使用三维稀疏表示的源检测：Fermi gamma-ray space telescope的应用
Source detection using a 3D sparse representation: application to the Fermi gamma-ray space telescope
摘要：
多尺度方差稳定变换（MSVST）最近被提出用于泊松数据去噪。这个过程，这是非参数的，是基于阈值小波系数。我们在本文中提出了一个扩展的MSVST到3D数据（实际上是2D-1D数据）时，第三个维度是不是一个空间维度，但波长，能量，或时间。我们表明，MSVST可用于检测和表征高能伽马射线的天体物理源，使用逼真的模拟观测与大面积望远镜（LAT）。LAT于2008年6月发射，用于费米伽马射线太空望远镜任务。相对于传统的似然模型拟合，MSVST算法是非常快的，并且允许跨时间维度的有效检测和光谱特性的即时估计。伽马射线的天体物理源，特别是活跃的星系，通常是相当可变的，我们目前的工作可能会导致一个可靠的方法来快速表征新探测到的源的耀斑特性。
英文摘要：
The multiscale variance stabilization Transform (MSVST) has recently been proposed for Poisson data denoising. This procedure, which is nonparametric, is based on thresholding wavelet coefficients. We present in this paper an extension of the MSVST to 3D data (in fact 2D-1D data) when the third dimension is not a spatial dimension, but the wavelength, the energy, or the time. We show that the MSVST can be used for detecting and characterizing astrophysical sources of high-energy gamma rays, using realistic simulated observations with the Large Area Telescope (LAT). The LAT was launched in June 2008 on the Fermi Gamma-ray Space Telescope mission. The MSVST algorithm is very fast relative to traditional likelihood model fitting, and permits efficient detection across the time dimension and immediate estimation of spectral properties. Astrophysical sources of gamma rays, especially active galaxies, are typically quite variable, and our current work may lead to a reliable method to quickly characterize the flaring properties of newly-detected sources.
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