A more efficient simulation. During a supernova simulation, (left) shows the prediction by a current simulation method. (right) shows the prediction by 3D-MIM, which looks close enough to the that of the current leading method, but it takes far less time to execute, saving time, energy and costs for computing time. Image Credit: ©2023 Hirashima et al. (CC-BY-ND) |
Supernovae, exploding stars, play a critical role in the formation and evolution of galaxies. However, key aspects of them are notoriously difficult to simulate accurately in reasonably short amounts of time. For the first time, a team of researchers, including those from The University of Tokyo, apply deep learning to the problem of supernova simulation. Their approach can speed up the simulation of supernovae, and therefore of galaxy formation and evolution as well. These simulations include the evolution of the chemistry which led to life.
When you hear about deep learning, you might think of the latest app that sprung up this week to do something clever with images or generate humanlike text. Deep learning might be responsible for some behind-the-scenes aspects of such things, but it’s also used extensively in different fields of research. Recently, a team at a tech event called a hackathon applied deep learning to weather forecasting. It proved quite effective, and this got doctoral student Keiya Hirashima from the University of Tokyo’s Department of Astronomy thinking.