Lee Hagaman - Data-driven Model Validation For Neutrino Cross Section Measurements

Wednesday 5 Mar 2025, 13:30 → 14:30 UTC

610 (G.O. Jones Building)

Lee Hagaman (MiniBooNE (University of Chicago))

About the speaker: Lee Hagaman got his undergraduate degree at University of California Berkeley, working on an R&D project measuring optical properties for liquid xenon dark matter experiments. He started his PhD at Yale in 2019, focusing on the MicroBooNE experiment with Professor Bonnie Fleming, before transferring with her to the University of Chicago in 2023. 

On MicroBooNE, he focused on investigations of the MiniBooNE anomaly in both the electron and photon channels, and also contributed to cross section analyses. He plans to continue his research on MicroBooNE, SBND, and DUNE as a postdoctoral researcher at Columbia University starting in the summer 2025.

 

Hybrid link: https://fnal.zoom.us/j/93197574512?pwd=bGlJTHlGTnVtTFFwRnlpYnhrdm5rdz09

lhagaman_queen_mary_model_validation.pdf

1 Data-driven Model Validation For Neutrino Cross Section Measurements

Neutrino-nucleus cross section measurements are often performed using event topologies which have complex detector efficiencies and smearings, and non-negligible backgrounds. Therefore, the use of a neutrino event generator is required in order to estimate these factors before unfolding a cross section measurement, and this inevitably leads to model dependence of the extracted cross section. To reduce this model dependence, it is common to validate the unfolding using fake data sets produced by different event generators, but this method has limitations. In this seminar, I will describe data-driven model validation techniques and how we use them to address model dependence in the MicroBooNE experiment.

Speaker: Lee Hagaman