N3AS Undergraduate Students

Malika Golshan, Current student

Neutrinos, once thought to be massless according to the standard model, have proven otherwise due to the fascinating observation of neutrino flavor oscillations. In our research project, we take a unique approach by harnessing the power of machine learning to address a crucial question: Can computers aid in distinguishing between mass-bearing and massless neutrinos? By leveraging Convolutional Neural Networks (CNNs) on simulated weak lensing maps, our goal is to achieve a more accurate measurement of neutrino mass using cosmological methods.

I am deeply grateful to both my mentors. Vanessa, my initial mentor, provided me with a solid foundation and inspiration for this project. Adrian then became my mentor while transitioning to his postdoctoral position at Princeton. His unwavering dedication and expertise guided me through the intricacies of data analysis.

Malika Golshan

Graduated 2023, currently training in Los Alamos, working with the DUNE team

Discovering the origin of neutrino’s mass and their oscillation parameters could answer the matter antimatter asymmetry in our universe, and allow us to better understand astrophysics phenomena. Currently, multiple flagship experiments exploring neutrinos are underway, and to have full confidence in their results, accurate and precise neutrino nucleus cross sections are required. My project is to analyze data from lattice QCD calculations to determine one of these cross sections.