Dimensionless physical constant

Any plausible fundamental physical theory must be consistent with these six constants, and must either derive their values from the mathematics of the theory, or accept their values as empirical.

Any plausible fundamental physical theory must be consistent with these six constants, and must either derive their values from the mathematics of the theory, or accept their values as empirical.

== Relationships between dimensionless physics constants ==

== Relationships between dimensionless physical constants ==

Another method to obtain dimensionless parameters is to use a non-fundamental particle at around GeV scale. This provides a convenient way to derive relationships between parameters for artificial intelligence methods, without sacrificing generality, since dimensional analysis is not affected by this technique. In a paper <ref>S. V. Chekanov and H. Kjellerstrand, "Discovering the Underlying Analytic Structure within Standard Model Constants Using Artificial Intelligence", {{cite arXiv |last1=Chekanov |first1=Sergei | last2=Kjellerstrand | first2=Hakan |date=June 30, 2025 |title=Discovering the Underlying Analytic Structure within Standard Model Constants Using Artificial Intelligence |arxiv=2507.00225 |class=hep-ph}} </ref> it was proposed to search for relationships between Standard Model parameters using [[Symbolic_programming|symbolic regression]] and [[Genetic programming|genetic programming]]. The dimensionless parameters (masses) were obtained by re-scaling the masses using the rho-meson mass instead of the Planck-scale mass. The latter was found to be too large for effective use in [[Genetic programming|genetic programming]]. The obtained dimensionless relationships were made public with the goal of using this data for the analysis of patterns or structures that may reveal underlying relationships in high-dimensional functional space using [[Generative_artificial_intelligence|generative artificial intelligence]].

Another method to obtain dimensionless parameters is to use a non-fundamental particle at around GeV scale. This provides a convenient way to derive relationships between parameters for artificial intelligence methods, without sacrificing generality, since dimensional analysis is not affected by this technique. In a paper <ref>S. V. Chekanov and H. Kjellerstrand, "Discovering the Underlying Analytic Structure within Standard Model Constants Using Artificial Intelligence", {{cite arXiv |last1=Chekanov |first1=Sergei | last2=Kjellerstrand | first2=Hakan |date=June 30, 2025 |title=Discovering the Underlying Analytic Structure within Standard Model Constants Using Artificial Intelligence |arxiv=2507.00225 |class=hep-ph}} </ref> it was proposed to search for relationships between [[Standard Model]] parameters using [[Symbolic_programming|symbolic regression]] and [[Genetic programming|genetic programming]]. The dimensionless parameters (masses) were obtained by re-scaling the masses using the rho-meson mass instead of the Planck-scale mass. The latter was found to be too large for effective use in [[Genetic programming|genetic programming]]. The obtained dimensionless relationships were made public with the goal of using this data for the analysis of patterns or structures that may reveal underlying relationships in high-dimensional functional space using [[Generative_artificial_intelligence|generative artificial intelligence]].

== See also ==

== See also ==