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David H Brooks - One of the best experts on this subject based on the ideXlab platform.

  • evidence for steady heating observations of an active region core with hinode and trace
    The Astrophysical Journal, 2010
    Co-Authors: Harry P Warren, Amy R Winebarger, David H Brooks
    Abstract:

    The timescale for energy release is an important parameter for constraining the coronal heating mechanism. Observations of 'warm' coronal loops ({approx}1 MK) have indicated that the heating is impulsive and that coronal plasma is far from equilibrium. In contrast, observations at higher temperatures ({approx}3 MK) have generally been consistent with steady heating models. Previous observations, however, have not been able to exclude the possibility that the high temperature loops are actually composed of many small-scale threads that are in various stages of heating and cooling and only appear to be in equilibrium. With new observations from the EUV Imaging Spectrometer and X-ray Telescope (XRT) on Hinode we have the ability to investigate the properties of high temperature coronal plasma in extraordinary detail. We examine the emission in the core of an active region and find three independent lines of evidence for steady heating. We find that the emission observed in XRT is generally steady for hours, with a fluctuation level of approximately 15% in an Individual Pixel. Short-lived impulsive heating events are observed, but they appear to be unrelated to the steady emission that dominates the active region. Furthermore, we find no evidence for warm emission that is spatially correlatedmore » with the hot emission, as would be expected if the high temperature loops are the result of impulsive heating. Finally, we also find that intensities in the 'moss', the footpoints of high temperature loops, are consistent with steady heating models provided that we account for the local expansion of the loop from the base of the transition region to the corona. In combination, these results provide strong evidence that the heating in the core of an active region is effectively steady, that is, the time between heating events is short relative to the relevant radiative and conductive cooling times.« less

  • evidence for steady heating observations of an active region core with hinode and trace
    arXiv: Solar and Stellar Astrophysics, 2009
    Co-Authors: Harry P Warren, Amy R Winebarger, David H Brooks
    Abstract:

    Previous observations have not been able to exclude the possibility that high temperature active region loops are actually composed of many small scale threads that are in various stages of heating and cooling and only appear to be in equilibrium. With new observations from the EUV Imaging Spectrometer (EIS) and X-ray Telescope (XRT) on \textit{Hinode} we have the ability to investigate the properties of high temperature coronal plasma in extraordinary detail. We examine the emission in the core of an active region and find three independent lines of evidence for steady heating. We find that the emission observed in XRT is generally steady for hours, with a fluctuation level of approximately 15% in an Individual Pixel. Short-lived impulsive heating events are observed, but they appear to be unrelated to the steady emission that dominates the active region. Furthermore, we find no evidence for warm emission that is spatially correlated with the hot emission, as would be expected if the high temperature loops are the result of impulsive heating. Finally, we also find that intensities in the "moss", the footpoints of high temperature loops, are consistent with steady heating models provided that we account for the local expansion of the loop from the base of the transition region to the corona. In combination, these results provide strong evidence that the heating in the core of an active region is effectively steady, that is, the time between heating events is short relative to the relevant radiative and conductive cooling times.

F V N Rangarajan - One of the best experts on this subject based on the ideXlab platform.

  • asmooth a simple and efficient algorithm for adaptive kernel smoothing of two dimensional imaging data
    Monthly Notices of the Royal Astronomical Society, 2006
    Co-Authors: H Ebeling, D A White, F V N Rangarajan
    Abstract:

    An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language (idl). The functional form of the kernel can be varied (top-hat, Gaussian, etc.) to allow different weighting of the event counts registered within the smoothing region. For each Individual Pixel, the algorithm increases the smoothing scale until the signal-to-noise ratio (S/N) within the kernel reaches a pre-set value. Thus, noise is suppressed very efficiently, while at the same time real structure, that is, signal that is locally significant at the selected S/N level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The asmooth algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all Pixel values share the same S/N above the background. We apply asmooth to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the asmoothed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, that is, they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.

  • asmooth a simple and efficient algorithm for adaptive kernel smoothing of two dimensional imaging data
    arXiv: Astrophysics, 2006
    Co-Authors: H Ebeling, D A White, F V N Rangarajan
    Abstract:

    An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language (IDL). The functional form of the kernel can be varied (top-hat, Gaussian etc.) to allow different weighting of the event counts registered within the smoothing region. For each Individual Pixel the algorithm increases the smoothing scale until the signal-to-noise ratio (s.n.r.) within the kernel reaches a preset value. Thus, noise is suppressed very efficiently, while at the same time real structure, i.e. signal that is locally significant at the selected s.n.r. level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The ASMOOTH algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all Pixel values share the same signal-to-noise ratio above the background. We apply ASMOOTH to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the ASMOOTHed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, i.e. they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.

