Dusty post-red giant branch (post-RGB) stars are low- and intermediate-mass stars where the RGB evolution was prematurely terminated by a poorly understood binary interaction. These binary stars are considered to be low-luminosity analogues of post-asymptotic giant branch (post-AGB) binary stars. In this study, we investigated the chemical

We present results of dust continuum and [C II]$\, 158\, {\rm \mu m}$ emission line observations of a remarkably UV luminous (M UV = -21.6) galaxy at z = 10.603: GN-z11. Using the Northern Extended Millimeter Array (NOEMA), observations have been carried out over multiple observing cycles. We achieved a high sensitivity resulting in a $\lambda _{

The hydrogen and water molecules respond very differently to the collisional-radiative processes taking place in planetary atmospheres. Naturally, the question arises whether H 2O-rich atmospheres are more (or less) resilient to long-term mass loss than H 2-dominated ones if they radiate away the incident stellar energy more (or less) efficiently

The distribution of moving groups in the solar neighbourhood has been used to constrain dynamical properties of the Milky Way for decades. The kinematic bimodality between the main mode (Hyades, Pleiades, Coma Berenices, and Sirius) and Hercules can be explained by two different bar models - via the outer Lindblad resonance of a bar with a high

Esta tesis doctoral está dedicada al estudio de la formación y evolución de galaxias en un contexto cosmológico, aprovechando la potencia de la computación de alto rendimiento con un énfasis particular en la computación acelerada por tarjetas gráficas (GPU). Para avanzar en el campo, la tesis se enfoca en ampliar las capacidades del código público

As surveys grow, the challenge is how to explore and interpret the increasing quantity of data. For this, removing the observational biases and reducing the dimensionality of the data are fundamental. A promising avenue to do this is a self-supervised deep learning algorithm called contrastive learning. Contrastive learning is especially effective