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Model building · research workbench II

SU(N) tensor products

Decompose compact SU(N) representations with exact Littlewood–Richardson coefficients and explicit Young-diagram conventions.

Scope: Finite-dimensional continuous complex SU(N) representations; 2≤N≤6. Each Dynkin label 0–12; at most 16 canonical Young boxes across the two inputs. Complete decomposition within the supported domain.

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V(0,1,0,0)⊗V(0,1,0,0)=V(0,0,0,1)⊕V(1,0,1,0)⊕V(0,2,0,0)\begin{aligned}V_{(0,1,0,0)}\otimes V_{(0,1,0,0)}&=V_{(0,0,0,1)}\oplus V_{(1,0,1,0)}\\&\quad{}\oplus V_{(0,2,0,0)}\end{aligned}
Input dimensions
10×1010\times 10
Product dimension
100100
Singlet multiplicity
00
Combined Young boxes
4
Input 1 · (1,1)(1,1)
Input 2 · (1,1)(1,1)
Complete irreducible decomposition
Dynkin labelsMultiplicityComplex dimensionConjugate labelsN-ality
(0,0,0,1)(0,0,0,1)1155(1,0,0,0)(1,0,0,0)4(mod5)4\pmod{5}
(1,0,1,0)(1,0,1,0)114545(0,1,0,1)(0,1,0,1)4(mod5)4\pmod{5}
(0,2,0,0)(0,2,0,0)115050(0,0,2,0)(0,0,2,0)4(mod5)4\pmod{5}

Dynkin labels identify each summand, including inequivalent representations of the same dimension. This is the full tensor product; exchange-symmetry projections and Clebsch–Gordan tensors require additional data.

Young shapes and determinant columns
Determinant-column reduction
Highest weightGL(N) partitionRemoved full columnsSU(N) partition
(0,0,0,1)(0,0,0,1)(1,1,1,1,0)(1,1,1,1,0)0(1,1,1,1,0)(1,1,1,1,0)
(1,0,1,0)(1,0,1,0)(2,1,1,0,0)(2,1,1,0,0)0(2,1,1,0,0)(2,1,1,0,0)
(0,2,0,0)(0,2,0,0)(2,2,0,0,0)(2,2,0,0,0)0(2,2,0,0,0)(2,2,0,0,0)

One exact polynomial-character specialization; not a separate full-character proof.

✓ nonnegative integer multiplicities✓ dimension conserved✓ center charge conserved✓ schur specialization✓ singlet duality

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Method & interpretation

Littlewood–Richardson coefficients

Highest weights become canonical Young diagrams. Exact skew-tableau multiplicities give the Schur product; determinant columns are then removed for SU(N). The result is checked by dimension, center charge, and an exact character specialization.

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Sources, conventions, and versions

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Convention: su-n-young-lr-v1
Algorithm: su-n-tensors-1.0.0

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