ROLE OF POTENTIAL STRUCTURE IN THE COLLISIONAL EXCITATION OF METASTABLE O((1)D) ATOMS

This paper considers the collisional excitation of O(1D) modeled by th e crossing of two valence 1 3PI(g) curves dissociating to O(3P) + O(3p ) [V11(R)] and O(3P) + O(1D) [V22(R)] which in turn are further crosse d by the C 3PI(g) Rydberg curve dissociating to O(3P) + O(5S) [V33(R)] . The role of structure in the potential curves and coupling matrix el ements is quantitatively probed by the first-order functional-sensitiv ity densities deltaInsigma12(E)/deltaInV(ij)(R) of the excitation cros s section sigma12(E) obtained from close-coupling calculations. The re sults reveal that, in spite of the well-separated nature of the crossi ng between the two valence curves from their crossings with the Rydber g potential curve, the excitation cross section sigma12 displays consi derable sensitivity to the Rydberg curve V33(R) at all energies in the range 3.0-9.0 eV. For relative collisional energies corresponding to the higher closely spaced vibrational energy levels of the Rydberg sta te, the excitation cross section is found to be much more sensitive to the Rydberg state than to the two valence states themselves. At all e nergies, the sensitivity of the excitation cross section sigma12 to th e coupling V12(R) between the valence states is much larger than the s ensitivity to the couplings V13(R) or V23(R) with the Rydberg state. A t higher energies, the large increase in the sensitivity of the cross section to the Rydberg potential is mirrored by a similar increase in sensitivity to its coupling V23(R) with the upper valence state. Due t o the weak coupling between the three curves, a qualitative similarity exists between the sensitivity profiles and those predicted by the La ndau-Zener-Stueckelberg (LZS) theory. Quantitative departures witnesse d in earlier work are, however, more pronounced for the multilevel cur ve crossings investigated here. Implications of the results for attemp ts to extend the LZS-type treatment to multilevel curve crossings and for functional-sensitivity-based algorithms for the inversion of cross -section data are discussed.