Ben Nachman – a staff scientist in Berkeley Lab’s Physics Division – is quoted in a new Symmetry Magazine article about how particle physicists are using AI to build innovative machine learning algorithms that enhance Monte Carlo simulations. Physicists can now improve the speed and accuracy of particle detector simulations by replacing them with machine-learning models that can be trained on data from real detector experiments, or even previous simulations.
Originally developed nearly a century ago by physicists studying neutron diffusion, Monte Carlo simulations are mathematical models that use random numbers to simulate different kinds of events. Much like the universe itself, Monte Carlo simulations are governed by randomness and chance, and this makes them well-suited to modeling natural systems. “A Monte Carlo simulation is basically our way of simulating nature,” says Nachman. Monte Carlo simulations are used to design and analyze new experiments, plan the construction of equipment, and predict how that equipment will perform.
One reason Monte Carlo simulations have become so useful is that they’re now much more accurate than they were in the past. According to Nachman, “The simulations are so good now, that if you have a full simulation event of, say, a collision at the Large Hadron Collider, and you show [the data] to an expert…most people wouldn’t be able to tell you which one’s real or which one’s fake,” Nachman says. “The Higgs boson would not have been discovered [when it was], probably, without that level of precise simulation that we have available.”
Read the full article:
Will AI make MC the MVP of particle physics?
July 18, 2023 / R.M. Davis / Symmetry Magazine