I am familiar with k-d trees being used to find nearest neighbors (NN) in 3-D euclidean space however in my particular case I am given a huge array of spherical coordinates. Due to accuracy complications as well as loss of information I cannot convert them to Cartesian coordinates. Then given these points in their spherical coordinates how can I go about determining their NN. I need an algorithm that is efficient (relatively) and more importantly accurate.
EDIT: For sake of argument and for the philosophical point let's accept the 'loss of information' to quickly summarize my research in astrophysics does not allow me to simply convert to Cartesian coordinates and use a nearest neighbor algorithm due to the universe expanding and me dealing with something called redshift. TLDR: I cannot convert to cartesian and must use an algorithm in spherical coordinates for nearest neighbor.