Particle physicists are constantly looking for ways to improve the performance of particle accelerators, which are essential tools for studying the fundamental building blocks of matter. One area where they are making significant progress is in the use of artificial intelligence (AI) to enhance beam dynamics.
Beam dynamics refers to the behavior of charged particles as they travel through an accelerator. The goal is to keep the particles tightly focused and moving at the desired speed and trajectory. However, there are many factors that can affect beam dynamics, such as magnetic fields, radiofrequency cavities, and even the shape of the accelerator itself. Particle physicists have traditionally relied on complex simulations and trial-and-error experiments to optimize beam dynamics, but AI is now offering a more efficient and effective approach.
In a recent article published in Physics World, researchers from the European Organization for Nuclear Research (CERN) and the University of Manchester described how they are using AI to improve beam dynamics at the Large Hadron Collider (LHC), the world’s largest and most powerful particle accelerator. The LHC is used to smash protons together at high energies, producing a shower of subatomic particles that can reveal new physics phenomena.
The researchers used a machine learning algorithm called a neural network to analyze data from the LHC’s beam position monitors, which measure the position of particles in the accelerator. The neural network was trained on a large dataset of simulated beam dynamics scenarios, allowing it to learn patterns and correlations that would be difficult for humans to discern.
Once trained, the neural network was able to predict the behavior of the beam with high accuracy, even in situations where traditional simulation methods were less reliable. This allowed the researchers to quickly identify and correct issues with beam dynamics, leading to more stable and efficient operation of the LHC.
The use of AI in particle physics is not limited to beam dynamics. Researchers are also exploring its potential for data analysis, event selection, and even detector design. For example, a team at Fermilab in the United States used a neural network to identify rare particle decays in data from the Mu2e experiment, which is searching for evidence of new physics beyond the Standard Model.
While AI is still in its early stages of development in particle physics, its potential for enhancing research is clear. By automating complex tasks and uncovering hidden patterns in data, AI can help researchers make new discoveries and push the boundaries of our understanding of the universe.
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