RT info:eu-repo/semantics/masterThesis
T1 Millilensing statistics for BH clusters and Gaussian subhaloes
A1 Rodríguez Vega, Guillermo
A2 Máster Universitario en Astrofísica
AB The aim of this TFM is to probe candidates to Dark Matter through their effect on the probability density function of the magnification induced by gravitational lensing, P DF(µ). This is doneby simulating the millilensing effect using the inverse ray tracing algorithm in both Python3 andFORTRAN95. Furthermore, we have implemented the nearest neighbours algorithm in the codesand performed a comparison of the computational efficiency between Python3 and FORTRAN95scripts. To cross-check our numerical models we compare with the theoretical predictions for thesparse case. Our first candidates to dark matter are primordial black holes, which become aninteresting possibility after LIGO-VIRGO observations. In this TFM we are considering themgrouped in clusters of variable compactness, instead of following a random uniform distribution.Furthermore, we study whether at large scales a compact cluster behaves as a pseudo-particle beingindistinguishable from a single black hole with the same mass of the cluster. Our second candidatesunder study are DM subhaloes. The existence of these subhaloes is predicted by CDM models ofstructure formation. We analyze whether the gravitational lensing magnification induced by DMGaussian subhaloes with different compactness is consistent with real observations. A Bayesiananalysis based on compactness and magnification shows that, according to observations, clustering makes less probable the existence of PBH’s and that a large compactness of the subhaloes shouldresult in millilensing magnification larger than observed.
YR 2024
FD 2024
LK http://riull.ull.es/xmlui/handle/915/39309
UL http://riull.ull.es/xmlui/handle/915/39309
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 23-dic-2024