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In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r -band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys. [ABSTRACT FROM AUTHOR]

During the last years, a number of telescopes have been dedicated to the followup of the GRBs. But after the Swift launch, the average observed intensity of the GRBs showed to be lower than thought before. Our experience with the robotic 60 cm REM telescope confirmed this evidence, with a large number of lost GRBs. Then, we proposed to study the feasibility of a 4 m fast pointing class telescope, equipped with a multichannel imagers, from Visible to Near Infrared. In this paper, we present the main result of the feasibility study we performed so far. [ABSTRACT FROM AUTHOR]