1. Random-Matrix Approach to RPA Equations X. Barillier-Pertuisel, IPN, Orsay O. Bohigas, LPTMS, Orsay H. A. Weidenmüller, MPI für Kernphysik, Heidelberg Workshop on “Random-Matrix Theory and Applications: From Number Theory to Mesoscopic Physics”, June 25 – 27, Orsay.

2. Contents: 1. Motivation 2. RPA Equations 3. Random-Matrix Approach 4. Pastur Equation 5. Solutions. Numerical Results 6. Critical Strength 7. Summary

3. 1. Motivation Random-phase approximation (RPA) is a standard tool of many-body physics. First used in condensed-matter physics but later also in other areas and especially in nuclear-structure physics. Study RPA equations in most general form by taking matrix elements as random. Yields unitarily invariant random-matrix model. Study spectrum using (generalized) Pastur equation as a function of coupling between states at positive and at negative energies. When does the spectrum become instable (i.e., when do eigenvalues become complex)? Distribution of eigenvalues in complex energy plane?

4. 2. RPA Equations N-dimensional space for particle-hole pairs. RPA equations have dimension 2N : Matrix A is Hermitean. Matrix C is symmetric. Total matrix not Hermitean. Eigenvalues not necessarily real. Non-real eigenvalues: Instability. If is eigenvalue then also (- * ) is an eigenvalue. Two symmetries:

5. If is an eigenvalue then also * is an eigenvalue. Result: Real and purely imaginary eigenvalues come in pairs with opposite signs. Complex eigenvalues with non-vanishing real and imaginary parts come in quartets arranged symmetrically with respect to the real and to the imaginary energy axis.

6. Hermitean matrix A 0 causes repulsion amongst positive eigenvalues, matrix – (A 0 ) * causes repulsion amongst negative eigenvalues. For C = 0 all eigenvalues real. Role of C: Eliminate negative-energy subspace and obtain Hermitean effective operator - C (E + (A (0)* ) -1 C * in positive-energy subspace. Causes additional level repulsion amongst positive and amongst negative eigenvalues and level attraction between positive and negative eigenvalues (Trace of that operator is negative). With increasing strength of C, pairs of real eigenvalues with opposite signs coalesce at E = 0 and then move along the imaginary axis in opposite directions. Instability of the RPA equations.

7. 3. Random-Matrix Approach Degenerate particle-hole energies at r, A 0 = 1 N r + A where matrix A contains particle-hole interaction matrix elements, belongs to GUE with and is invariant under A → U A (U * ) T. Matrix C obeys C is invariant under C → U C U T. The RPA matrix becomes

8. Generalized unitary invariance: Ensemble is invariant under The matrices are not Hermitian. Ensemble does not belong to ten generic random-matrix ensembles listed by Altland and Zirnbauer. We have also looked at the orthogonal case. Aim: To study average spectrum for N → 1. Model depends on two dimensionless parameters:  =  2 / 2 measures relative strength of C versus A. For  = 0, spectrum consists of two semicircles. And x = r / (2  gives distance of centers of semicircles from origin at E = 0. Spectral fluctuations not studied; are very likely to be same as for GUE in each branch. Two limiting cases: C = 0 and A = 0. Find value of  for which RPA equations become instable.

9. Distribution of eigenvalues in the complex energy plane N = 20, x = 4

10. 4. Pastur Equation Do this by calculating Imaginary part of average Green’s function yields level density. Expand in powers of H. where Average each term in sum separately. For N large, keep only nested contributions. Yields Pastur equation

11. Define for i = 1,2 the spectral density of subspace i in total spectrum (projection operators Q i onto the two subspaces) and take trace of Pastur equation. Yields two coupled equations for  1 and  2,  1 = / (E – r - (  1 -   2 )),  2 = / (E + r - (  2 -   1 )). For  = 0 these are two separate equations. Imaginary parts of solutions yield two semicircles of radius 2 centered at r and at – r. Two dimensionless variables  =  2 / 2, x = r / (2 ). Study spectrum as function of  for fixed x.

12. 5. Solutions. Numerical Results To gain understanding of solutions, consider first  = 0. With  i = (E – (-) i r) / (2 ), spectrum given by Im(  i ) = {1 -  2 i } 1/2. Usual semicircle law. Two branch points at  = § 1. Each  i defined on Riemann surface with two sheets. For   0, solution defined on Riemann surface with four sheets. But which sheet to choose for physically relevant solution? Take  very small, use perturbation theory, find that we need pair (  1,  2 ) of solutions for which imaginary parts have opposite signs and Im(  1 ) > 0. Then total level density  (E) given by  (E) = (N / (  )) Im (  1 +  2 ).

13. Eliminate  2 and obtain fourth-order equation for  1. Then  2 obtained from solution  1 via linear equation. Pairs of solutions: ● Four pairs of complex solutions: Domain (I) ● Two pairs of real and two pairs of complex solutions with equal signs for Im(  1 ) and Im(  2 ): Domain (II) ● Two pairs of real and two pairs of complex solutions with opposite signs for Im(  1) and Im(  2): Domain (III) ● Four pairs of real solutions: Domain (IV) Physically interesting solutions only in domains (I) and (III).

14. Case (a): x = 1.2 Case (b): x = 2.0 Case (c): x = 4.0

15. N = 50, 100 realizations Comparison of results for Pastur equation with those of matrix diagonalization.

16. Evolution of two spectra with increasing . Upper panels: x = 1.2 Lower panels: x = 4

17. Normalization integral of total level density taken over real energies versus . For  = 0, integral is normalized to unity. X = 1.2

18. 6. Critical Strength Find smallest value of  for which average spectra touch: Imaginary parts of a pair of physically acceptable solutions (  1,  2 ) have non-vanishing values at E = 0. Determine  crit analytically from solutions of fourth-order equation for  1 at E = 0. Find  crit = x 2 – 1.

19. 7. Summary Random-Matrix model for RPA equations with “Generalized unitary invariance”. Level repulsion between levels of same sign, level attraction between levels with opposite sign. Latter causes coalescence of pairs of eigenvalues with opposite signs at E = 0 and instability of RPA equations. Use Pastur equation to derive two coupled equations for  1,  2. Surprisingly simple structure. Criterion for physically relevant solutions. Get average level density from (  1,  2 ). Two parameters: x and . With increasing , semicircles are deformed and move toward each other. RPA instability for average spectrum: The deformed spectra touch. Critical strength  cri t = x 2 – 1.