![]() We evaluate more » the new algorithm using two late-stage cosmology datasets. Because resulting point distributions no longer satisfy the assumptions of existing parallel Delaunay algorithms, we develop a new parallel algorithm that adapts to its input and prove its correctness. We investigate the use of k-d trees to evenly distribute points among processes and compare two strategies for picking split points between domain regions. The algorithms for computing these tessellations at scale perform poorly when the input data is unbalanced. ![]() They are important in data analysis, where they can represent the geometry of a point set or approximate its density. « lessĭelaunay tessellations are fundamental data structures in computational geometry. We illustrate our method with several toy examples of both straight and curved boundaries with varying amounts of signal present in the data. Here we propose an alternative approach where we simultaneously form and evaluate the significance of all possible boundaries in terms of the total gradient flux. The outcome from traditional wombling algorithms is a set of boundary cell candidates with relatively large gradients, whose spatial properties must then more » be scrutinized in order to construct the boundary and evaluate its significance. ![]() We discuss the use of Voronoi and Delaunay tessellations of the point data for estimating the local gradients and investigate methods for sharpening the boundaries by reducing the statistical noise. We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in the phase space of a final state signature, with the wombling boundary created by some new physics.
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