This is my Thesis repo.
Based on the
We are living the golden age of observational cosmology. There is a consolidated standard cosmological model ($\Lambda$CDM) that explains the observed Large Scale Structure (LSS) of galaxies by introducing dark matter and dark energy as the dominant Universe components along with baryonic matter. Furthermore, we are able to do precise observatioanl measurements of the cosmological parameters in that model. Most of this success is due to computational cosmology that is now an stablished tool to probe theoretical models and compare them with observations. The main features of the LSS can been reproduced in large cosmological N-body simulations.
One of the most striking features in the LSS are voids; irregular volumes on the order of tens of Mpc scales, where the matter density is below the Universe average density. Statistics about voids such as its volume, shape and orientation also encode cosmological information. For this reason there is a great interest in algorithms that find and characterize voids both in simulations and observations.
The objetive of this work is to develop a new void finder based
on the
Keywords: Large Scale Structure, cosmology, voids, computational astrophysics
Estamos viviendo en la era dorada de la cosmología observacional.
Existe un modelo estándar comológico (
Una de las características más prominentes en la LSS son los vacíos: volúmenes irregulares de escalas del orden de decenas de Mpc, donde la densidad de materia está por debajo de la densidad media en el Universo. El análisis estadístico de propiedades de los vacíos, como su volumen, forma y orientación también nos puede dar información cosmológica. Por esta razón existe un gran interés en algoritmos que encuentren y caractericen vacíos tanto en simulaciones como en observaciones.
El objetivo de este trabajo es desarrollar un nuevo buscador de vacíos basado en el
m'etodo
Palabras clave: estructura de gran escala, cosmología, vacíos, astrofísica computacional.