Difference: 20151118_MSciSpectrocopyMeeting ( vs. 1)

Revision 119 Nov 2015 - JosephBayley

Line: 1 to 1
META TOPICPARENT name="StewartBoogertSpectroscopy2015"
  • Mapping wavelength\grating\position on camera
    • add term to fit function so $f(m,\mu) = \lambda_{0}+a_{1}m+b_{1}\mu+ c_{1}m\mu... $
    • plot histogram of residuals instead of 3d plot, where $\rm{Residual} = \frac{\rm{Value of point}-\rm{fitted point}}{\rm{errorbar}}$
    • histogram should follow Gaussian with $\mu = 0$, $\sigma = 1$, is if $\mu > 0$ all point lie above fit plane etc
    • with $\chi^2$, write down $N_{\rm{DOF}}$ as well so can get $\chi^2$ per degrees of freedom.
    • do a linear fit first, then add terms and see how $\chi^2$ varies.
    • could also plot data points for each grating position with fit function
    • put all lamps onto same plot
  • Scan of peak across CCD
    • Have the scan plot as parametric function
    • then can see intrumental response
    • similtaneous fit with r of circle fit
    • fit with parabola
  • Errors
    • Take lots of 0.5s frames and look at the spread of the data
    • normalise to the same area under curves, then see how individual point varies
    • to normalise, divide each point by bin width and the total number of points in the distribution
    • see how the $\sigma_{\sigma}$ varies with exposure time
    • derive some error bar from this and compare it to the poisson error found before
    • may follow students-t distribution not Gaussian
  • Fit to line
    • for background fit quadratic that is not to large so affects Gaussian
    • add some skew to the Gaussian
    • see if the skew is the same on both sides of the chip
    • look up how to skew the Gaussian
    • odd/even functions in the $(x-\mu)^2 term$
    • could use Gaussian as a function of $y$ where:
      • if $y = x$ then its a normal Gaussian
      • if $y = ax^2+bx+c ....$ or other function then can add skew
  • Look at fourier transform of the ccd pixel readout

-- JosephBayley - 19 Nov 2015

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