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Register a new userA Full-sky, High-resolution Atlas of Galactic 12 micron Dust Emission with WISE
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We describe our custom processing of the entire Wide-field Infrared Survey Explorer (WISE) 12 micron imaging data set, and present a high-resolution, full-sky map of diffuse Galactic dust emission that is free of compact sources and other contaminating artifacts. The principal distinctions between our resulting co-added images and the WISE Atlas stacks are our removal of compact sources, including their associated electronic and optical artifacts, and our preservation of spatial modes larger than 1.5 degrees. We provide access to the resulting full-sky map via a set of 430 12.5 degree by 12.5 degree mosaics. These stacks have been smoothed to 15 resolution and are accompanied by corresponding coverage maps, artifact images, and bit-masks for point sources, resolved compact sources, and other defects. When combined appropriately with other mid-infrared and far-infrared data sets, we expect our WISE 12 micron co-adds to form the basis for a full-sky dust extinction map with angular resolution several times better than Schlegel et al. (1998).
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After eight months of continuous observations, the Wide-field Infrared Survey Explorer (WISE) mapped the entire sky at 3.4 {mu}m, 4.6 {mu}m, 12 {mu}m and 22 {mu}m. We have begun a dedicated WISE High Resolution Galaxy Atlas (WHRGA) project to fully characterize large, nearby galaxies and produce a legacy image atlas and source catalogue. Here we summarize the deconvolution technique used to significantly improve the spatial resolution of WISE imaging, specifically designed to study the internal anatomy of nearby galaxies. As a case study, we present results for the galaxy NGC 1566, comparing the WISE super-resolution image processing to that of Spitzer, GALEX and ground-based imaging. The is the first paper in a two part series; results for a much larger sample of nearby galaxies is presented in the second paper.
Technology has advanced to the point that it is possible to image the entire sky every night and process the data in real time. The sky is hardly static: many interesting phenomena occur, including variable stationary objects such as stars or QSOs, transient stationary objects such as supernovae or M dwarf flares, and moving objects such as asteroids and the stars themselves. Funded by NASA, we have designed and built a sky survey system for the purpose of finding dangerous near-Earth asteroids (NEAs). This system, the Asteroid Terrestrial-impact Last Alert System (ATLAS), has been optimized to produce the best survey capability per unit cost, and therefore is an efficient and competitive system for finding potentially hazardous asteroids (PHAs) but also for tracking variables and finding transients. While carrying out its NASA mission, ATLAS now discovers more bright ($m < 19$) supernovae candidates than any ground based survey, frequently detecting very young explosions due to its 2 day cadence. ATLAS discovered the afterglow of a gamma-ray burst independent of the high energy trigger and has released a variable star catalogue of 5$times10^{6}$ sources. This, the first of a series of articles describing ATLAS, is devoted to the design and performance of the ATLAS system. Subsequent articles will describe in more detail the software, the survey strategy, ATLAS-derived NEA population statistics, transient detections, and the first data release of variable stars and transient lightcurves.
The Infrared Spectrograph (IRS) on board the Spitzer Space Telescope observed about 15,000 objects during the cryogenic mission lifetime. Observations provided low-resolution (R~60-127) spectra over ~5-38um and high-resolution (R~600) spectra over ~10-37um. The Cornell Atlas of Spitzer/IRS Sources (CASSIS) was created to provide publishable quality spectra to the community. Low-resolution spectra have been available in CASSIS since 2011, and we present here the addition of the high-resolution spectra. The high-resolution observations represent approximately one third of all staring observations performed with the IRS instrument. While low-resolution observations are adapted to faint objects and/or broad spectral features (e.g., dust continuum, molecular bands), high-resolution observations allow more accurate measurements of narrow features (e.g., ionic emission lines) as well as a better sampling of the spectral profile of various features. Given the narrow aperture of the two high-resolution modules, cosmic ray hits and spurious features usually plague the spectra. Our pipeline is designed to minimize these effects through various improvements. A super sampled point-spread function was created in order to enable the optimal extraction in addition to the full aperture extraction. The pipeline selects the best extraction method based on the spatial extent of the object. For unresolved sources, the optimal extraction provides a significant improvement in signal-to-noise ratio over a full aperture extraction. We have developed several techniques for optimal extraction, including a differential method that eliminates low-level rogue pixels (even when no dedicated background observation was performed). The updated CASSIS repository now includes all the spectra ever taken by the IRS, with the exception of mapping observations.
We present a high-resolution (R ~ 50 000) atlas of a uranium-neon (U/Ne) hollow-cathode spectrum in the H-band (1454 nm to 1638 nm) for the calibration of near-infrared spectrographs. We obtained this U/Ne spectrum simultaneously with a laser-frequency comb spectrum, which we used to provide a first-order calibration to the U/Ne spectrum. We then calibrated the U/Ne spectrum using the recently-published uranium line list of Redman et al. (2011), which is derived from high-resolution Fourier transform spectrometer measurements. These two independent calibrations allowed us to easily identify emission lines in the hollow cathode lamp that do not correspond to known (classified) lines of either uranium or neon, and to compare the achievable precision of each source. Our frequency comb precision was limited by modal noise and detector effects, while the U/Ne precision was limited primarily by the signal-to-noise ratio (S/N) of the observed emission lines and our ability to model blended lines. The standard deviation in the dispersion solution residuals from the S/N-limited U/Ne hollow cathode lamp were 50% larger than the standard deviation of the dispersion solution residuals from the modal-noise-limited laser frequency comb. We advocate the use of U/Ne lamps for precision calibration of near-infrared spectrographs, and this H-band atlas makes these lamps significantly easier to use for wavelength calibration.
Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning algorithms, a description of the statistical properties of such emission can be helpful. Here we examine a machine learning approach to inferring the statistical properties of dust from either observational data or physics-based simulations. In particular, we apply a type of neural network called a Variational Auto Encoder (VAE) to maps of the intensity of emission from interstellar dust as inferred from Planck sky maps and demonstrate its ability to a) simulate new samples with similar summary statistics as the training set, b) provide fits to emission maps withheld from the training set, and c) produce constrained realizations. We find VAEs are easier to train than another popular architecture: that of Generative Adversarial Networks (GANs), and are better-suited for use in Bayesian inference.
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