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Creators/Authors contains: "Toth, C"

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  1. ; ; (, 31st International Symposium on Algorithms and Computation (ISAAC 2020))
    Full Text Available 
  2. ; ; ; (, 28th International Symposium on Graph Drawing and Network Visualization (GD))
    We consider the construction of a polygon P with n vertices whose turning angles at the vertices are given by a sequence A = (α0 , . . . , αn−1 ), αi ∈ (−π,π), for i ∈ {0,...,n − 1}. The problem of realizing A by a polygon can be seen as that of constructing a straight-line drawing of a graph with prescribed angles at vertices, and hence, it is a special case of the well studied problem of constructing an angle graph. In 2D, we characterize sequences A for which every generic polygon P ⊂ R2 realizing A has at least c crossings, and describe an efficient algorithm that constructs, for a given sequence A, a generic polygon P ⊂ R2 that realizes A with the minimum number of crossings. In 3D, we describe an efficient algorithm that tests whether a given sequence A can be realized by a (not necessarily generic) polygon P ⊂ R3, and for every realizable sequence finds a realization. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; ; ; et al (, Journal of Cosmology and Astroparticle Physics)
    Abstract We demonstrate a new type of analysis for the DRIFT-IId directional dark matter detector using a machine learning algorithm called a Random Forest Classifier. The analysis labels events as signal or background based on a series of selection parameters, rather than solely applying hard cuts. The analysis efficiency is shown to be comparable to our previous result at high energy but with increased efficiency at lower energies. This leads to a projected sensitivity enhancement of one order of magnitude below a WIMP mass of 15 GeV c -2 and a projected sensitivity limit that reaches down to a WIMP mass of 9 GeV c -2 , which is a first for a directionally sensitive dark matter detector. 
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    Full Text Available