T-ReX: a graph-based filament detection method. (arXiv:1912.00732v2 [astro-ph.CO] UPDATED)

<a href="http://arxiv.org/find/astro-ph/1/au:+Bonnaire_T/0/1/0/all/0/1">T. Bonnaire</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Aghanim_N/0/1/0/all/0/1">N. Aghanim</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Decelle_A/0/1/0/all/0/1">A. Decelle</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Douspis_M/0/1/0/all/0/1">M. Douspis</a>

Numerical simulations and observations show that galaxies are not uniformly

distributed in the universe but, rather, they are spread across a filamentary

structure. In this large-scale pattern, highly dense regions are linked

together by bridges and walls, all of them surrounded by vast, nearly-empty

areas. While nodes of the network are widely studied in the literature,

simulations indicate that half of the mass budget comes from a more diffuse

part of the network, which is made up of filaments. In the context of recent

and upcoming large galaxy surveys, it becomes essential that we identify and

classify features of the Cosmic Web in an automatic way in order to study their

physical properties and the impact of the cosmic environment on galaxies and

their evolution.

In this work, we propose a new approach for the automatic retrieval of the

underlying filamentary structure from a 2D or 3D galaxy distribution using

graph theory and the assumption that paths that link galaxies together with the

minimum total length highlight the underlying distribution. To obtain a

smoothed version of this topological prior, we embedded it in a Gaussian

mixtures framework. In addition to a geometrical description of the pattern, a

bootstrap-like estimate of these regularised minimum spanning trees allowed us

to obtain a map characterising the frequency at which an area of the domain is

crossed. Using the distribution of halos derived from numerical simulations, we

show that the proposed method is able to recover the filamentary pattern in a

2D or 3D distribution of points with noise and outliers robustness with a few

comprehensible parameters.

Numerical simulations and observations show that galaxies are not uniformly

distributed in the universe but, rather, they are spread across a filamentary

structure. In this large-scale pattern, highly dense regions are linked

together by bridges and walls, all of them surrounded by vast, nearly-empty

areas. While nodes of the network are widely studied in the literature,

simulations indicate that half of the mass budget comes from a more diffuse

part of the network, which is made up of filaments. In the context of recent

and upcoming large galaxy surveys, it becomes essential that we identify and

classify features of the Cosmic Web in an automatic way in order to study their

physical properties and the impact of the cosmic environment on galaxies and

their evolution.

In this work, we propose a new approach for the automatic retrieval of the

underlying filamentary structure from a 2D or 3D galaxy distribution using

graph theory and the assumption that paths that link galaxies together with the

minimum total length highlight the underlying distribution. To obtain a

smoothed version of this topological prior, we embedded it in a Gaussian

mixtures framework. In addition to a geometrical description of the pattern, a

bootstrap-like estimate of these regularised minimum spanning trees allowed us

to obtain a map characterising the frequency at which an area of the domain is

crossed. Using the distribution of halos derived from numerical simulations, we

show that the proposed method is able to recover the filamentary pattern in a

2D or 3D distribution of points with noise and outliers robustness with a few

comprehensible parameters.

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