517 research outputs found
SANEPIC: A Map-Making Method for Timestream Data From Large Arrays
We describe a map-making method which we have developed for the Balloon-borne
Large Aperture Submillimeter Telescope (BLAST) experiment, but which should
have general application to data from other submillimeter arrays. Our method
uses a Maximum Likelihood based approach, with several approximations, which
allows images to be constructed using large amounts of data with fairly modest
computer memory and processing requirements. This new approach, Signal And
Noise Estimation Procedure Including Correlations (SANEPIC), builds upon
several previous methods, but focuses specifically on the regime where there is
a large number of detectors sampling the same map of the sky, and explicitly
allowing for the the possibility of strong correlations between the detector
timestreams. We provide real and simulated examples of how well this method
performs compared with more simplistic map-makers based on filtering. We
discuss two separate implementations of SANEPIC: a brute-force approach, in
which the inverse pixel-pixel covariance matrix is computed; and an iterative
approach, which is much more efficient for large maps. SANEPIC has been
successfully used to produce maps using data from the 2005 BLAST flight.Comment: 27 Pages, 15 figures; Submitted to the Astrophysical Journal; related
results available at http://blastexperiment.info/ [the BLAST Webpage
An Scalable matrix computing unit architecture for FPGA and SCUMO user design interface
High dimensional matrix algebra is essential in numerous signal processing and machine learning algorithms. This work describes a scalable square matrix-computing unit designed on the basis of circulant matrices. It optimizes data flow for the computation of any sequence of matrix operations removing the need for data movement for intermediate results, together with the individual matrix operations' performance in direct or transposed form (the transpose matrix operation only requires a data addressing modification). The allowed matrix operations are: matrix-by-matrix addition, subtraction, dot product and multiplication, matrix-by-vector multiplication, and matrix by scalar multiplication. The proposed architecture is fully scalable with the maximum matrix dimension limited by the available resources. In addition, a design environment is also developed, permitting assistance, through a friendly interface, from the customization of the hardware computing unit to the generation of the final synthesizable IP core. For N x N matrices, the architecture requires N ALU-RAM blocks and performs O(N*N), requiring N*N +7 and N +7 clock cycles for matrix-matrix and matrix-vector operations, respectively. For the tested Virtex7 FPGA device, the computation for 500 x 500 matrices allows a maximum clock frequency of 346 MHz, achieving an overall performance of 173 GOPS. This architecture shows higher performance than other state-of-the-art matrix computing units
A fast Bayesian approach to discrete object detection in astronomical datasets - PowellSnakes I
A new fast Bayesian approach is introduced for the detection of discrete
objects immersed in a diffuse background. This new method, called PowellSnakes,
speeds up traditional Bayesian techniques by: i) replacing the standard form of
the likelihood for the parameters characterizing the discrete objects by an
alternative exact form that is much quicker to evaluate; ii) using a
simultaneous multiple minimization code based on Powell's direction set
algorithm to locate rapidly the local maxima in the posterior; and iii)
deciding whether each located posterior peak corresponds to a real object by
performing a Bayesian model selection using an approximate evidence value based
on a local Gaussian approximation to the peak. The construction of this
Gaussian approximation also provides the covariance matrix of the uncertainties
in the derived parameter values for the object in question. This new approach
provides a speed up in performance by a factor of `hundreds' as compared to
existing Bayesian source extraction methods that use MCMC to explore the
parameter space, such as that presented by Hobson & McLachlan. We illustrate
the capabilities of the method by applying to some simplified toy models.
Furthermore PowellSnakes has the advantage of consistently defining the
threshold for acceptance/rejection based on priors which cannot be said of the
frequentist methods. We present here the first implementation of this technique
(Version-I). Further improvements to this implementation are currently under
investigation and will be published shortly. The application of the method to
realistic simulated Planck observations will be presented in a forthcoming
publication.Comment: 30 pages, 15 figures, revised version with minor changes, accepted
for publication in MNRA
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