Universal critical coupling constants for the three-dimensional n-vector model from field theory
A. I. Sokolov, E. V. Orlov, V. A. Ul'kov, and S. S. Kashtanov (Saint, Petersburg Electrotechnical University, St. Petersburg, Russia)

TL;DR
This paper uses field theory and renormalization group techniques to estimate universal critical coupling constants g_6 and g_8 for the three-dimensional O(n) model, providing highly accurate numerical values for a wide range of n.
Contribution
It presents new high-precision estimates of critical coupling constants g_6 and g_8 for the 3D O(n) model using advanced resummation of RG series, extending results to n=40.
Findings
Accurate estimates of g_6^*(n) for n=1 to 40 with less than 0.3% error.
Consistent estimates of g_8^* with RG equations and epsilon-expansion for n > 8.
Demonstrates the effectiveness of Pade-Borel-Leroy resummation in critical coupling calculations.
Abstract
The field-theoretical renormalization group approach in three dimensions is used to estimate the universal critical values of renormalized coupling constants g_6 and g_8 for the O(n)-symmetric model. The RG series for g_6 and g_8 are calculated in the four-loop and three-loop approximations respectively and then resummed by means of the Pade-Borel-Leroy technique. Under the optimal value of the shift parameter b providing the fastest convergence of the iteration procedure numerical estimates for the universal critical values g_6^*(n) are obtained for n = 1, 2, 3,...40 with the accuracy no worse than 0.3%. The RG expansion for g_8 demonstrates stronger divergence and results in considerably cruder numerical estimates. They are found to be consistent with the values of g_8^* deduced from the exact RG equations and, for n > 8, with those given by a constrained analysis of corresponding…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
