In 1930, a young physicist named Carl D. Anderson was tasked by his mentor with measuring the energies of cosmic rays—particles arriving at high speed from outer space. Anderson built an improved version of a cloud chamber, a device that visually records the trajectories of particles. In 1932, he saw evidence that confusingly combined the properties of protons and electrons. “A situation began to develop that had its awkward aspects,” he wrote many years after winning a Nobel Prize at the age of 31. Anderson had accidentally discovered antimatter.
Four years after his first discovery, he codiscovered another elementary particle, the muon. This one prompted one physicist to ask, “Who ordered that?”
Carl Anderson [top] sits beside the magnet cloud chamber he used to discover the positron. His cloud-chamber photograph [bottom] from 1932 shows the curved track of a positron, the first known antimatter particle. Caltech Archives & Special Collections
Over the decades since then, particle physicists have built increasingly sophisticated instruments of exploration. At the apex of these physics-finding machines sits the Large Hadron Collider, which in 2022 started its third operational run. This underground ring, 27 kilometers in circumference and straddling the border between France and Switzerland, was built to slam subatomic particles together at near light speed and test deep theories of the universe. Physicists from around the world turn to the LHC, hoping to find something new. They’re not sure what, but they hope to find it.
It’s the latest manifestation of a rich tradition. Throughout the history of science, new instruments have prompted hunts for the unexpected. Galileo Galilei built telescopes and found Jupiter’s moons. Antonie van Leeuwenhoek built microscopes and noticed “animalcules, very prettily a-moving.” And still today, people peer through lenses and pore through data in search of patterns they hadn’t hypothesized. Nature’s secrets don’t always come with spoilers, and so we gaze into the unknown, ready for anything.
But novel, fundamental aspects of the universe are growing less forthcoming. In a sense, we’ve plucked the lowest-hanging fruit. We know to a good approximation what the building blocks of matter are. The Standard Model of particle physics, which describes the currently known elementary particles, has been in place since the 1970s. Nature can still surprise us, but it typically requires larger or finer instruments, more detailed or expansive data, and faster or more flexible analysis tools. Those analysis tools include a form of artificial intelligence (AI) called machine learning. Researchers train complex statistical models to find patterns in their data, patterns too subtle for human eyes to see, or too rare for a single human to encounter. At the LHC, which smashes together protons to create immense bursts of energy that decay into other short-lived particles of matter, a theorist might predict some new particle or interaction and describe what its signature would look like in the LHC data, often using a simulation to create synthetic data. Experimentalists would then collect petabytes of measurements and run a machine learning algorithm that compares them with the simulated data, looking for a match. Usually, they come up empty. But maybe new algorithms can peer into corners they haven’t considered. A New Path for Particle Physics “You’ve heard probably that there’s a crisis in particle physics,” says Tilman Plehn, a theoretical physicist at Heidelberg University, in Germany. At the LHC and other high-energy physics facilities around the world, the experimental results have failed to yield insights on new physics. “We have a lot of unhappy theorists who thought that their model would have been discovered, and it wasn’t,” Plehn says.
Tilman Plehn “We have a lot of unhappy theorists who thought that their model would have been discovered, and it wasn’t.”
Gregor Kasieczka, a physicist at the University of Hamburg, in Germany, recalls the field’s enthusiasm when the LHC began running in 2008. Back then, he was a young graduate student and expected to see signs of supersymmetry, a theory predicting heavier versions of the known matter particles. The presumption was that “we turn on the LHC, and supersymmetry will jump in your face, and we’ll discover it in the first year or so,” he tells me. Eighteen years later, supersymmetry remains in the theoretical realm. “I think this level of exuberant optimism has somewhat gone.”
The result, Plehn says, is that models for all kinds of things have fallen in the face of data. “And I think we’re going on a different path now.” That path involves a kind of machine learning called unsupervised learning. In unsupervised learning, you don’t teach the AI to recognize your specific prediction—signs of a particle with this mass and this charge. Instead, you might teach it to find anything out of the ordinary, anything interesting—which could indicate brand new physics. It’s the equivalent of looking with fresh eyes at a starry sky or a slide of pond scum. The problem is, how do you automate the search for something “interesting”? Going Beyond the Standard Model The Standard Model leaves many questions unanswered. Why do matter particles have the masses they do? Why do neutrinos have mass at all? Where is the particle for transmitting gravity, to match those for the other forces? Why do we see more matter than antimatter? Are there extra dimensions? What is dark matter—the invisible stuff that makes up most of the universe’s matter and that we assume to exist because of its gravitational effect on galaxies? Answering any of these questions could open the door to new physics, or fundamental discoveries beyond the Standard Model.
The Large Hadron Collider at CERN accelerates protons to near light speed before smashing them together in hopes of discovering “new physics.” CERN
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