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001     840299
005     20210129231811.0
024 7 _ |a 2128/15995
|2 Handle
037 _ _ |a FZJ-2017-07845
041 _ _ |a English
100 1 _ |a Ghanem, Khaldoon
|0 P:(DE-Juel1)168540
|b 0
|e Corresponding author
245 _ _ |a Stochastic Analytic Continuation: A Bayesian Approach
|f - 2017-06-26
260 _ _ |c 2017
300 _ _ |a 183 p.
336 7 _ |a Output Types/Dissertation
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336 7 _ |a DISSERTATION
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336 7 _ |a PHDTHESIS
|2 BibTeX
336 7 _ |a Thesis
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336 7 _ |a Dissertation / PhD Thesis
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|s 1511879854_21601
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336 7 _ |a doctoralThesis
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502 _ _ |a Dissertation, RWTH Aachen University, 2017
|c RWTH Aachen University
|b Dissertation
|d 2017
|o 2017-06-26
520 _ _ |a The stochastic sampling method (StochS) is used for the analytic continuation of quantum Monte Carlo data from the imaginary axis to the real axis. Compared to the maximum entropy method, StochS does not have explicit parameters, and one would expect the results to be unbiased. We present a very efficient algorithm for performing StochS and use it to study the effect of the discretization grid. Surprisingly, we find that the grid affects the results of StochS acting as an implicit default model. We provide a recipe for choosing a reliable StochS grid.To reduce the effect of the grid, we extend StochS into a gridless method (gStochS) by sampling the grid points from a default model instead of having them fixed. The effect of the default model is much reduced in gStochS compared to StochS and depends mainly on its width rather than its shape. The proper width can then be chosen using a simple recipe like we did in StochS.Finally, to avoid fixing the width, we go one step further and extend gStochS to sample over a whole class of default models with different widths. The extended method (eStochS) is then able to automatically relocate the grid points and concentrate them in the important region. Test cases show that eStochS gives good results resolving sharp features in the spectrum without the need for fine tuning a default model.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
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536 _ _ |0 G:(DE-Juel1)AICES-AACHEN-20170406
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|c AICES-AACHEN-20170406
|a AICES Aachen Institute for Advanced Study in Computational Engineering Science (AICES-AACHEN-20170406)
856 4 _ |u http://dx.doi.org/10.18154/RWTH-2017-06704
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910 1 _ |a Forschungszentrum Jülich
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|v Computational Science and Mathematical Methods
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914 1 _ |y 2017
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