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
UNCOVERING THE PHYSICS OF GALAXIES WITH SELF-SUPERVISED DEEP LEARNING
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
THE QUIJOTE EXPERIMENT: COMPONENT SEPARATION ANALYSES WITH MFI DATA AND TFGI COMMISSIONING RESULTS
This thesis is dedicated to the study of the data from the first two instruments of the Q-U-I JOint Tenerife Experiment (QUIJOTE) experiment: the Multi Frequency Instrument (MFI) and the Thirty-and-Forty Gigahertz Instrument (TFGI). The MFI was installed at the Observatorio del Teide (OT) from 2012 to 2018, observing the sky between 10-20 GHz at