Finding Space Junk with the World’s Biggest Telescopes

Project Summary

A team of students led by Physics professors Dan Scolnic, Michael Troxel and Chris Walter will build their own algorithms to use images taken as part of The Dark Energy Survey, one of the largest cosmological surveys, to learn more about all the things we find in space that we aren’t looking for. These can be anything from image artifacts, to cosmic ray hits, to satellite trails to Elon Musk's car (see picture). Each of these different things has their own signatures on the images, and automatic detection and identification algorithms would enable improved image processing. As surveys attempt to measure increasingly difficult and subtle features of the universe, like the imprint of dark energy and dark matter, identification of any kind of artifact will be critical.

Project Lead: Dan Scolnic, Michael Troxel, Chris Walter

Themes and Categories
Year
2020
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Related Projects

A team of students that worked together for a semester in the Mission Driven Startups class will obtain and analyze data to create a predictive maintenance model for F15-E Fighter Jets from Seymour Johnson Air Base. Using data provided by the Base, the Data+ team will evaluate the relationship between unscheduled maintenance and external factors such as weather, sortie hours between repairs, and failure frequency of aircraft components. These findings will then feed into a predictive maintenance model to enhance the Air Force Crew’s ability to anticipate maintenance needs, helping to minimize unscheduled aircraft downtime. 

 

Faculty Lead: Dr. Emma Rasiel

Client Lead: Lt. Devon Burger

Project Manger:  Vignesh Kumaresan

A team of students, led by Electrical and Computer Engineering professor Vahid Tarokh, will develop methods to improve the efficiency of information processing with adaptive decisions according to the structure of new incoming data. Students will have the opportunity to explore data-driven adaptive strategies based on neural networks and statistical learning models, investigate trade-offs between error threshold and computational complexity for various fundamental operations, and implement software prototypes. The outcome of this project can potentially speed up many systems and networks involving data sensing, acquisition, and computation.

Project Leads: Yi Feng, Vahid Tarokh

A team of students will explore new ways of reading pre-modern maps and perspectival views through image tagging, annotation and 3D modeling. Each student will build a typology of icons found in these early maps (for example, houses, churches, roads, rivers, etc.). By extracting, modeling, and cataloging these features, the team will create a library of 2D and 3D objects that will be used to (a) identify patterns in how space and power are represented across these maps, and (b) to create a model for “experiencing” these maps in 3D, using the Unity game engine platform. This is a combined Data+ / Bass Connections project that will instruct students in qualitative and quantitative mapping techniques, basic 3D modeling and the history of cartography.

Project Lead: Philip Stern, Ed Triplett

Project Manager: Sam Horewood