Speaker
Zeviel Imani
(Tufts University)
Description
Seeking to harness the power of generative modeling for neutrino physics, we have successfully generated high-fidelity images of track and shower particle event types. We implemented a diffusion model on the PILArNet public dataset comprising 2D images from a simulated Liquid Argon Time Projection Chamber (LArTPC). In this presentation, I will outline the methodology behind the score-based generative model developed by Song & Ermon in 2019, and measure the quality of our generated LArTPC images.
Primary authors
Zeviel Imani
(Tufts University)
Taritree Wongjirad
(Tufts University)
