, and one of the breaks over the rest of the redshift range. Our comparison of these results with a compilation of robust spectroscopic redshifts shows an improvement in the galaxy photometric redshifts by a factor of two in scatter and a factor three in outlier fraction over previous UDF catalogs. The inclusion of the new NUV data is responsible for a factor of two decrease in the outlier fraction compared to redshifts determined from only the optical and NIR data, and improves the scatter at z<0.5 and at z>2. The panchromatic coverage of the UDF from the NUV through the NIR yields robust photometric redshifts of the UDF, with the lowest outlier fraction available.
We present the rest-frame UV wavelength dependence of the Petrosian-like half-light radius ($r_{50}$), and the concentration parameter for a sample of 198 star-forming galaxies at 0.5 < z < 1.5. We find a ~5% decrease in $r_{50}$ from 1500 AA to 3000
AA, with half-light radii at 3000 AA ranging from 0.6 kpc to 6 kpc. We also find a decrease in concentration of ~0.07 (1.9 < $C_{3000}$ < 3.9). The lack of a strong relationship between $r_{50}$ and wavelength is consistent with a model in which clumpy star formation is distributed over length scales comparable to the galaxys rest-frame optical light. While the wavelength dependence of $r_{50}$ is independent of size at all redshifts, concentration decreases more sharply in the far-UV (~1500 AA) for large galaxies at z ~ 1. This decrease in concentration is caused by a flattening of the inner ~20% of the light profile in disk-like galaxies, indicating that the central regions have different UV colors than the rest of the galaxy. We interpret this as a bulge component with older stellar populations and/or more dust. The size-dependent decrease in concentration is less dramatic at z ~ 2, suggesting that bulges are less dusty, younger, and/or less massive than the rest of the galaxy at higher redshifts.
We describe the infrared properties of sources detected over ~36 deg^2 of sky in the GAMA 15-hr equatorial field, using data from both the Herschel Astrophysical Terahertz Large-Area Survey (H-ATLAS) and Wide-field Infrared Survey (WISE). With 5-sigm
a point-source depths of 34 and 0.048 mJy at 250 micron and 3.4 micron, respectively, we are able to identify 50.6% of the H-ATLAS sources in the WISE survey, corresponding to a surface density of ~630 deg^{-2}. Approximately two-thirds of these sources have measured spectroscopic or optical/near-IR photometric redshifts of z<1. For sources with spectroscopic redshifts at z<0.3, we find a linear correlation between the infrared luminosity at 3.4 micron and that at 250 micron, with +-50% scatter over ~1.5 orders of magnitude in luminosity, ~10^9 - 10^{10.5} L_sun. By contrast, the matched sources without previously measured redshifts (r>~20.5) have 250-350 micron flux density ratios that suggest either high-redshift galaxies (z>~1.5) or optically faint low-redshift galaxies with unusually low temperatures (T<~20). Their small 3.4-250 micron flux ratios favor a high-redshift galaxy population, as only the most actively star-forming galaxies at low redshift (e.g., Arp 220) exhibit comparable flux density ratios. Furthermore, we find a relatively large AGN fraction (~30%) in a 12 micron flux-limited subsample of H-ATLAS sources, also consistent with there being a significant population of high-redshift sources in the no-redshift sample.
The far-ultraviolet (FUV) number counts of galaxies constrain the evolution of the star-formation rate density of the universe. We report the FUV number counts computed from FUV imaging of several fields including the Hubble Ultra Deep Field, the Hub
ble Deep Field North, and small areas within the GOODS-North and -South fields. These data were obtained with the Hubble Space Telescope Solar Blind Channel of the Advance Camera for Surveys. The number counts sample a FUV AB magnitude range from 21-29 and cover a total area of 15.9 arcmin^2, ~4 times larger than the most recent HST FUV study. Our FUV counts intersect bright FUV GALEX counts at 22.5 mag and they show good agreement with recent semi-analytic models based on dark matter merger trees by Somerville et al. (2011). We show that the number counts are ~35% lower than in previous HST studies that use smaller areas. The differences between these studies are likely the result of cosmic variance; our new data cover more lines of sight and more area than previous HST FUV studies. The integrated light from field galaxies is found to contribute between 65.9 +/-8 - 82.6 +/-12 photons/s/cm^2/sr/angstrom to the FUV extragalactic background. These measurements set a lower limit for the total FUV background light.
The three-point correlation function (3PCF) provides an important view into the clustering of galaxies that is not available to its lower order cousin, the two-point correlation function (2PCF). Higher order statistics, such as the 3PCF, are necessar
y to probe the non-Gaussian structure and shape information expected in these distributions. We measure the clustering of spectroscopic galaxies in the Main Galaxy Sample of the Sloan Digital Sky Survey (SDSS), focusing on the shape or configuration dependence of the reduced 3PCF in both redshift and projected space. This work constitutes the largest number of galaxies ever used to investigate the reduced 3PCF, using over 220,000 galaxies in three volume-limited samples. We find significant configuration dependence of the reduced 3PCF at 3-27 Mpc/h, in agreement with LCDM predictions and in disagreement with the hierarchical ansatz. Below 6 Mpc/h, the redshift space reduced 3PCF shows a smaller amplitude and weak configuration dependence in comparison with projected measurements suggesting that redshift distortions, and not galaxy bias, can make the reduced 3PCF appear consistent with the hierarchical ansatz. The reduced 3PCF shows a weaker dependence on luminosity than the 2PCF, with no significant dependence on scales above 9 Mpc/h. On scales less than 9 Mpc/h, the reduced 3PCF appears more affected by galaxy color than luminosty. We demonstrate the extreme sensitivity of the 3PCF to systematic effects such as sky completeness and binning scheme, along with the difficulty of resolving the errors. Some comparable analyses make assumptions that do not consistently account for these effects.
Virtual observatories will give astronomers easy access to an unprecedented amount of data. Extracting scientific knowledge from these data will increasingly demand both efficient algorithms as well as the power of parallel computers. Nearly all effi
cient analyses of large astronomical datasets use trees as their fundamental data structure. Writing efficient tree-based techniques, a task that is time-consuming even on single-processor computers, is exceedingly cumbersome on massively parallel platforms (MPPs). Most applications that run on MPPs are simulation codes, since the expense of developing them is offset by the fact that they will be used for many years by many researchers. In contrast, data analysis codes change far more rapidly, are often unique to individual researchers, and therefore accommodate little reuse. Consequently, the economics of the current high-performance computing development paradigm for MPPs does not favor data analysis applications. We have therefore built a library, called Ntropy, that provides a flexible, extensible, and easy-to-use way of developing tree-based data analysis algorithms for both serial and parallel platforms. Our experience has shown that not only does our library save development time, it can also deliver excellent serial performance and parallel scalability. Furthermore, Ntropy makes it easy for an astronomer with little or no parallel programming experience to quickly scale their application to a distributed multiprocessor environment. By minimizing development time for efficient and scalable data analysis, we enable wide-scale knowledge discovery on massive datasets.
