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Type:
Task
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Status: In Progress (View Workflow)
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Priority:
Normal
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Resolution: Unresolved
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Labels:None
Created on 2021-09-27 10:00:19 by Didier Vibert. % Done: 50
Since the fitting of the line width, w, is done by least-square minimization by looping on a grid of velocity dispersion, we can estimate the width of the peak in pdf(w) to infer var(w), and deduce var(v), where w^2 = sigma_LSF^2 + v ^2 .
Or we could use the analytical expression derived in https://www.osapublishing.org/abstract.cfm?URI=ao-46-22-5374 (eq 19)
var(w) = 2 * sigma^2 * w / ( sqrt(pi) * A^2 * dx )
where
- A is the Gaussian amplitude
- w is the Gaussian width (sigma)
- sigma is the noise standard deviation at the line position (assuming it is uncorrelated pixel 2 pixel, and with constant variance)
- dx is the pixel size (same units than w, typically wavelength in Angstroms)
Note: when/if we will use a non-linear-least square minimizer (eg Levenberg-Marquardt), we could use the estimated returned covariance matrix to get the variance on sigma