Astrophysics (Index)About

convolution

(type of product function of two functions)

A convolution is a type of product function of two functions defined as:

(f*g)(t) means ∫ f(x)g(t-x) dx

   (for x from -∞ to ∞)

f*g - the convolution of functions f and g.

One way to explain it is that it is the amount of overlap when one function is shifted over another.

Convolutions are the means of producing density functions that results from combination of independent probability density functions, as well as density functions that describe the results of physical processes, such as those producing spectral line shapes. The Voigt function is a convolution of two functions (for two processes) affecting spectral line shape (the Voigt profile). Convolution appears to be basic to some optical applications and gives a mathematical model for effects of blurring, e.g., from aberration, and also the effect of detectors on images. It models the effect of a function representing the intensity of incoming light with another function representing the distortion, and the mathematics of convolutions spells out the possibility and means of reversing the distortion. It also yields the result of some particular spectral energy distribution (SED) sensed with some particular sensitivity function.

In observed processes or observation techniques involving implicit convolution, a step in analysis may carrying out the inverse mathematical process, deconvolution. Other than "take a shot at it" guesses, there are mathematical methods to assist the process, which is one use of the Fourier transform. In addition to spectrography, deconvolution is used in parsing of observation data in interferometry and aperture synthesis.


(mathematics)
Further reading:
https://en.wikipedia.org/wiki/Convolution
https://en.wikipedia.org/wiki/Deconvolution
https://www.mathworks.com/discovery/convolution.html
http://www.rodenburg.org/theory/convolution_integral_22.html
http://www.ifa.hawaii.edu/users/jpw/classes/alma/lectures/imaging.pdf
https://ui.adsabs.harvard.edu/abs/2012A%26A...539A.133P/abstract

Referenced by pages:
CLEAN
collisional broadening
DSA-2000
Gamow peak
Jeans anisotropic modeling (JAM)
line broadening
line shape function
mixture
sensitivity function (S)
Voigt profile

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