The miniJPAS survey: star-galaxy classification using machine learning
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- Created: Thursday, 18 February 2021 10:44
Future astrophysical surveys such as J-PAS will produce very large datasets, which will require the deployment of accurate and efficient Machine Learning (ML) methods. In this work, we analyze the miniJPAS survey, which observed about 1 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses.
Il giorno 1 febbraio 2021, è stato firmato 
Nowadays, a number of observational evidence consistently support the Standard Model of Cosmology: about 70% of the content of the Universe is provided by an elusive form of Dark Energy, about 25% by Dark Matter whose nature is still unknown and only 5% by “normal” atoms. Despite the nature of these dark components is unknown at present, this cosmological model allows us to make detailed predictions on the physical properties of the cosmic structures that we observe. The most sophisticated tools to work out such predictions are represented by numerical simulations which make optimal use of high-performance computing infrastructures.