INPhINIT Incoming PhD: "Computer Science and physical properties of minor bodies"

Deadline: 
February, 4th 2021
Type: 
INPhINIT PhD
Code: 
SO_IN_13
Pilar: 
(1) Towards the understanding of the planetary systems
Introduction: 

Until the mid-2000's, the study of the physical properties of the minor bodies in our solar system was done using ground-based and space observations. In the last 20 years, large observational surveys, with a variety of targets (from observational cosmology to stellar astrophysics) have produced a plethora of data. Among these are observations of minor bodies, usually deemed as contamination to the main survey objective, but that are, nonetheless, very appreciated by the community. As a by-product of the processing pipelines, sometimes, the minor bodies are marked and identified and catalogues are published. One of the first, and most productive, of these catalogues was the Sloan Digital Sky Survey (SDSS) Moving Object Catalogue. This catalogue lists astrometric and photometric data for moving objects. One advantage of these kind of data is that, sometimes, they are ready to use in the databases where the survey’s team applied the pipeline to extract the data from the observations. Other source of data are the regular ground-based observations. These are observations planned with diverse objectives, for example the morphology of a galaxy, or the metallicity of an open stellar cluster, and a minor body can be found in the background because of its movement. We want to extract this information as well. We have expertise on determining rotational periods, shapes, and pole direction on minor bodies. Noteworthy, we participated in the discovery of the rings around the centaur Chariklo and Haumea. Furthermore, using the observations mentioned above for selected minor bodies, our group was able to identify the 3D shape of Varuna, to analyse the presence of rings around centaurs, to create a database of absolute magnitudes that can be converted to diameters, to search for new unidentified objects, to the discovery of active asteroids, and to the study of many more physical properties. This work is the perfect combination on combining computer science and planetary science.

Tasks: 

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

IAA is an equal opportunity institution. Applications to this program by female scientists are particularly encouraged.

Should you need any further information or assistance concerning the application, please contact the IAA at severoochoa[at]iaa.es