Speaker
Zeviel Imani
(Tufts University)
Description
We present a data-driven approach for the generation and inference of 2D LArTPC events. Using a conditional latent diffusion model trained on LArTPC images, we have demonstrated the generation of physically realistic protons. Combining this conditional model with Earth Mover’s Distance (EMD) enables us to perform stochastic gradient descent to efficiently infer the 3D momentum of an input image and subsequently generate physically similar events. This same framework naturally serves as a discriminator of the number and types of particles present in LArTPC images. Because this approach requires no underlying physics simulation, it is particularly appealing for regimes where traditional event generators struggle.
| Contribution types | Short talk (15min + 5min Q/A) |
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Author
Zeviel Imani
(Tufts University)
