Obtaining interesting celestial objects from tens of thousands or even millions of recorded optical-ultraviolet spectra depends not only on the data quality but also on the accuracy of spectra decomposition. Additionally rapidly growing data volumes demands higher computing power and/or more efficient algorithms implementations. In this paper we speed up the process of substracting iron transitions and fitting Gaussian functions to emission peaks utilising C++ and OpenCL methods together with the NOSQL database. In this paper we implemented typical astronomical methods of detecting peaks in comparison to our previous hybrid methods implemented with CUDA.

Date Published: 11 September 2015
PDF: 6 pages
Proc. SPIE 9662, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015, 966218 (11 September 2015); doi: 10.1117/12.2205888

Published in SPIE Proceedings Vol. 9662:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015
Ryszard S. Romaniuk, Editor(s)