The candidate needs to have experience with computer science and be capable of analyze many Tb of astronomical images to extract the sources fluxes, shape and position and transform it to astrometry and photometry of the sources, using an existing computing cluster. All the work must be done in automatic way and using parallel computing. One need to think that images are data arrays that can be processed and extract the information. The main idea of this proposal is to extract the information from images and from different observatories and refer them to the same standard, for example the Sloan filters. The plan is to extract from the images, the position, shape and fluxes of all the sources, stars and minor bodies, and to make a registration on variability of the sources. Also, the candidate will combine this information with the one present in different databases of ground-based and space telescopes like SDSS-MOC, Wise, K2 or Tess. The thesis will consist on the implementation of an automatic way to analyze many types of images, from different telescopes, and extract the information on minor bodies. On the other hand, combine this information with the one present on existing database and make the physical interpretation, as individual objects or as populations. The first step will be the extraction of the sources from our own images, the identification of the known objects, register the coordinates and fluxes and report them and check for any unknown object. Once checked with the previous information we have on the objects for validation, the next step will be the automatization of the procedure and the extension to all the database. The goal will be to analyze any image, extract the corresponding information (spatial and intensity of the source over time) and report them. It will be valued the knowledge in: python (design of graphical user interfaces), source detection on images, machine learning, data science, non-sql databases, Scala language and Spark-Hadoop.

Period (months): 36 months