Deep-Learning for Galaxy Morphology in the Big Data Era

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Astronomy is entering the Big Data era. The wealth of data which will soon be available from massive surveys will be invaluable for understanding galaxy evolution. However, extracting and interpreting information from enormously rich datasets is a challenge with no sufficiently efficient, demonstrated solutions to date. This is a project to tailor Deep Learning algorithms, which have been used successfully in other fields where pattern recognition matters, to measure galaxy morphologies quickly and accurately. The long-term goal is to develop algorithms which transform Big Data into Big Discovery in astrophysics. All algorithms and classifications will be made available for more general use. The principal researcher is committed to supporting scientists from under-represented groups, through hiring practices, teaching in the US and in other countries, and by helping to build the capacity for developing countries to produce cutting-edge science.Morphology is a key observable for constraining galaxy formation models, but quantifying morphology is currently a time-consuming process, severely compromised by the big-data transition. Deep Learning algorithms may be the answer. In the first phase, algorithms will learn from the large set of morphological classifications available from the Sloan Digital Sky Survey (SDSS). The next phase studies how to transfer the knowledge gained from SDSS into analysis of images from the Dark Energy Survey (DES). The use of simulated images to accelerate the learning process will also be studied. This is the first step towards the automated classification of other aspects of galaxy structure. The long-term goal is to develop algorithms which transform Big Data into Big Discovery in astrophysics. Work with Deep Learning derived morphologies will illustrate the data-to-discovery process. Teaching expertise in using the SDSS and DES databases to visiting astronomers, educators and masters-level students will help to ensure a wider global impact of the investment in building these databases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.