NSF Award
1546079
While the most significant projects in modern observational astrophysics generate very large data sets, the computational methodology lags behind, and has trouble effectively analyzing these data. Although current and future astronomical surveys will produce the world's largest public databases, the methods to turn these data into scientific discoveries do not yet exist. This project will develop an automatic query-by-example system for classifying galaxies by their similarities to other galaxies, using unsupervised machine learning techniques. This capability does not currently exist and it can substantially enhance the experience and discovery power of digital sky surveys. The project has an educational component, focusing in particular on undergraduate research for under-represented minority students.<br/><br/>Existing, and especially planned, surveys can image billions of galaxies, making the ability to study rare galaxies through computer analysis absolutely essential. These uncommon objects are critical for understanding the most fundamental questions about the early, present, and future universe, as they carry crucial information on the history of the interactions of objects, their formation, and their evolution. The system to be developed will take an image of a certain (normally peculiar) galaxy, identified by the researcher as being of interest, and will search through millions of galaxies to find the visually most similar galaxies to the query galaxy. Because studying unusual galaxies and making scientific conclusions about their nature requires a certain population from which to derive properties that can be compared to other systems, this capability and the resulting listings will greatly increase the ability to make discoveries from sky surveys, optimizing the scientific return of these important and expensive research instruments.<br/><br/>This project connects with existing efforts to attract under-represented minorities, adding more advanced research training in the later years of their undergraduate degree. Studies like this always include opportunities for public outreach, and the team expects to contribute to the forming big data hub in their area.
