#United States #Machine Learning #Lakeland #Florida Poly #Nuclear Physics

Florida Polytechnic University Innovates Nuclear Physics

A significant advancement in the field of nuclear physics has emerged from Florida Polytechnic University, where researchers have developed an innovative machine learning model that enhances the accuracy of predicting nuclear binding energies. This unprecedented accuracy helps scientists delve deeper into understanding the fundamental components of matter.

Breakthrough Model Overview

The driving force behind this groundbreaking innovation is Dr. Ian Bentley, the esteemed professor and chair of the Department of Physics at Florida Polytechnic University. He introduced a novel technique known as the Four Model Tree Ensemble, which is a sophisticated amalgamation of various machine learning models. Through this method, Dr. Bentley and his team were able to achieve levels of prediction precision that outstrip existing models in the nuclear physics domain.

Dr. Bentley unveiled his model at an international astrophysics conference in Germany, igniting discussions about its remarkable predictive capabilities. In addition, he published two pivotal papers this year in the prestigious journal, Physical Review C, detailing his findings and methodology.

“In the beginning, I was incredulous. I closely examined other models to evaluate their performance against ours,” Dr. Bentley remarked. His persistence has paved the way for a significant leap in predicting nuclear mass measurements, a critical factor in understanding elemental formation in the universe.

The Science Behind Nuclear Masses

Nuclear masses denote the mass contained within an atom's nucleus, a vital aspect necessary for comprehending how elements are synthesized throughout the cosmos. While the production of lighter elements such as helium, carbon, and oxygen through thermonuclear fusion is well understood, scientists continue to unravel the complex processes responsible for the creation of heavier elements like gold, lead, and uranium.

Dr. Bentley elucidates that extreme astrophysical contexts, such as supernova events and neutron star collisions, are thought to contribute to the formation of these heavier elements. Accurate data on nuclear mass is essential for simulating such cosmic phenomena and conducting experiments aimed at creating new nuclei.

His work highlights a crucial intersection between theoretical knowledge and experimental pursuits in nuclear physics.

A New Approach to Modeling

Traditional methods have often employed neural networks or kernel-based machine learning techniques to tackle prediction challenges. Dr. Bentley's approach, however, diverges from the norm by utilizing an advanced algorithm that integrates multiple decision trees, leading to superior accuracy in predictions. “Just four years ago, no one was employing this methodology,” he noted.

In addition to his impactful presentation in Germany, Dr. Bentley has garnered invitations to share his insights at two renowned national laboratories, establishing his status as a thought leader in the field.

Plans are already underway for further research in this domain, with a collaborative team that includes undergraduate students James Tedder and Anthony Fiorito. “Anthony will guide me on effectively applying physics-informed machine learning, while they learn foundational physics from me. It’s an exciting venture,” Dr. Bentley shared enthusiastically.

This remarkable achievement not only propels the research efforts of Florida Polytechnic University forward but also positions the institution as a key player in transforming the landscape of nuclear physics through enhanced predictive modeling. As scientists work to unlock the mysteries of elemental formation, this pioneering model stands as a beacon of progress, promising a new dawn in the accuracy and understanding of nuclear binding energies.

• ---

Florida Polytechnic University continues to push the boundaries of knowledge and innovation, making waves in the academic community with their dedication to advancing science and technology.