Research Projects

The community is working to find the correct model for addressing the experimental and theoretical shortcomings of the Standard Model. They have developed numerous new physics (NP) models, but the challenges include identifying all types of observables, combining different measurements, effectively analyzing large models with numerous parameters, and overcoming library dependency and lack of backward compatibility. The goal is to bring relevant observables and NP models within a single, coherent framework, select the right scenarios, find model parameter-space satisfying observable precision, and make it future-proof and less resource-intensive. Mathematica will be used to maintain backward compatibility, and dedicated interfaces and databases will be created to overcome library dependencies. Machine learning (ML) will be used to overcome the shortcomings of existing information-theoretic model selection techniques. Modern optimized ML algorithms, particularly neural networks, can tune model complexity and minimize generalization error by using unseen data sets for validation. This can be used for regression, classification, and model selection. The resulting ML framework will be instantaneous in computation and work for all future measurements of the same observable set. Transfer learning of the same network takes significantly less time and resources. The framework will be easily expandable and customizable for any user-defined model, and all trained neural networks will be free and operating system independent. A Git-user community will be responsible for future maintenance of online resources.