WIM no. 17 Spring 2020

מכון ויצמן למדע the behavior of subatomic particles at very high energies—is a great place to start. Weizmann Institute scientists are prominent leaders in ATLAS, a detector that is part of CERN’s Large Hadron Collider (LHC)—the world’s most powerful particle accelerator. At the LHC, thousands of magnets speed up sub-atomic particles so they collide at close to the speed of light. Over a billion such collisions occur in the ATLAS detector every second, generating vast quantities of data analyzed at 130 computing centers around the globe. Complex data produced by particle physics experiments strains the data storage capacity of the world’s strongest computers. AI can help, by generating real-time results from detected events. AI architectures might also be trained to identify and save events that don’t match expectations— rather than rejecting them—something that might alert scientists to phenomena that hold the key to the next major breakthrough. Prof. Eilam Gross is a member of the Department of Particle Physics and Astrophysics who has devoted much of his scientific career to the ATLAS project. Prof. Gross was the overall team coordinator for the international group of scientists responsible for the LHC’s most celebrated accomplishment—the discovery of the Higgs boson. He is now hard at work on a new challenge: helping to design algorithms that will improve monitoring and data analysis in the coming upgrade of the ATLAS experiment. These algorithms are based on deep learning—a machine learning method based on artificial neural networks—and will enable faster and more efficient data analysis. This will make it possible to characterize rare sub-atomic events that have been neglected because of the enormous density—even by ATLAS standards!— of the data involved. A world-renowned expert on the meeting point between AI and particle physics, Prof. Gross moved the field forward through a collaboration with Prof. Yaron Lipman from the Department of Computer Science and Applied Mathematics. Together, the scientists developed a novel method using geometric deep learning to improve detector performance by “tagging” particles of interest. In another project, Prof. Gross used a type of machine learning called convolutional neural networks to predict how much of a certain type of energy would be “deposited” in components of the ATLAS detector. This advance makes it easier to separate inconsequential background noise from significant experimental findings. He is also using machine learning protocols to identify malfunctions in the detector itself. Rapid AI progress has Prof. Gross and his colleagues dreaming of what could become possible in the near future. Rather than using machine learning to find patterns in high-density data in order to answer existing questions, tomorrow’s AI platforms may be able to ask their own questions independently, and even run the experiments. And if tomorrow’s AI systems ask their own questions, will they experience a “Eureka moment” in their circuits when they discover the answer? Only time will tell. g Prof. Yaron Lipman g Prof. Eilam Gross Will tomorrow’s AI systems ask their own questions, and experience a “Eureka moment” in their circuits when they discover the answer? Only time will tell. Weizmann MAGAZINE 48–49 S P R I N G 2 0 2 0