From RHIC to the EIC: How John Lajoie and ORNL Are Engineering the Future of Particle Physics

Aug 23

9 min read

The next generation of particle physics will depend not only on more powerful accelerators, but on an equally important technology operating at the point where invisible quantum events become measurable information. Advanced particle detectors are becoming sophisticated scientific instruments capable of tracking trajectories, measuring energy, identifying particles and processing enormous data streams in real time. At the U.S. Department of Energy’s Oak Ridge National Laboratory, physicists such as John Lajoie are helping push this technology toward a new frontier through work connected to the future Electron-Ion Collider, or EIC.

The significance extends well beyond particle physics. Detector development creates technologies, data systems and highly specialized engineering expertise that can migrate into materials science, national security, biomedical research and other fields. The emerging combination of advanced detectors, high-speed computing and artificial intelligence could therefore produce benefits that are difficult to predict when the instruments are first designed.

Why Particle Detectors Matter to Fundamental Physics

Particle accelerators create conditions in which scientists can investigate matter at extraordinarily small scales. Yet an accelerator alone cannot reveal what happens during a collision. The detector is the measurement system that captures the evidence.

When particles collide at high energies, the resulting fragments can travel in different directions and exhibit properties that reveal the structure and behavior of matter. A modern detector therefore functions as a sophisticated information pipeline. It converts physical interactions into electrical signals, digitizes those signals, reconstructs particle behavior and ultimately produces data that researchers can analyze.

Three broad categories are particularly important:

Tracking detectors reconstruct the paths of charged particles.
Calorimeters measure deposited energy and help determine particle properties.
Particle identification systems distinguish particle types using measurable characteristics.

The challenge is that these systems must work together. A single measurement rarely provides enough information to identify a particle or reconstruct a complex collision. Combining multiple detector technologies creates a much richer picture of what occurred.

This is where detector design becomes an interdisciplinary problem involving physics, electronics, materials, software, data acquisition, computing and engineering.

John Lajoie and the Engineering of Discovery

John Lajoie, who leads the Relativistic Nuclear Physics Group within ORNL’s Physics Division, has built his career around precisely these difficult problems. His work sits between fundamental scientific questions and the engineering required to answer them.

Lajoie is also spokesperson for the ePIC Collaboration, the international scientific effort developing the first detector for the future Electron-Ion Collider at Brookhaven National Laboratory. The collaboration brings together hundreds of scientists and engineers from 183 institutions across 26 countries.

The EIC has been identified as a top priority in the U.S. long-range nuclear physics research program. Its scientific mission is closely tied to one of the central questions in modern nuclear physics: how quarks and gluons interact to produce the properties of protons, neutrons and nuclear matter.

Quarks and gluons are governed by the strong interaction, the fundamental force responsible for binding the components of atomic nuclei. Although the underlying theory of the strong force is well established, many aspects of how its dynamics produce the observable properties of matter remain challenging to calculate and understand.

The EIC is intended to provide a new experimental environment for investigating these questions.

The Electron-Ion Collider’s Detector Challenge

The EIC will require an exceptionally capable detector because the scientific questions are not limited to identifying individual particles. Researchers want to reconstruct complex interactions and understand how the internal structure of protons and nuclei emerges from quarks and gluons.

The ePIC detector incorporates multiple complementary systems. Its design includes three types of trackers, seven calorimeter systems and four particle identification detector systems.

This architecture illustrates an important principle in experimental physics: greater scientific capability often comes from combining specialized instruments rather than relying on a single universal sensor.

Detector system	Primary role	Scientific value
Trackers	Reconstruct charged-particle trajectories	Reveals particle motion and interaction characteristics
Calorimeters	Measure deposited energy	Helps determine energy and particle behavior
Particle identification detectors	Distinguish particle species	Adds critical information about collision products
Streaming data system	Continuously processes detector information	Enables real-time selection and analysis

The engineering challenge is to integrate these systems without allowing increasing complexity to make the overall instrument impractical. Scientists must simultaneously consider spatial constraints, performance, reliability, cost, electronics, data rates and computational requirements.

The result is a scientific optimization problem in which every improvement can introduce another engineering trade-off.

