High-Energy Theoretical Physics seeks to understand the fundamental laws governing matter, energy, and interactions in the universe. Using mathematical models and advanced computational tools, researchers at SAPHIR explore everything from Standard Model particles to possible new forms of matter and physics beyond the known, addressing key questions such as the origin of mass, the nature of dark matter, the properties of neutrinos, and the existence of new particles and interactions. The results of these studies directly guide the design and analysis of international experiments at CERN and other cutting-edge laboratories.
Modern particle physics relies on advanced computing systems capable of processing the enormous volumes of data generated by large-scale experiments. In this regard, SAPHIR develops solutions in computational architecture, scientific software, distributed systems, artificial intelligence, and data management, while also participating in international scientific computing networks and promoting the development of local infrastructure for data analysis, simulations, and distributed processing, thereby contributing to the training of specialists in areas critical to research and technological innovation.
The development of advanced scientific instrumentation is one of the cornerstones of SAPHIR. This line of work focuses on the design, construction, integration, and validation of detectors capable of detecting subatomic particles in accelerators and high-energy natural phenomena. The institute’s teams actively participate in international projects such as ATLAS, NA64, SND@LHC and SHiP at CERN, contributing electronic systems, monitoring, cryogenics, precision mechanics, sensors, and specialized structures, thereby strengthening technology transfer and the development of highly specialized human capital in Chile.
Particle physics experiments generate some of the largest and most complex datasets in the world. This line of research uses advanced tools in statistical analysis, machine learning, artificial intelligence, and data mining to extract relevant scientific information from enormous volumes of data, employing methodologies that extend beyond particle physics and are applied in fields such as medicine, industry, engineering, and other disciplines where big data analysis is essential.
