Physics Informed Deep Learning Model for Deeply Virtual Compton Scattering

We present a physics informed deep learning technique for Deeply Virtual Compton

Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized

and polarized electron beam. Training a deep learning model typically requires a large size of

data that might not always be available or possible to obtain. Alternatively, a deep learning

model can be trained using additional knowledge gained by enforcing some physics constraints

such as angular symmetries for better accuracy and generalization. By incorporating physics

knowledge to our deep learning model, our framework shows precise predictions on the DVCS

cross sections and better extrapolation on unseen kinematics compared to the basic deep learning

approaches. We also introduce a new methodology for the uncertainty quantification throughout

the latent analysis of our physics informed network.