Linear inversion, singular value decomposition and regularization: Intuition derived from digital signal processing

2002 ◽  
Author(s):  
Heidi Anderson Kuzman ◽  
Allen Porter ◽  
Linbin Zhang ◽  
Youngki Choi ◽  
James Rector
Keyword(s):  
Signal Processing ◽  
Singular Value Decomposition ◽  
Digital Signal Processing ◽  
Digital Signal ◽  
Singular Value ◽  
Linear Inversion ◽  
Value Decomposition

scholarly journals Determination of Broadening Functions using the Singular Value Decomposition (SVD) Technique

1999 ◽  
Vol 170 ◽  
pp. 82-90 ◽  
Author(s):  
Slavek Rucinski
Keyword(s):  
Correlation Function ◽  
Singular Value Decomposition ◽  
Cross Correlation ◽  
Singular Value ◽  
Spectral Lines ◽  
Linear Inversion ◽  
Standard Tool ◽  
Contact Binaries ◽  
Value Decomposition

AbstractThe cross-correlation function (CCF) has become the standard tool for extraction of radial-velocity and broadening information from high resolution spectra. It permits integration of information which is common to many spectral lines into one function which is easy to calculate, visualize and interpret. However, the CCF is not the best tool for many applications where it should be replaced by the proper broadening function (BF). Typical applications requiring use of BFs rather than CCFs involve finding locations of star spots, studies of projected shapes of highly distorted stars such as contact binaries (as no assumptions can be made about BF symmetry or even continuity) and [Fe/H] metallicity determinations (good baselines and avoidance of negative lobes are essential). It is stressed that the CCFs are not broadening functions. This note concentrates on the advantages of determining BFs through the process of linear inversion, preferably accomplished using the singular value decomposition (SVD). Some basic examples of numerical operations are given in the IDL programming language.

Sign in / Sign up

Export Citation Format

Share Document