The world’s first AI particle accelerator is under construction

Theo Interesting Engineeringthe Electron-Ion collider (EIC) under construction at Brookhaven National Laboratory in New York, is the first particle accelerator to integrate AI and machine learning right from the design stage. This new generation physics research facility will collide electrons with protons or nuclei to probe the structure of matter. This is a collaborative project between Brookhaven and the Thomas Jefferson National Accelerator Laboratory of the US Department of Energy (DOE), bringing together more than 300 research institutes worldwide, worth 1.7 – 2.8 billion USD and expected to begin operations in the mid-2030s.

The EIC is a 3.9 km long ring accelerator with two beams the thickness of a human hair, running in opposite directions at nearly the speed of light. The house-sized detector called ePIC will act as a high-speed 3D camera to record what happens when two beams collide. The EIC can process 500,000 collisions per second while the machine learning system classifies, sifts and reproduces what happens inside the detector. The machine reuses key components of the Relativistic Heavy Ion Collider (RHIC) at Brookhaven, which was decommissioned in February.

AI is already being used to improve accelerator operations, particle identification and data analysis at many facilities such as RHIC and the Large Hadron Collider at the European Organization for Nuclear Research (CERN). However, in the past, AI tools were often added after many years of construction. For EIC, the EIC-BeamAI team of experts from multiple institutes develops and tests AI to adjust the machine design more quickly and accurately from the beginning.

EIC will use decades of operational data from RHIC to train and test the AI ​​engine. Machine learning algorithms can maintain beam quality comparable to an experienced operator. The AI ​​system also creates a digital replica of the accelerator, a real-time virtual model that allows researchers to test many changes without touching the real machine. This replica can detect abnormal magnet behavior early enough to trigger a shutdown before damage is caused.

Theo Phys.orgscientists also used AI tools in detector design by digitally modeling the machine’s geometry and running particle collision simulations to evaluate performance. They refine the design many times through millions of simulations that require complex calculations before starting construction. The team also trained the algorithm to predict design changes that affect the detector’s ability to identify particles, saving time and reducing computational costs and energy use.

When the EIC is operational, the ePIC detector will generate 100 gigabits of data per second. AI-controlled systems will classify that data stream in real time, eliminating noise in the signal when a collision occurs. The deep learning model then converts the tiny traces left by the particles as they pass through the detector into detailed information about their energy and momentum, improving both the speed and accuracy of event reconstruction.