The Evolution of the Global Star Formation History as Measured from the Hubble Deep Field

The Hubble Deep Field (HDF) is the deepest set of multicolor optical photometric observations ever undertaken, and offers a valuable data set with which to study galaxy evolution. Combining the optical WFPC2 data with ground-based near-infrared photometry, we derive photometrically estimated redshifts for HDF galaxies with J<23.5. We demonstrate that incorporating the near-infrared data reduces the uncertainty in the estimated redshifts by approximately 40% and is required to remove systematic uncertainties within the redshift range 1<z<2. Utilizing these photometric redshifts, we determine the evolution of the comoving ultraviolet (2800 A) luminosity density (presumed to be proportional to the global star formation rate) from a redshift of z=0.5 to z=2. We find that the global star formation rate increases rapidly with redshift, rising by a factor of 12 from a redshift of zero to a peak at z~1.5. For redshifts beyond 1.5, it decreases monotonically. Our measures of the star formation rate are consistent with those found by Lilly et al. (1996) from the CFRS at z 2, and bridge the redshift gap between those two samples. The overall star formation or metal enrichment rate history is consistent with the predictions of Pei and Fall (1995) based on the evolving HI content of Lyman-alpha QSO absorption line systems.Comment: Latex format, 10 pages, 3 postscript figures. Accepted for publication in Ap J Letter

Redshift-Space Enhancement of Line-of-Sight Baryon Acoustic Oscillations in the SDSS Main-Galaxy Sample

We show that redshift-space distortions of galaxy correlations have a strong effect on correlation functions with distinct, localized features, like the signature of the baryon acoustic oscillations (BAO). Near the line of sight, the features become sharper as a result of redshift-space distortions. We demonstrate this effect by measuring the correlation function in Gaussian simulations and the Millennium Simulation. We also analyze the SDSS DR7 main-galaxy sample (MGS), splitting the sample into slices 2.5 degrees on the sky in various rotations. Measuring 2D correlation functions in each slice, we do see a sharp bump along the line of sight. Using Mexican-hat wavelets, we localize it to (110 +/- 10) Mpc/h. Averaging only along the line of sight, we estimate its significance at a particular wavelet scale and location at 2.2 sigma. In a flat angular weighting in the (pi,r_p) coordinate system, the noise level is suppressed, pushing the bump's significance to 4 sigma. We estimate that there is about a 0.2% chance of getting such a signal anywhere in the vicinity of the BAO scale from a power spectrum lacking a BAO feature. However, these estimates of the significances make some use of idealized Gaussian simulations, and thus are likely a bit optimistic.Comment: 17 pages, 27 figures. Minor changes to match final version accepted to Ap

IVOA Recommendation: VOTable Format Definition Version 1.3

This document describes the structures making up the VOTable standard. The main part of this document describes the adopted part of the VOTable standard; it is followed by appendices presenting extensions which have been proposed and/or discussed, but which are not part of the standard

Spectral Templates from Multicolor Redshift Surveys

Understanding how the physical properties of galaxies (e.g. their spectral type or age) evolve as a function of redshift relies on having an accurate representation of galaxy spectral energy distributions. While it has been known for some time that galaxy spectra can be reconstructed from a handful of orthogonal basis templates, the underlying basis is poorly constrained. The limiting factor has been the lack of large samples of galaxies (covering a wide range in spectral type) with high signal-to-noise spectrophotometric observations. To alleviate this problem we introduce here a new technique for reconstructing galaxy spectral energy distributions directly from samples of galaxies with broadband photometric data and spectroscopic redshifts. Exploiting the statistical approach of the Karhunen-Loeve expansion, our iterative training procedure increasingly improves the eigenbasis, so that it provides better agreement with the photometry. We demonstrate the utility of this approach by applying these improved spectral energy distributions to the estimation of photometric redshifts for the HDF sample of galaxies. We find that in a small number of iterations the dispersion in the photometric redshifts estimator (a comparison between predicted and measured redshifts) can decrease by up to a factor of 2.Comment: 25 pages, 9 figures, LaTeX AASTeX, accepted for publication in A

Transition to SDN is HARMLESS: Hybrid ARchitecture for Migrating Legacy Ethernet Switches to SDN

Software-Defined Networking (SDN) offers a new way to operate, manage, and deploy communication networks and to overcome many long-standing problems of legacy networking. However, widespread SDN adoption has not occurred yet due to the lack of a viable incremental deployment path and the relatively immature present state of SDN-capable devices on the market. While continuously evolving software switches may alleviate the operational issues of commercial hardware-based SDN offerings, namely lagging standards-compliance, performance regressions, and poor scaling, they fail to match the cost-efficiency and port density. In this paper, we propose HARMLESS, a new SDN switch design that seamlessly adds SDN capability to legacy network gear, by emulating the OpenFlow switch OS in a separate software switch component. This way, HARMLESS enables a quick and easy leap into SDN, combining the rapid innovation and upgrade cycles of software switches with the port density and cost-efficiency of hardware-based appliances into a fully dataplane-transparent and vendor-neutral solution. HARMLESS incurs an order of magnitude smaller initial expenditure for an SDN deployment than existing turnkey vendor SDN solutions while, at the same time, yields matching, or even better, data plane performance for smaller enterprises