Generative Adversarial Networks for Particle Physics Applications

The project aims implementation of GANs in the Machine Learning toolkit, TMVA of the ROOT framework would be immensely useful because of the advent, popularity and versatile nature of GANs. GANs can essentially be used for simulation and physical/mathematical modeling of patterns learned from training data substantially faster and more accurate than any other generative model. The model can be used for generating training data and finds many applications in high particle physics and astrophysical research realms.