Surprisingly, our approach is analogous to the analysis of games of chance where the gambler only plays if a positive expected net gain can be achieved based on some degree of privileged information. With Jens Jasche and Benjamin Wandelt, I proposed to use Bayesian decision theory for segmenting the cosmic web into different structure types, on the basis of their (previously inferred) probabilities ( Leclercq et al. Cosmic web analysis using decision theory and information theory In purple, two nearby voids in the directions of Ursa-Major and Coma-Berenices, as well as the BoГ¶tes void. The Virgo-BoГ¶tes-Hercules (VBH) filament is also labeled. Some structures of the Local Universe are identified in orange: the Coma cluster, the Ursa-Majoris supercluster, and two farther clusters in the direction of Ursa-Major and Ursa-Major/Coma-Berenices. Slice through supergalactic plane, showing the dark matter density overplotted with galaxies. 2017), which was the object of a press release. Using it as well a set of phase-space techniques resulted in the first phase-space analysis of the nearby Universe as probed by the SDSS ( Leclercq et al. Within the same project, we also introduced a new cosmic web classifier, LICH, to identify structures while taking into account the existence of both potential and vortical flows. From left to right: voids, sheets, filaments, and clusters. Mean of the posterior probability distribution function for the four different web-types in the large-scale structure in the Sloan volume. (2007), we used the tidal field to classify the large-scale structure into four distinct web-types (voids, sheets, filaments, and clusters) and quantify corresponding uncertainties ( Leclercq et al. Following the dynamic cosmic web classification procedure proposed by Hahn et al.

More generally, BORG infers a wealth of cosmological information that can be used to classify the large-scale structure. The SDSS galaxies are represented as red dots and the core of dark matter voids in colour (purple to blue). Slices through data-constrained realisations of the Universe used to build samples of the dark matter void catalogue. The catalogues have been made publicly available here and on. In 2014, we produced and analysed constrained catalogues of dark matter voids in the Sloan volume, probing a deeper void hierarchy and than the distribution of galaxies and alleviating the issues due to sparsity and bias ( Leclercq et al. Dark matter voidsДЄn interesting aspect is voids in the dark matter distribution. A natural application of Bayesian large-scale structure inference with BORG is a high-fidelity description of the complex web-like patterns in cosmic structure.