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Imaging Spectrometer (imaging + spectrometer)

Distribution by Scientific Domains

Physics and Astronomy	50%

Selected Abstracts

Upper limits on X-ray emission from two rotating radio transients

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 3 2009
D. L. Kaplan
ABSTRACT X-ray emission from the enigmatic rotating radio transients (RRATs) offers a vital clue to understanding these objects and how they relate to the greater neutron star population. An X-ray counterpart to RRAT J1819,1458 is known, and its properties are similar to those of other middle-aged (0.1 Myr) neutron stars. We have searched for X-ray emission with Chandra/Advanced CCD Imaging Spectrometer at the positions of two RRATs with arcsecond (or better) localization, J0847,4316 and J1846,0257. Despite deep searches (especially for RRAT J1846,0257) we did not detect any emission with 0.3,8 keV count-rate limits of 1 and 0.068 counts ks,1, respectively, at 3, confidence. Assuming thermal emission similar to that seen from RRAT J1819,1458 (a blackbody with radius ,20 km), we derive effective temperature limits of 77 and 91 eV for the nominal values of the distances and column densities to both sources, although both of those quantities are highly uncertain and correlated. If we instead fix the temperature of the emission (a blackbody with kT= 0.14 keV), we derive unabsorbed luminosity limits in the 0.3,8 keV range of 1 × 1032 and 3 × 1032 erg s,1. These limits are considerably below the luminosity of RRAT J1819,1458(4 × 1033 erg s,1), suggesting that RRATs J0847,4316 and J1846,0257 have cooled beyond the point of visibility (plausible given the differences in characteristic age). However, as we have not detected X-ray emission, it may also be that the emission from RRATs J0847,4316 and J1846,0257 has a different character from that of RRAT J1819,1458. The two non-detections may prove a counterpoint to RRAT J1819,1458, but more detections are certainly needed before we can begin to derive general X-ray emission properties for the RRAT populations. [source]

The effect of overlying absorbing aerosol layers on remote sensing retrievals of cloud effective radius and cloud optical depth

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 598 2004
Jim M. Haywood
Abstract Two types of partially absorbing aerosol are included in calculations that are based on intensive aircraft observations: biomass burning aerosol characterized during the Southern AFricAn Regional science Initiative (SAFARI 2000) and mineral dust aerosol characterized during the SaHAran Dust Experiment (SHADE). Measurements during SAFARI 2000 reveal that the biomass burning aerosol layer is advected over the South Atlantic ocean at elevated altitudes above the marine boundary layer which is capped by semi-permanent stratocumulus cloud sheets. Similarly, the mineral dust is measured at elevated altitudes during SHADE resulting in transport above cloud for distances of several thousands of kilometres. We perform theoretical calculations of the effect of these partially absorbing aerosol layers on satellite retrievals of cloud effective radius and cloud optical depth, and show that, in these cases, retrievals of cloud optical depth or liquid water path are likely to be subject to systematic low biases. The theoretical calculations suggest that the cloud effective radius may be subject to a significant low bias for Moderate resolution Imaging Spectrometer (MODIS) retrievals that rely on the 0.86 and 1.63 µm radiance pair for an overlying aerosol layer of either biomass burning aerosol or mineral dust. Conversely, the cloud effective radius may be subject to a significant high bias for Advanced Very High Resolution Radiometer or MODIS retrievals that rely on the 0.63 and 3.7 µm radiance pair for an overlying aerosol layer of mineral dust. Analysis of 1 km resolution MODIS data for the SAFARI 2000 period suggests that the effective radius derived from the 0.86 and 1.63 µm radiance pair is, indeed, subject to a low bias in the presence of overlying biomass burning aerosol. These results show the difficulties associated with remote sensing retrievals, which must be kept in mind when attempting to assess any potential indirect effect. © Crown copyright 2004. [source]

Review of Hinode results

ASTRONOMISCHE NACHRICHTEN, Issue 6 2010
Y. Suematsu
Abstract Hinode is an observatory-style satellite, carrying three advanced instruments being designed and built to work together to explore the physical coupling between the photosphere and the upper layers for understanding the mechanism of dynam- ics and heating. The three instruments aboard are the Solar Optical Telescope (SOT), which can provide high-precision photometric and polarimetric data of the lower atmosphere in the visible light (388,668 nm) with a spatial resolution of 0.2,0.3 arcseconds, the X-Ray Telescope (XRT) which takes a wide field of full sun coverage X-ray images being capable of diagnosing the physical condition of coronal plasmas, and the EUV Imaging Spectrometer (EIS) which observes the upper transition region and coronal emission lines in the wavelength ranges of 17,21 nm and 25,29 nm. Since first-light observations in the end of October 2006, Hinode has been continuously providing unprecedented high-quality solar data. We will present some new findings of the sun with Hinode, focusing on those from SOT (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Parallel processing of remotely sensed hyperspectral imagery: full-pixel versus mixed-pixel classification

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 13 2008
Antonio J. Plaza
Abstract The rapid development of space and computer technologies allows for the possibility to store huge amounts of remotely sensed image data, collected using airborne and satellite instruments. In particular, NASA is continuously gathering high-dimensional image data with Earth observing hyperspectral sensors such as the Jet Propulsion Laboratory's airborne visible,infrared imaging spectrometer (AVIRIS), which measures reflected radiation in hundreds of narrow spectral bands at different wavelength channels for the same area on the surface of the Earth. The development of fast techniques for transforming massive amounts of hyperspectral data into scientific understanding is critical for space-based Earth science and planetary exploration. Despite the growing interest in hyperspectral imaging research, only a few efforts have been devoted to the design of parallel implementations in the literature, and detailed comparisons of standardized parallel hyperspectral algorithms are currently unavailable. This paper compares several existing and new parallel processing techniques for pure and mixed-pixel classification in hyperspectral imagery. The distinction of pure versus mixed-pixel analysis is linked to the considered application domain, and results from the very rich spectral information available from hyperspectral instruments. In some cases, such information allows image analysts to overcome the constraints imposed by limited spatial resolution. In most cases, however, the spectral bands collected by hyperspectral instruments have high statistical correlation, and efficient parallel techniques are required to reduce the dimensionality of the data while retaining the spectral information that allows for the separation of the classes. In order to address this issue, this paper also develops a new parallel feature extraction algorithm that integrates the spatial and spectral information. The proposed technique is evaluated (from the viewpoint of both classification accuracy and parallel performance) and compared with other parallel techniques for dimensionality reduction and classification in the context of three representative application case studies: urban characterization, land-cover classification in agriculture, and mapping of geological features, using AVIRIS data sets with detailed ground-truth. Parallel performance is assessed using Thunderhead, a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. The detailed cross-validation of parallel algorithms conducted in this work may specifically help image analysts in selection of parallel algorithms for specific applications. Copyright © 2008 John Wiley & Sons, Ltd. [source]