From Triggered Experiments to Continuous Data

One of the most consequential characteristics of ePIC is its planned streaming data architecture.

Traditional high-energy physics detectors have often relied on trigger systems. A trigger examines incoming signals and decides whether a particular event appears important enough to retain for detailed processing. This approach is essential when raw detector output exceeds available storage and computing capacity.

A streaming architecture changes the philosophy.

Instead of relying entirely on predefined triggers, the detector continuously collects information while sophisticated processing systems determine which signals deserve attention. This creates new opportunities, but also introduces a major computational challenge.

The system must distinguish scientifically valuable information from overwhelming volumes of incoming data quickly enough to keep pace with the experiment.

That is where artificial intelligence and machine learning become particularly important.

AI Moves Closer to the Detector

Artificial intelligence is increasingly being incorporated into scientific instruments rather than being reserved exclusively for post-experiment analysis.

For ePIC, AI-assisted processing can operate near the point where detector data is generated. This concept, often described as AI at the edge, means computational models can help identify significant patterns before the complete data stream reaches downstream analysis systems.

The distinction is important.

A conventional workflow might generate, store and subsequently analyze large quantities of data. An intelligent streaming system instead attempts to make informed decisions during acquisition itself.

Potential advantages include:

Faster identification of scientifically significant signals
More efficient use of computing and storage resources
Greater ability to handle continuous data streams
Flexible recognition of complex patterns
Reduced dependence on rigid, predefined selection criteria

However, AI does not eliminate the underlying scientific challenge. A model must still be trained, evaluated and monitored. Researchers need to understand its performance, failure modes and potential biases. An automated decision made at the detector level can affect what information is ultimately available for scientific analysis.

Consequently, AI becomes part of the experimental methodology itself, not simply a convenient software tool.

The Unexpected Ripple Effect of Detector Innovation

One of the most important lessons from detector science is that the value of a technology can extend far beyond the experiment for which it was created.

A detector designed for fundamental physics can contain advances in sensing, electronics, data acquisition, radiation detection and computational processing that become useful elsewhere.

ORNL provides examples of this technology transfer. Detector technologies developed for fundamental research have supported applications such as radiological monitoring. The Timepix4 detector, originally developed at CERN for particle physics, is also being integrated into microscopes at ORNL’s Center for Nanophase Materials Sciences for materials characterization.

This illustrates why basic science can have economic and technological consequences that are difficult to forecast in advance.

The original scientific question might concern the structure of matter, while the resulting technology could eventually support industrial inspection, security systems, advanced microscopy or other applications.

The pathway is rarely linear. A capability developed to solve one difficult problem can become the missing component in an entirely different scientific or engineering challenge.

Detector Development Is Also a Workforce Engine

The benefits of major scientific projects are not limited to hardware and discoveries. Large detector programs create environments in which researchers learn how to solve problems for which no established solution exists.

Students, interns, engineers and scientists working on such systems gain experience across multiple disciplines. They may encounter problems involving electronics one day, data processing the next and mechanical integration later.

Even when these researchers eventually move outside fundamental physics, the problem-solving skills developed through demanding scientific projects can remain valuable.

This creates a second-order benefit from large research programs. The scientific infrastructure produces both technologies and people capable of developing the next generation of technologies.

From RHIC to the Electron-Ion Collider

Lajoie’s experience at Brookhaven’s Relativistic Heavy Ion Collider provides an important foundation for his work on the EIC.

Before joining ORNL in 2023, he spent more than 26 years at RHIC. His work included contributions to the PHENIX detector and management of the construction of hadronic calorimeters for its sPHENIX upgrade.

Those systems supported research into quark-gluon plasma, a state of matter associated with the conditions of the early universe.

The transition from RHIC to the EIC represents continuity as well as technological evolution. Experience gained from previous generations of detectors provides practical knowledge about construction, integration, data acquisition and scientific interpretation.

At the same time, the EIC introduces new demands, particularly around its streaming architecture and the integration of AI-assisted processing.

Why the EIC Could Have Effects Beyond Nuclear Physics

The historical development of science demonstrates that fundamental discoveries can eventually become foundations for transformative technologies.

