Visualization Platform for Earthquake Catalog Completeness
Qingkai Kong, UC Berkeley Seismology Lab
Discovery Team will build a small website/tool to visualize the earthquake catalog completeness from various sources, i.e. USGS, or Japan catalog etc. The completeness of a catalog is the minimum magnitude above which all earthquakes within a certain region are reliably recorded. The work will involve building a website that could query the earthquake catalog from USGS like agencies, and then based on the time and space parameter the user selected, the website/tool will return an image of completeness of the catalog for this region at this time span. The goal of this small project is to have an open source website/tool that the community could use, and a journal/conference paper is an end product as well.
Evaluating SmallSat Performance
Andreas Zoglauer, Space Science Labs
Evaluating the performance of a concept for a SmallSat astrophysics space mission to map the inner Milky Way in low-energy gamma rays and to observe the polarization form pulsars, AGN, etc. One key open question is how good we will be able to identify and suppress gamma-ray background originating from internal radioactive activation and subsequent decays as well as from the Earth’s albedo emission. Applying machine learning techniques to identify the background and distinguish it from source gamma rays will be the key topic of this project.
Reducing Human Radio Frequency Interference
Steve Croft, Berkeley SETI Research Center
Breakthrough Listen is the world's leading search for extraterrestrial intelligence, using some of the world's most powerful telescopes to survey planetary systems around nearby stars for signs of technology. Students will develop code to extract features from large arrays of image-like data, visualize and explore patterns in the data using approaches such as PCA and clustering. Apply unsupervised and supervised approaches to reject human radio frequency interference and hone in on signals of interest. Develop interfaces allowing for labeling of data by volunteers.