Norman S. Kopeika - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive suboptimum detection of an optical pulse-position-modulation signal with a detection matrix and centroid tracking
    Journal of the Optical Society of America A, 1998
    Co-Authors: Shlomi Arnon, Norman S. Kopeika
    Abstract:

    In some applications of optical communication systems, such as satellite optical communication and atmospheric optical communication, the optical beam wanders on the detector surface as a result of vibration and turbulence effects, respectively. The wandering of the beam degrades the communication system performance. In this research, we derive a mathematical model of an optical communication system with a detection matrix to improve the system performance for direct-detection pulse-position modulation. We include a centroid tracker in the communication system model. The centroid tracker tracks the center of the beam. Using the position of the beam center and an a priori model of the beam spreading, we estimate the optical power on each Pixel (element) in the detection matrix. Using knowledge of the amplitudes of signal and noise in each Pixel, we tune adaptively and separately the gain of each Individual Pixel in the detection matrix for communication signals. Tuning the gain is based on the mathematical model derived in this research. This model is defined as suboptimal, owing to some approximations in the development and is a suboptimum solution to the optimization problem of n multiplied by m free variables, where n, m are the dimensions of the detection matrix. Comparison is made between the adaptive suboptimum model and the standard model. From the mathematical analysis and the results of the comparison it is clear that this model significantly improves communication system performance.

  • Adaptive suboptimum detection of optical PPM signal with detection matrix and centroid tracking
    Photonics for Space Environments V, 1997
    Co-Authors: Arnon Shlomi, Norman S. Kopeika
    Abstract:

    In some applications of optical communication systems, such as satellite optical communication and atmospheric optical communication, the optical beam wanders on the detector surface due to vibration and turbulence effects, respectively. The wandering of the beam degrades the communication system performance. In this research, we derive a mathematical model of an optical communication system with a detection matrix to improve the system performance for direct detection pulse position modulation (PPM) We include a centroid tracker in the communication system model. The centroid tracker tracks the center of the beam. Using the position of beam center and an apriori model of beam spreading we estimate the optical power on each Pixel (element) in the detection matrix. Based on knowledge of the amplitudes of signal and noise in each Pixel, we tune adaptively and separately the gain of each Individual Pixel in the detection matrix for communication signals. Tuning the gain is based on the mathematical model derived in this research. This model is defined as suboptimal due to some approximations in the development and is a suboptimum solution to the optimization problem of n multiplied by m free variables, where U,mare the dimensions of the detection matrix. Comparison is made between the adaptive suboptimum model and the standard model. From the mathematical analysis and the results of the comparison it is clear that this model improves significantly communication system performance.

H Ebeling - One of the best experts on this subject based on the ideXlab platform.

  • asmooth a simple and efficient algorithm for adaptive kernel smoothing of two dimensional imaging data
    Monthly Notices of the Royal Astronomical Society, 2006
    Co-Authors: H Ebeling, D A White, F V N Rangarajan
    Abstract:

    An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language (idl). The functional form of the kernel can be varied (top-hat, Gaussian, etc.) to allow different weighting of the event counts registered within the smoothing region. For each Individual Pixel, the algorithm increases the smoothing scale until the signal-to-noise ratio (S/N) within the kernel reaches a pre-set value. Thus, noise is suppressed very efficiently, while at the same time real structure, that is, signal that is locally significant at the selected S/N level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The asmooth algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all Pixel values share the same S/N above the background. We apply asmooth to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the asmoothed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, that is, they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.

  • asmooth a simple and efficient algorithm for adaptive kernel smoothing of two dimensional imaging data
    arXiv: Astrophysics, 2006
    Co-Authors: H Ebeling, D A White, F V N Rangarajan
    Abstract:

    An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language (IDL). The functional form of the kernel can be varied (top-hat, Gaussian etc.) to allow different weighting of the event counts registered within the smoothing region. For each Individual Pixel the algorithm increases the smoothing scale until the signal-to-noise ratio (s.n.r.) within the kernel reaches a preset value. Thus, noise is suppressed very efficiently, while at the same time real structure, i.e. signal that is locally significant at the selected s.n.r. level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The ASMOOTH algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all Pixel values share the same signal-to-noise ratio above the background. We apply ASMOOTH to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the ASMOOTHed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, i.e. they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.

D A White - One of the best experts on this subject based on the ideXlab platform.

  • asmooth a simple and efficient algorithm for adaptive kernel smoothing of two dimensional imaging data
    Monthly Notices of the Royal Astronomical Society, 2006
    Co-Authors: H Ebeling, D A White, F V N Rangarajan
    Abstract:

    An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language (idl). The functional form of the kernel can be varied (top-hat, Gaussian, etc.) to allow different weighting of the event counts registered within the smoothing region. For each Individual Pixel, the algorithm increases the smoothing scale until the signal-to-noise ratio (S/N) within the kernel reaches a pre-set value. Thus, noise is suppressed very efficiently, while at the same time real structure, that is, signal that is locally significant at the selected S/N level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The asmooth algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all Pixel values share the same S/N above the background. We apply asmooth to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the asmoothed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, that is, they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.

  • asmooth a simple and efficient algorithm for adaptive kernel smoothing of two dimensional imaging data
    arXiv: Astrophysics, 2006
    Co-Authors: H Ebeling, D A White, F V N Rangarajan
    Abstract:

    An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language (IDL). The functional form of the kernel can be varied (top-hat, Gaussian etc.) to allow different weighting of the event counts registered within the smoothing region. For each Individual Pixel the algorithm increases the smoothing scale until the signal-to-noise ratio (s.n.r.) within the kernel reaches a preset value. Thus, noise is suppressed very efficiently, while at the same time real structure, i.e. signal that is locally significant at the selected s.n.r. level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The ASMOOTH algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all Pixel values share the same signal-to-noise ratio above the background. We apply ASMOOTH to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the ASMOOTHed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, i.e. they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.