James Clerk Maxwell’s unification of electricity and magnetism in the nineteenth century provides a classic example. Theoretical advances in understanding electromagnetism ultimately contributed to technological developments that shaped communications, computing, electrical engineering and numerous other industries.

The lesson is not that every fundamental physics project will produce an identifiable commercial technology. Rather, scientific infrastructure can expand the boundaries of what engineers and researchers are capable of building.

The EIC may produce a similar ripple effect through advances in particle detection, high-speed data processing, AI-assisted scientific computing, radiation-tolerant electronics and precision instrumentation.

The timeline for such benefits cannot necessarily be predicted. Some applications may emerge quickly, while others may appear only after technologies migrate into completely different research environments.

The Strategic Importance of Building What Does Not Yet Exist

The central challenge of detector science is that researchers often need instruments capable of measuring phenomena that existing technology cannot adequately capture.

That creates a cycle:

A scientific question exposes a measurement problem.
Engineers and physicists identify the limitations of existing detectors.
New sensing, electronics or computing approaches are developed.
The resulting instrument produces measurements that were previously inaccessible.
Unexpected observations generate new scientific questions.
Those questions create demand for another generation of technology.

This process explains why detector development is more than technical support for physics. It is an integral part of scientific discovery.

The most valuable experiment is not necessarily the one that confirms the original prediction. Unexpected results can be more consequential because they expose gaps in existing understanding.

Detector technology therefore determines not only what scientists can measure, but also which questions nature is able to answer.

The Future of AI-Enabled Particle Detection

The combination of increasingly capable detectors and AI could reshape experimental physics over the coming decades.

Future systems may increasingly distribute intelligence throughout the measurement pipeline, from sensors and front-end electronics to real-time reconstruction and high-level scientific analysis.

Several developments are particularly significant:

Real-time inference: AI models could identify complex signatures while experiments are running.
Adaptive data acquisition: Detector systems could dynamically prioritize scientifically valuable information.
Automated reconstruction: Machine learning could accelerate the conversion of raw signals into physical measurements.
Integrated scientific computing: Hardware and AI algorithms could increasingly be designed together rather than independently.
Cross-disciplinary technology transfer: Detector innovations could move more rapidly into medicine, materials science, security and industrial applications.

These advances will also require rigorous validation. Scientific instruments must remain trustworthy, reproducible and interpretable. The greater the role of automated decision-making, the more important it becomes to understand how those decisions affect experimental results.

A New Era of Seeing the Quantum World

The work being undertaken at ORNL and through the ePIC Collaboration represents a broader transformation in experimental science. Modern physics increasingly depends on instruments that combine precision sensing, sophisticated electronics, high-performance computing and artificial intelligence.

The Electron-Ion Collider is especially important because it aims to investigate the internal dynamics of the matter that forms the visible world. Yet its potential significance extends beyond the scientific questions written into its original mission.

Advanced detectors can become platforms for technological innovation. AI can turn continuous streams of measurements into actionable scientific information. And the people trained to solve the engineering problems behind these systems can carry their expertise into fields far beyond particle physics.

For Dr. Shahid Masood and the expert team at 1950.ai, the EIC offers a compelling example of a larger principle shaping advanced technology: breakthroughs often emerge when fundamental science, computational intelligence and engineering capability converge. The most important discoveries may ultimately come not only from answering the questions scientists already know how to ask, but from building instruments capable of revealing phenomena that force humanity to ask entirely new questions.

Key Takeaways

Advanced particle detectors transform otherwise invisible quantum interactions into measurable scientific data.
ORNL physicist John Lajoie is contributing to the development of detector technologies for the future Electron-Ion Collider.
The ePIC detector combines multiple tracking, calorimetry and particle identification systems.
Its streaming architecture represents a significant shift from conventional trigger-based data acquisition.
AI and machine learning can become part of the detector’s front-end decision-making process through AI at the edge.
Detector technologies can produce unexpected applications in areas such as radiological monitoring and materials characterization.
Major physics projects also cultivate a workforce trained to solve complex, previously unsolved problems.
The long-term technological consequences of the EIC may extend well beyond nuclear physics.