6,944 research outputs found

    A New Algorithm For Difference Image Analysis

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    In the context of difference image analysis (DIA), we present a new method for determining the convolution kernel matching a pair of images of the same field. Unlike the standard DIA technique which involves modelling the kernel as a linear combination of basis functions, we consider the kernel as a discrete pixel array and solve for the kernel pixel values directly using linear least-squares. The removal of basis functions from the kernel model is advantageous for a number of compelling reasons. Firstly, it removes the need for the user to specify such functions, which makes for a much simpler user application and avoids the risk of an inappropriate choice. Secondly, basis functions are constructed around the origin of the kernel coordinate system, which requires that the two images are perfectly aligned for an optimal result. The pixel kernel model is sufficiently flexible to correct for image misalignments, and in the case of a simple translation between images, image resampling becomes unnecessary. Our new algorithm can be extended to spatially varying kernels by solving for individual pixel kernels in a grid of image sub-regions and interpolating the solutions to obtain the kernel at any one pixel.Comment: MNRAS Letters Accepte

    Measurement of helium-3 and deuterium stopping power ratio for negative muons

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    The measurement method and results measuring of the stopping power ratio of helium-3 and deuterium atoms for muons slowed down in the D/3^3He mixture are presented. Measurements were performed at four values of pure 3^3He gas target densities, ϕHe=0.0337,0.0355,0.0359,0.0363\phi_{He} = 0.0337, 0.0355, 0.0359, 0.0363 (normalized to the liquid hydrogen density) and at a density 0.0585 of the D/3^3He mixture. The experiment was carried out at PSI muon beam μ\muE4 with the momentum Pμ=34.0\mu =34.0 MeV/c. The measured value of the mean stopping ratio S3He/DS_{^3He/D} is 1.66±0.041.66\pm 0.04. This value can also be interpreted as the value of mean reduced ratio of probabilities for muon capture by helium-3 and deuterium atoms.Comment: 7 pages, 6 figure

    Disc galaxies with multiple triaxial structures. II. JHK surface photometry and numerical simulations

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    We present detailed JHK surface photometry with ellipse fits of 13 galaxies selected from previous optical observations as likely candidates for having a secondary bar or a triaxial bulge within the primary bar. We have found 7 double-barred galaxies, 3 double-barred galaxies with an additional intermediate structure with twisted isophotes, and 3 galaxies with a bar and central twisted isophotes. A global analysis of the structural parameter characteristics in the I- and K-bands is presented. Various numerical models of galaxies with bars within bars are also analysed using the ellipse fitting technique and compared to the observations. A thorough review of the possible hypotheses able to explain this phenomenon is given with emphasis on the most likely ones.Comment: 12 pages, AATEX. Accepted for publication in A&A. Large color postscript figures omitted (Figs. 1), figures 2-9 included; gzip'ed postscript files of the paper and Figs. 1 available via anonymous ftp at ftp://obsftp.unige.ch/pub/fri/aasjhk/ , files fri_aasjhk.ps.gz and ngc*.ps.g

    Estimating Photometric Redshifts Using Support Vector Machines

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    We present a new approach to obtaining photometric redshifts using a kernel learning technique called Support Vector Machines (SVMs). Unlike traditional spectral energy distribution fitting, this technique requires a large and representative training set. When one is available, however, it is likely to produce results that are comparable to the best obtained using template fitting and artificial neural networks. Additional photometric parameters such as morphology, size and surface brightness can be easily incorporated. The technique is demonstrated using samples of galaxies from the Sloan Digital Sky Survey Data Release 2 and the hybrid galaxy formation code GalICS. The RMS error in redshift estimation is <0.03<0.03 for both samples. The strengths and limitations of the technique are assessed.Comment: 10 pages, 3 figures, to appear in the PASP, minor typos fixed to make consistent with published versio
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