From S -matrix theory to strings

Scattering data and the commitment to non-arbitrariness

Bibliographic Data

ID10798456
AuthorsRobert Van Leeuwen (0000-0002-3346-9919)
Year2024
Volume104
Pages130-149
Publication date2024-04-01
Open AccessYes
TypeARTICLE
VenueStudies in History and Philosophy of Science (JOURNAL)
Journal identifiersISSN: 0039-3681 • E-ISSN: 1879-2510
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.shpsa.2023.12.011
PMID38518509
OpenAlexW4392736258
LanguageEN
References cited66

The early history of string theory is marked by a shift from strong interaction physics to quantum gravity. The first string models and associated theoretical framework were formulated in the late 1960s and early 1970s in the context of the S-matrix program for the strong interactions. In the mid-1970s, the models were reinterpreted as a potential theory unifying the four fundamental forces. This paper provides a historical analysis of how string theory was developed out of S-matrix physics, aiming to clarify how modern string theory, as a theory detached from experimental data, grew out of an S-matrix program that was strongly dependent upon observable quantities. Surprisingly, the theoretical practice of physicists already turned away from experiment before string theory was recast as a potential unified quantum gravity theory. With the formulation of dual resonance models (the "hadronic string theory"), physicists were able to determine almost all of the models' parameters on the basis of theoretical reasoning. It was this commitment to "non-arbitrariness", i.e., a lack of free parameters in the theory, that initially drove string theorists away from experimental input, and not the practical inaccessibility of experimental data in the context of quantum gravity physics. This is an important observation when assessing the role of experimental data in string theory

Arbitrariness · Epistemology · Mathematical economics · Matrix (chemical analysis · Physics · Quantum mechanics · Scattering · Theoretical physics · Computational Physics and Python Applications · Graph Theory and Algorithms · Materials Science · Mathematics · Philosophy

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