Professor Lucy Fortson

CSENG Physics & Astronomy
College of Science & Engineering
Twin Cities
Project Title: 
Gamma Ray Astrophysics; Zooniverse Crowdsourcing Science

The Fortson research group is focused on two main research areas, both of which can require MSI resources.

  • Gamma Ray Astrophysics: Supercomputing resources are required for analyzing data recorded with the NASA Fermi-LAT satellite. Typically this is run in several stages depending on the data products required such as counts maps, test statistic maps, spectra and light curves. For example, to perform a standard binned analysis on a single gamma-ray source (using all the photons collected by the Fermi satellite to date) this typically requires about 2 GB of disk storage space with memory usage between 2 to 4 GB using approximately 15 CPU hours. This example is for a Log Likelihood analysis of an object situated away from the Galactic plane where the relative number of nearby Fermi sources is smaller and the diffuse background emission low. For an object on or close to the Galactic plane the same analysis could easily take 30 CPU hours depending on the number of sources to be included in the Log Likelihood fit. For data products such as a test statistic map which can only be generated once the standard analysis is complete, this requires significantly longer CPU hour usage e.g. ~168 CPU hours. This is because a maximum likelihood computation is performed on each and every pixel in the requested map. In addition, this group is in a position to start making full use of Singularity. The installation of Singularity will greatly help the researchers to deploy pre-made data analysis containers for Fermi-LAT without the need for complex software installation. They expect to analyze several dozen Fermi-LAT sources again during 2020.
  • Zooniverse Citizen-Science Platform: The Zooniverse is the world’s largest online citizen science platform and several members of the Fortson group are involved in the development and analysis of Zooniverse project data. The group uses MSI resources to batch process hundreds of thousands of images in preparation for their upload to the Zooniverse site. The group also plans to use MSI's GPU computing capabilities to rapidly train machine-learning classifiers based on convolutional neural networks (CNNs). The trained CNNs can be used to efficiently classify very large numbers of astrophysical images that are expected to be produced by forthcoming survey instruments. The researchers plan to train machine learning models on volunteer labels gathered for scientific datasets uploaded to the Zooniverse citizen science platform. For the most part, this work will involve experiments cross-validating different model architectures in order to maximize performance.

Several Zooniverse projects have been featured on the MSI website:


Project Investigators

Suhail Alnahari
Professor Lucy Fortson
Melanie Galloway
Rafia Omer
Cameron Rulten
Karlen Shahinyan
Andrea Simenstad
Abinash Sinha
Trevor Wennblom
Patrick Wilcox
Darryl Wright
Are you a member of this group? Log in to see more information.