Since the late 1980s particle physicists have used AI even as the concept of a neural network was barely in the public’s consciousness. AI and particle physics go hand in hand as the experiments the physicists perform usually revolves around seeking out patterns in the data from particle detectors and AI is excellent at pattern detection. Boaz Klima, a Physicists from the Fermi National Accelerator Laboratory, also called Fermilab, says “It took us several years to convince people that this is not just some magic, hocus-pocus, black box stuff.” He was amongst the first to adopt AI tools but today, it’s a part of standard particle physics practices.
Usually, particle physicists aim to comprehend the way the inner gears of the universe works, typically by colliding subatomic particles at hit speeds to break them down into even smaller and more unusual kinds of matter. For example the Higgs boson particle, discovered in 2012 by physicists using the Large Hadron Collider, the inscrutable particle is the key to a physics explanation of they way that fundamental particles gain their masses.
However, working with such fleeting particles is tricky and does not come with naturally occurring instructions. The Higgs boson only results in one out of one billion proton collisions, and it very quickly, in one-billionth of a picosecond (already one-trillionth of a second), decays into different particles like a couple of photons or four muons, a particle similar to an electron. In order to study the Higgs boson particles, they must be ‘reconstructed’ from that other particulate matter and determine if they go together consistent with the parent particle, a difficult task due to the extra particles generated in the same collision.
According to Pushpalatha Bhat, a Fermilab physicist, algorithms, and neural networks do a superb job of sifting pertinent data from background data. Inside particle detectors, normally a massive cylindrical structure full of a variety of sensors, a photon forms a shower or spray of particles in an electrometer calorimeter, a type of subsystem. And while the particles they produce are different, electrons and hadrons produce similar showers as well. To tell the difference, machine-learning algorithms are used to detect correlations found in the variables that make up the showers. For example, an algorithm is able to differentiate between Higgs boson related photon pairs and other random photons. “This is the proverbial needle-in-the-haystack problem,” Bhat says. “That’s why it’s so important to extract the most information we can from the data.”
Despite advancements in machine learning, however, physicists remain dependent on their comprehension of the underlying science in order to determine how to sort through results for evidence of new phenomena and particles. But AI is becoming more and more significant tells computer scientist Paolo Calafiura from Lawrence Berkeley National Laboratory. Already plans are in action to upgrade the Large Hadron Collider at CERN in order to speed up the collision rate to ten times its current speed in 2024. Then, Calafiura explains, machine learning will be essential if scientists want to keep up the massive amount of data produced from particle collisions.
Psychologist Martin Seligman and colleagues from the University of Pennsylvania’s Positive Psychology Center, have recognized the opportunity to utilize AI in the analysis of the massive amount of social data that can be collected from online social media. The World Well-Being Project combs through the hundreds of billions posts and tweets from billions of users using natural language processors and machine learning to track both physical and emotional health of the public.
Usually, this kind of data collection is achieved through surveys, but mining social media is “unobtrusive, it’s inexpensive, and the numbers you get are orders of magnitude greater,” explains Seligman.
