TL;DR:
- Cristiano Fanelli of William & Mary utilizes AI and machine learning to enhance nuclear physics research.
- U.S. Department of Energy funds $16 million for AI/ML research in nuclear physics.
- Fanelli leads projects optimizing detector designs for the Electron-Ion Collider (EIC).
- AI/ML techniques enhance beam polarization and particle reconstruction, improving experiment performance and reducing costs.
- William & Mary fosters AI and machine learning literacy through workshops and hackathons.
- The synergy between human expertise and AI promises groundbreaking advancements in nuclear physics.
Main AI News:
In the realm of nuclear physics, where the mysteries of the universe’s fundamental forces are unveiled, the significance of the strong nuclear force binding atomic particles cannot be overstated. Yet, even with our current understanding, several enigmas persist within this realm. Cristiano Fanelli, an assistant professor of data science at William & Mary, has embarked on a transformative journey to shed light on these mysteries using the power of machine learning and artificial intelligence (AI).
The U.S. Department of Energy, recognizing the potential of AI and machine learning in accelerating experimental discovery in nuclear physics, has allocated a substantial $16 million in funding for 15 carefully selected projects in the AI/ML research domain for nuclear physics accelerators and detectors. Two of these groundbreaking projects are under Fanelli’s guidance.
One of Fanelli’s key roles is as the principal investigator for “A scalable and distributed AI-assisted detector design for the EIC” (Electron-Ion Collider). This cutting-edge project is poised to assist in the design of the ePIC detector, a crucial component of the $2 billion Electron-Ion Collider, set to commence operations in the next decade. This collider, touted as the highest-priority facility for nuclear physics in the United States, promises to redefine the frontiers of physics, catalyze technological innovations, and accelerate advancements in fields like nuclear medicine and national security.
Simultaneously, Fanelli contributes as a co-investigator in the “AI/ML Optimized Polarization” project, led by the Thomas Jefferson National Accelerator Facility. This venture focuses on the GlueX experiment, a scientific pursuit aimed at unraveling the mysteries of particle confinement within hadrons like protons and neutrons.
The strong nuclear force, governing the intricate dance of quarks and gluons within these hadrons, remains a perplexing enigma. Fanelli’s projects will undoubtedly play a pivotal role in exploring the internal structures and dynamics of these particles with unparalleled precision.
At the heart of these endeavors lies the construction of the Electron-Ion Collider at Brookhaven Lab in New York State, comprising two intersecting accelerators for polarized electrons and protons or ions. The electron beam, when directed at protons and neutrons, unveils the complex interplay between quarks and gluons, representing the mightiest force in the natural world. The high-resolution insights derived from these interactions will offer unprecedented depictions of the particles’ internal structures.
However, the pursuit of knowledge in nuclear physics comes at a substantial cost. The ePIC detector, with an estimated budget of around $300 million, necessitates meticulous design considering hundreds of multidimensional parameters, competing objectives, and numerous constraints. Fanelli’s project introduces an AI-assisted framework to optimize this detector’s design, marking a pioneering endeavor as the first large-scale experiment of its kind to harness the power of artificial intelligence. This collaborative effort spans over 170 institutions worldwide, including national laboratories like Brookhaven and Jefferson Lab, as well as prestigious higher education institutions.
The advent of AI heralds an era where complex design spaces can be explored in ways previously deemed unattainable. Fanelli underscores the critical importance of this evolution, as even minor improvements in design objectives translate into significantly more efficient utilization of beam time—a key cost factor in the lifetime of the Electron-Ion Collider.
But Fanelli doesn’t see AI as a replacement for human expertise; rather, it complements and enhances it. The benefits encompass simulations, control systems, data acquisition, analysis, and much more, promising a future where human intellect and AI coalesce to drive scientific advancements.
The AIOP project at the Jefferson Lab GlueX experiment is a prime example of this synergy. Here, a photon beam interacts with fixed nuclear targets, resulting in final state particles that shed light on the nature of confinement in quantum chromodynamics. Fanelli’s proposal introduces an AI/ML control strategy to enhance the beam’s polarization, employing techniques like deep reinforced learning to make real-time adjustments based on multifaceted inputs. This not only improves the experiment’s performance but also yields substantial cost savings—a crucial consideration given the expenses associated with delivering the beam.
Furthermore, AI and machine learning revolutionize the reconstruction of final state particles in experimental nuclear physics. Conventional algorithms struggle to cope with complex event topologies, often found in these experiments. Machine learning, with its adaptability and capacity to handle intricate scenarios, offers a powerful solution.
William & Mary, deeply committed to nurturing the potential of AI and machine learning, provides students with opportunities to engage in these transformative technologies. Events such as the “Artificial Intelligence for the Electron-Ion Collider” workshop and hackathons draw experts from national laboratories, universities, and industry, fostering collaborative efforts to harness AI’s potential. These initiatives not only enrich the academic experience but also pave the way for a new era of scientific discovery.
Fanelli’s mission extends beyond the laboratory. He plans to organize educational events to increase AI and machine learning literacy, reaching out to schools and higher education institutions. With a background in experimental nuclear physics and extensive experience in AI and machine learning, Fanelli embodies the multidisciplinary approach essential for advancing experimental nuclear physics and preparing the next generation for roles across various domains.
Conclusion:
The integration of AI and machine learning into nuclear physics research, as demonstrated by Cristiano Fanelli’s projects, signifies a significant leap forward. It not only promises to deepen our understanding of fundamental forces but also offers substantial cost savings and efficiency improvements. This infusion of advanced technology into a traditionally scientific field has the potential to revolutionize research methodologies and drive innovation, making it an exciting development for the market. Companies specializing in AI applications for scientific research may find a burgeoning niche in the nuclear physics sector, while educational institutions could witness a growing demand for AI literacy programs.
