A Deeper Look at the New Milky Way Satellites: Sagittarius II, Reticulum II, Phoenix II, and Tucana III

In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.

We present a full sky 100 micron map that is a reprocessed composite of the COBE/DIRBE and IRAS/ISSA maps, with the zodiacal foreground and confirmed point sources removed. Before using the ISSA maps, we remove the remaining artifacts from the IRAS scan pattern. Using the DIRBE 100 micron and 240 micron data, we have constructed a map of the dust temperature, so that the 100 micron map can be converted to a map proportional to dust column density. The result of these manipulations is a map with DIRBE-quality calibration and IRAS resolution. To generate the full sky dust maps, we must first remove zodiacal light contamination as well as a possible cosmic infrared background (CIB). This is done via a regression analysis of the 100 micron DIRBE map against the Leiden- Dwingeloo map of H_I emission, with corrections for the zodiacal light via a suitable expansion of the DIRBE 25 micron flux. For the 100 micron map, no significant CIB is detected. In the 140 micron and 240 micron maps, where the zodiacal contamination is weaker, we detect the CIB at surprisingly high flux levels of 32 \pm 13 nW/m^2/sr at 140 micron, and 17 \pm 4 nW/m^2/sr at 240 micron (95% confidence). This integrated flux is ~2 times that extrapolated from optical galaxies in the Hubble Deep Field. The primary use of these maps is likely to be as a new estimator of Galactic extinction. We demonstrate that the new maps are twice as accurate as the older Burstein-Heiles estimates in regions of low and moderate reddening. These dust maps will also be useful for estimating millimeter emission that contaminates CMBR experiments and for estimating soft X-ray absorption.

We present measurements of dust reddening using the colors of stars with spectra in the Sloan Digital Sky Survey. We measure reddening as the difference between the measured and predicted colors of a star, as derived from stellar parameters from the SEGUE Stellar Parameter Pipeline (Lee et al. 2008a). We achieve uncertainties of 56, 34, 25, and 29 mmag in the colors u-g, g-r, r-i, and i-z, per star, though the uncertainty varies depending on the stellar type and the magnitude of the star. The spectrum-based reddening measurements confirm our earlier "blue tip" reddening measurements (Schlafly et al. 2010, S10), finding reddening coefficients different by -3%, 1%, 1%, and 2% in u-g, g-r, r-i, and i-z from those found by the blue tip method, after removing a 4% normalization difference. These results prefer an R_V=3.1 Fitzpatrick (1999) reddening law to O'Donnell (1994) or Cardelli et al. (1989) reddening laws. We provide a table of conversion coefficients from the Schlegel et al. (1998) maps of E(B-V) to extinction in 88 bandpasses for 4 values of R_V, using this reddening law and the 14% recalibration of SFD first reported by S10 and confirmed in this work.