Rate Constants for Finestructure Excitations in OH Collisions with Error Bars Obtained by Machine Learning
Abstract
We present an approach using a combination of coupled channel scattering calculations with a machinelearning technique based on Gaussian Process regression to determine the sensitivity of the rate constants for nonadiabatic transitions in inelastic atomic collisions to variations of the underlying adiabatic interaction potentials. Using this approach, we improve the previous computations of the rate constants for the finestructure transitions in collisions of O({}^{3}{P}_{j}) with atomic H. We compute the error bars of the rate constants corresponding to 20% variations of the ab initio potentials and show that this method can be used to determine which of the individual adiabatic potentials are more or less important for the outcome of different finestructure changing collisions.
 Publication:

The Astrophysical Journal
 Pub Date:
 February 2017
 DOI:
 10.3847/15384357/835/2/255
 arXiv:
 arXiv:1701.01897
 Bibcode:
 2017ApJ...835..255V
 Keywords:

 ISM: atoms;
 Astrophysics  Astrophysics of Galaxies;
 Physics  Chemical Physics
 EPrint:
 10 pages, 5 figures, accepted for publication in ApJ