Machine Learning classification of astronomical transients
Saul Perlmutter, Professor
Physics
Closed. This professor is continuing with Fall 2023 apprentices on this project; no new apprentices needed for Spring 2024.
A main challenge in time domain astronomy is that of classifying different types of high-energy transients from large datasets solely based on imaging data or sparse spectroscopic observations. As part of this project, the student will use state-of-the-art machine learning techniques to identify different classes of transients in the latest Dark Energy Camera (DECam) and Dark Energy Spectroscopic Instrument (DESI) data. For students interested in the astronomical component of transients other than the classification, this project can also be extended to analyses of interesting transients, for example of transients from gravitational wave sources like kilonova candidates, which are expected to emerge from the merger of two neutron stars, or a neutron star and a black hole. Analyses of transients will be used to infer the physical properties of the mergers, to constrain the merger rate of merging neutron stars in the Universe or the Hubble constant.
Qualifications: Proficiency in Python coding. Experience with numpy/scipy/matplotlib/pandas, machine learning, and parallel computing is a plus.
Day-to-day supervisor for this project: Antonella Palmese
Hours: to be negotiated
Off-Campus Research Site: Lawrence Berkeley National Lab, Building 50, or Campbell Hall. This project can also be carried out remotely with regular Zoom meetings.
Engineering, Design & Technologies Mathematical and Physical Sciences