A particle that took 26 years to detect is now teaching machines how to see. At Fermilab, machine learning is recovering neutrino interactions that conventional methods missed, turning faint traces into increasingly detailed pictures.
But reading the invisible raises another question: could the same computational intelligence help investigate how tiny physical interactions might be converted into usable energy?
Between detection, material science and experimental engineering lies a frontier where mathematics proposes, machines explore and measurements decide.
