Unraveling the Subatomic World: My PhD Journey and the Future of AI in Science

Ever wondered what makes up everything around us? Not just the things you can see—tables, trees, and people—but the very essence of matter itself? Scientists have spent centuries peeling back the layers of reality, and it turns out the world is built from a "particle zoo"—a diverse collection of tiny, energetic particles that form everything in the universe.

During my PhD, I set out to study one particular group of these particles, mesons, which hold the key to understanding some of the biggest mysteries in physics. But my work wasn't just about smashing particles together; it also involved cutting-edge AI, machine learning, and optimization techniques, skills that have applications far beyond the lab.

The Particle Zoo: Why Mesons Matter

Imagine you're at the beach, scooping up a handful of wet sand. If you look closely, you'll see tiny grains of different shapes and colors—some smooth, some jagged. These grains are like the fundamental building blocks of the universe, known as quarks.

But here’s the twist: quarks never exist alone. They always team up in groups to form larger particles. Some group into protons and neutrons, which make up the atoms in everything from your coffee mug to the Sun. Others form mesons, which are smaller, more short-lived particles found in high-energy environments like inside stars or particle colliders.

Why do we care about mesons? Because they hold secrets about the fundamental forces of nature. They're like messages left behind by the universe, giving us clues about how the invisible "glue" that holds everything together—called the strong nuclear force—actually works.

One of the biggest mysteries in physics is whether mesons can have an extra component—not just quarks, but also contributions from the force itself. That’s where my research comes in.

Seeing the Invisible: How We Study Mesons

Studying mesons isn't like studying a rock or a tree. You can’t hold one in your hand or even see it directly. Instead, scientists must reconstruct what happened when mesons were created, much like solving a crime scene with only a few scattered clues.

Imagine you throw a handful of pebbles into a calm lake, but you’re not allowed to see the pebbles themselves—only the ripples they leave behind. Some ripples are large, some small, some overlap, and some move in strange patterns. Your job? Figure out where the pebbles landed, how big they were, and what shape they had, just by analyzing the ripples.

This is exactly how we study mesons. Instead of pebbles, we use a beam of high-energy light (photons) to hit protons, creating mesons for a tiny fraction of a second before they decay into other particles. These secondary particles are what we actually detect. The problem is, just like the ripples in the water, different mesons can create similar patterns. How do we tell them apart?

This is where Partial Wave Analysis (PWA) comes in. PWA is like a forensic tool that allows us to mathematically reconstruct the original meson’s properties—its mass, energy, and behavior—just from the particles we observe. It takes all the “ripples” (data from our detectors) and reverse-engineers them to tell us what kind of meson was actually there.

Using PWA, we can find patterns in the chaos and identify mesons that may have never been seen before.

The Hunt for the π1: The Rebel of the Particle Zoo

Now, in any zoo, there's always that one animal that doesn’t quite fit in. Maybe it doesn’t follow the rules, looks a bit different, or behaves in a way nobody expects. In the particle zoo, that animal is the π1 meson.

According to what we currently understand about quarks, mesons should only exist in specific configurations. But π1 doesn’t play by the rules—it seems to have extra contributions from the strong nuclear force itself. This means it might be part of a special, exotic class of particles called hybrid mesons—something that has never been directly confirmed.

Finding the π1 is like discovering a new species of animal that challenges everything we thought we knew about evolution. If it exists the way we think it does, it will reshape how we understand the fundamental forces of nature.

But the π1 doesn’t show up easily. It decays in complex ways, meaning we must look at its footprints (other mesons it decays into) rather than seeing it directly. That’s why my research focused on studying another meson, b1(1235), because it might be the key to unlocking the mystery of the π1.

Beyond Physics: The Power of AI and Optimization

While my research was deeply rooted in physics, it also involved a heavy dose of data science, machine learning, and optimization.

One of the biggest challenges in experiments like this is dealing with an overwhelming amount of data. Our detectors record billions of events, most of which are just noise. Sifting through all this and identifying meaningful patterns is like searching for a single photograph in a massive library full of random images.

To solve this, I used AI-driven optimization techniques to refine how we analyze the data. Instead of manually tweaking detector settings or blindly testing configurations, I applied Bayesian Optimization and Evolutionary Algorithms to intelligently improve the way we extract signals from our experiments.

These same AI techniques are used in data science, finance, and healthcare for optimizing everything from logistics and supply chains to fraud detection and risk assessment.

Why This Matters for Industry

While my PhD focused on the fundamental building blocks of the universe, the skills I developed—data analysis, AI-driven optimization, machine learning, and statistical modeling—are highly valuable outside of physics.

Whether it’s predicting consumer behavior, optimizing financial models, or improving AI-driven decision-making, the ability to extract meaningful insights from complex data is one of the most in-demand skills today.

My journey from studying the tiniest particles in the universe to applying AI in real-world problems is a testament to how fundamental research drives innovation across industries.

Looking Ahead

Science isn’t just about answering questions—it’s about asking new ones. My next step? Bringing these skills into industry to solve big, data-driven challenges. Whether it’s working with AI-powered analytics, optimization models, or predictive algorithms, I’m excited to take my expertise beyond the lab and into the real world.

After all, whether you're studying particles in a collider or patterns in a dataset, the goal is the same: making sense of the unseen and uncovering hidden truths.

And that’s what makes this journey so exciting. 🚀