Marcus Spradlin, a physicist at Brown University, is exploring the emerging field of surfaceology, which he describes as offering a natural framework for assembling large numbers of Feynman diagrams, resulting in an exponential compactification of information. Carolina Figueiredo, a graduate student at Princeton University, observed a peculiar similarity in the behavior of three seemingly unrelated species of quantum particles.
Unlike the amplituhedron, which necessitated exotic particles to achieve supersymmetry, surfaceology applies to more realistic, non-supersymmetric particles. Spradlin highlights that surfaceology is entirely indifferent to supersymmetry, which he and others find surprising.
The current inquiry among theoreticians is whether this primitive geometric approach will allow them to surpass the traditional boundaries of space and time. Jacob Bourjaily from Pennsylvania State University recognizes the potential of this approach, suggesting it might offer a new way to rethink space-time, though its implications remain uncertain.
The challenge of predicting outcomes when quantum particles collide has been a perennial issue for physicists. This was initially tackled in the late 1940s by Julian Schwinger, Sin-Itiro Tomonaga, and Richard Feynman, whose solutions for electrically charged particles won them a Nobel Prize, with Feynman’s visual approach becoming particularly dominant.
When quantum particles interact, their outcomes can vary widely, from merging to splitting or other complex sequences. Feynman diagrams help track these possibilities by representing particles’ paths through space-time and calculating a number known as an “amplitude,” indicative of the likelihood of various events. Adding up numerous amplitudes is believed to form the basis of all matter and interactions.
However, these amplitudes present a persistent challenge. Decades of quantum physicists, tracing back to Feynman and Schwinger, have faced the enigma of spending extensive efforts detailing particle trajectories and complex formulas, only to arrive at unexpectedly simple outcomes. This perplexity underscores the consistent simplicity found in nature’s predictions, despite the intricate processes involved in calculating them, a sentiment echoed by Nima Arkani-Hamed from the Institute for Advanced Study.
