In spectroscopy, the Voigt profile (named after Woldemar Voigt) is a line profile resulting from the convolution of two broadening mechanisms, one of which alone would produce a Gaussian profile (usually, as a result of the Doppler broadening), and the other would produce a Lorentzian profile. Voigt profiles are common in many branches of spectroscopy and diffraction. Due to the computational expense of the convolution operation, the Voigt profile is often approximated using a pseudoVoigt profile.
All normalized line profiles can be considered to be probability distributions. The Gaussian profile has a Gaussian, or normal, distribution and a Lorentzian profile has a Lorentz, or Cauchy, distribution. Without loss of generality, we can consider only centered profiles, which peak at zero. The Voigt profile is then a convolution of a Lorentz profile and a Gaussian profile:
where x is the shift from the line center, is the centered Gaussian profile:
and is the centered Lorentzian profile:
The defining integral can be evaluated as:
where Re[w(z)] is the real part of the Faddeeva function evaluated for
(Centered) Voigt  

Probability density function
Plot of the centered Voigt profile for four cases. Each case has a full width at halfmaximum of very nearly 3.6. The black and red profiles are the limiting cases of the Gaussian (γ =0) and the Lorentzian (σ =0) profiles respectively. 

Cumulative distribution function


Parameters  
Support  
CDF  (complicated  see text) 
Mean  (not defined) 
Median  
Mode  
Variance  (not defined) 
Skewness  (not defined) 
Ex. kurtosis  (not defined) 
MGF  (not defined) 
CF 
The Voigt profile is normalized:
since it is a convolution of normalized profiles. The Lorentzian profile has no moments (other than the zeroth), and so the momentgenerating function for the Cauchy distribution is not defined. It follows that the Voigt profile will not have a momentgenerating function either, but the characteristic function for the Cauchy distribution is well defined, as is the characteristic function for the normal distribution. The characteristic function for the (centered) Voigt profile will then be the product of the two:
Since normal distributions and Cauchy distributions are stable distributions, they are each closed under convolution (up to change of scale), and it follows that the Voigt distributions are also closed under convolution.
Using the above definition for z , the cumulative distribution function (CDF) can be found as follows:
Substituting the definition of the Faddeeva function (scaled complex error function) yields for the indefinite integral:
which may be solved to yield
where is a hypergeometric function. In order for the function to approach zero as x approaches negative infinity (as the CDF must do), an integration constant of 1/2 must be added. This gives for the CDF of Voigt:
The full width at half maximum (FWHM) of the Voigt profile can be found from the widths of the associated Gaussian and Lorentzian widths. The FWHM of the Gaussian profile is
The FWHM of the Lorentzian profile is
Define Φ = . Then the FWHM of the Voigt profile () can be estimated as
where = 2.0056 and = 1.0593. This estimate has a standard deviation of error of about 2.4% for values of φ between 0 and 10. Note that the above equation is exactly correct in the limit of φ = 0 and φ = ∞, that is for pure Gaussian and Lorentzian profiles.
A rough approximation for the relation between the widths of the Voigt, Gaussian, and Lorentzian profiles is:
A better approximation with an accuracy of 0.02% is given by^{[1]}
This approximation is exactly correct for a pure Gaussian, but has an error of about 0.000305% for a pure Lorentzian profile.
If the Gaussian profile is centered at and the Lorentzian profile is centered at , the convolution is centered at and the characteristic function is
The mode and median are both located at .
The Voigt functions^{[2]} U, V, and H (sometimes called the line broadening function) are defined by
where
erfc is the complementary error function, and w(z) is the Faddeeva function.
with
and
The pseudoVoigt profile (or pseudoVoigt function) is an approximation of the Voigt profile V(x) using a linear combination of a Gaussian curve G(x) and a Lorentzian curve L(x) instead of their convolution.
The pseudoVoigt function is often used for calculations of experimental spectral line shapes.
The mathematical definition of the normalized pseudoVoigt profile is given by
There are several possible choices for the parameter.^{[3]}^{[4]}^{[5]}^{[6]} A simple formula, accurate to 1%, is^{[7]}^{[8]}
where
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