Adding Scientific End-to-End Tests to the Medium-Energy Gamma-ray Astronomy Library (MEGAlib)
Andreas Zoglauer, Staff Researcher
Space Sciences Laboratory
Applications for Fall 2025 are closed for this project.
MEGAlib is a toolkit designed to calibrate, simulate, reconstruct, and analyze data from gamma-ray detectors. It is widely used in astrophysics, solar physics, planetary science, nuclear security applications, and medical imaging.
To support the continued development of MEGAlib, we plan to implement a set of end-to-end tests that will run automatically whenever there is a significant update to the MEGAlib code. These tests will help detect regressions or unintended changes in performance.
Each end-to-end test will:
+ Implement the geometry of a specific detector (e.g., spectrometer, Compton telescope)
+ Set up a detector effects engine (e.g., energy resolution, thresholds)
+ Perform Monte Carlo simulations
+ Reconstruct the data
+ Carry out high-level analyses (e.g., generating spectra, images)
Intermediate and final results will be statistically compared to results from previous runs to identify any significant deviations.
The outcomes should be presented in a human-readable format—such as a PDF report containing plots of spectra, comparisons, and differences—so that potential issues can be quickly diagnosed and understood.
Role: Undergraduate team members will be responsible for implementing specific end-to-end tests. Each student will develop their own test, while the team collaborates with the lead developer to set up the infrastructure for running the tests and performing statistical comparisons.
We expect to build a team of three students.
Learning outcomes include gaining knowledge of:
+ How gamma-ray detectors operate
+ The nature and structure of gamma-ray detector data
+ Types of measurements gamma-ray detectors provide
+ Statistical methods for comparing datasets
+ Practical programming and data analysis skills in Python
Qualifications: All end-to-end tests will be controlled by Python scripts and analyzed using Python. Thus, the key qualifications are focused on programming and data analysis:
+ Proficiency in Python 3
+ Familiarity with git and GitHub
+ Basic statistical understanding, including comparison of 1D, 2D, and 3D distributions
+ Sufficient time and commitment to work collaboratively with the team
Application Instructions
In your application, please describe your prior research or programming experience and explain why you are interested in joining our team. We look forward to hearing from you!
Hours: 9-11 hrs
Off-Campus Research Site: We will meet weekly either via zoom or at the Space Sciences Laboratory. Please take the free hill shuttle from the Hearst mining circle to the top of the hill. SSL is the last stop (~10 min bus ride).
Related website: https://megalibtoolkit.com
Related website: https://github.com/zoglauer/megalib