Predicting the Masses of Exotic Hadrons with Data Augmentation Using Multilayer Perceptron
This work addresses the problem of predicting exotic hadron masses for physicists, but it is incremental as it applies existing neural network and data augmentation methods to this domain.
The study tackled predicting masses of exotic hadrons using neural networks and data augmentation, finding that augmented data improved predictive ability and yielded results comparable to Gaussian Process and Constituent Quark Model.
Recently, there have been significant developments in neural networks, which led to the frequent use of neural networks in the physics literature. This work is focused on predicting the masses of exotic hadrons, doubly charmed and bottomed baryons using neural networks trained on meson and baryon masses that are determined by experiments. The original data set has been extended using the recently proposed artificial data augmentation methods. We have observed that the neural network's predictive ability increases with the use of augmented data. The results indicated that data augmentation techniques play an essential role in improving neural network predictions; moreover, neural networks can make reasonable predictions for exotic hadrons, doubly charmed, and doubly bottomed baryons. The results are also comparable to Gaussian Process and Constituent Quark Model.