Inverting experimental data provides a powerful technique for obtaining information about molecular Hamiltonians. However, rigorously quantifying how laboratory error propagates through the inversion algorithm has always presented a challenge. In this paper, we develop an inversion algorithm that realistically treats experimental error. It propagates the distribution of observed laboratory measurements into a family of Hamiltonians that are statistically consistent with the distribution of the data. This algorithm is built upon the formalism of map-facilitated inversion to alleviate computational expense and permit the use of powerful nonlinear optimization algorithms. Its capabilities are demonstrated by identifying inversion families for the [Formula Presented] and [Formula Presented] states of [Formula Presented] that are consistent with the laboratory data.
|Original language||English (US)|
|Number of pages||1|
|Journal||Physical Review A - Atomic, Molecular, and Optical Physics|
|State||Published - Jan 1 2003|
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics