LEIA AND LOUISE: DATABASE DEVELOPMENT AND INTERFACING FOR ASTROPHYSICS RESEARCH

Project 1640, a collaboration between the American Museum of Natural History and California Institute of Technology, works to discover possible exoplanet candidates using a novel telescopy technique at Palomar Observatory in Palomar Mountain, California. Differing from the Kepler technique, using a device outside of Earth’s atmosphere, the technique uses a coronagraphic mask to correct for distortions caused by airmass and seeing quality. Thus, scientists in the P1640 group spend a majority of their efforts processing a large series of digital images, placing the mask, or filter, in a process known as occulting, to identify possible star companions. The data collected by P1640, of which there is a great deal, had previously solely been stored in a shared group server in a disjointed and arbitrarily complex organization. The Leia/Louise project’s purpose was to create a tool allowing P1640 researchers to more conveniently access information in the data already processed and the state of the image processing workflow. Using the database language SQL and the Python web package Django, the Leia/Louise project created a model for astrophysics data schematics, and set a precedent for straightforward nontechnical user interfacing, both of which can be applied to other physical sciences research projects with similarly unruly datasets.