Friday 07 March 2025
A team of researchers has made a significant breakthrough in the field of high-energy physics by using machine learning to infer a functional form of fragmentation functions directly from experimental data. Fragmentation functions are crucial for understanding hadron production, which is a fundamental aspect of particle physics.
In recent years, physicists have been working tirelessly to understand the behavior of subatomic particles at high energies. One of the key challenges in this field is the need to develop more accurate models of fragmentation functions, which describe how particles break apart into smaller fragments when they collide. These functions are essential for predicting the outcomes of particle collisions and understanding the properties of hadrons, such as protons and neutrons.
Traditionally, physicists have relied on global fits of experimental data using pre-assumed functional forms to learn about fragmentation functions. However, this approach has its limitations, as it requires a deep understanding of the underlying physics and can be prone to bias. In contrast, machine learning offers a more flexible and objective way to infer these functions from data.
The researchers used a technique called symbolic regression to develop a model that can learn a functional form of fragmentation functions directly from experimental data. Symbolic regression is a type of machine learning algorithm that uses a combination of mathematical operations and logical rules to discover equations that describe complex phenomena.
To test their approach, the team analyzed data from several high-energy particle collisions, including those conducted by the COMPASS experiment at CERN. By applying their symbolic regression model to this data, they were able to infer a functional form of fragmentation functions that accurately predicted the outcomes of subsequent collisions.
The implications of this breakthrough are significant. It opens up new possibilities for physicists to explore the properties of hadrons and understand the behavior of subatomic particles at high energies. Furthermore, it paves the way for more accurate predictions of particle collisions, which is essential for advancing our understanding of the fundamental forces of nature.
In addition to its scientific significance, this research also has practical implications. For example, it could be used to improve the design of particle colliders and enhance the precision of particle detectors. These advancements could lead to a deeper understanding of the universe and potentially unlock new technologies with applications in medicine, energy production, and other areas.
The use of machine learning in high-energy physics is an exciting development that has the potential to revolutionize our understanding of the fundamental laws of nature. By combining the power of artificial intelligence with the precision of experimental data, physicists can make significant strides in advancing our knowledge of the universe.
Cite this article: “Machine Learning Breakthrough Unlocks New Possibilities in High-Energy Physics”, The Science Archive, 2025.
High-Energy Physics, Machine Learning, Fragmentation Functions, Particle Collisions, Hadron Production, Subatomic Particles, Symbolic Regression, Cern, Compass Experiment, Artificial Intelligence
