Research

Various studies on the properties of the Large Scale Structure of the Universe.

Jun
2017

Sub-megaparsec Redshift Estimates from Cosmic Web Constraints

Posted by Miguel Aragon

This is one of those ideas that seem "to good to be true". Conventional photo-z techniques can be used to derive distances to galaxies but the uncertainties are or the order of tens of megaparsecs (see let panel of the figure above). By using the cosmic web as an additional constraint over existing photo-z estimates we can derive redshifts with an equivalen accuracy of a couple of megaparsecs and even sub-megaparsecs! This paper explains why this makes sense and describes the basic method.

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Feb
2013

The Multum in Parvo (MIP) simulation.

Posted by Miguel Aragon

The MIP simulation is a special kind of ensemble simulation in which all realizations share the same large-scale structure features. A simple way of describing this is as a "parallel universe simulation". The key of the MIP is generating initial conditions that are correlated above a given scale, this ensures that each simulation contains the same cosmic web structures while containing a different halo/galaxy population. The MIP can be used to study halo properties in regions where conventional simulations do not offer enough haloes to compute statistics such as the interior or voids and walls.

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Feb
2007

The spine of the cosmic web.

Posted by Miguel Aragon

The Spine is a topological method to classify the cosmic web into its basic morphological components. It is based on a very neat property of the cosmic web: local geometry can be derived from topology. The spine method computes the critical points in the density field and relates their connectivity to a specific geometry. It can find voids (via the watershed method) and from that walls and filaments.

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Feb
2007

Hierarchical Multiscale Morphology Filter.

Posted by Miguel Aragon

The idea of scale spaces can be generalized (in cosmology at least) to hierarchical spaces where scale of interest is defined at the initial conditions (i.e. linear regime) and then let evolve to the non-linear regime. This basically means that by smoothing the initial conditions we can generate a much cleaner scale space, which we call hierarchical space. This has the advantage of producing a scale space with well defined features similar to anisotropic filtering but in this case made by gravity.

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