Predicting structures in the Zone of Avoidance

Jenny G. Sorce*, Matthew Colless, Renée C. Kraan-Korteweg, Stefan Gottlöber

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)


    The Zone of Avoidance (ZOA), whose emptiness is an artefact of our Galaxy dust, has been challenging observers as well as theorists for many years. Multiple attempts have been made on the observational side to map this region in order to better understand the local flows. On the theoretical side, however, this region is often simply statistically populated with structures but no real attempt has been made to confront theoretical and observed matter distributions. This paper takes a step forward using constrained realizations (CRs) of the local Universe shown to be perfect substitutes of local Universe-like simulations for smoothed high-density peak studies. Far from generating completely 'random' structures in the ZOA, the reconstruction technique arranges matter according to the surrounding environment of this region. More precisely, the mean distributions of structures in a series of constrained and random realizations (RRs) differ: while densities annihilate each other when averaging over 200 RRs, structures persist when summing 200 CRs. The probability distribution function of ZOA grid cells to be highly overdense is a Gaussian with a 15 per cent mean in the random case, while that of the constrained case exhibits large tails. This implies that areas with the largest probabilities host most likely a structure. Comparisons between these predictions and observations, like those of the Puppis 3 cluster, show a remarkable agreement and allow us to assert the presence of the, recently highlighted by observations, Vela supercluster at about 180 h-1 Mpc, right behind the thickest dust layers of our Galaxy.

    Original languageEnglish
    Pages (from-to)3087-3097
    Number of pages11
    JournalMonthly Notices of the Royal Astronomical Society
    Issue number3
    Publication statusPublished - Nov 2017


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