If you click on the file link and download the file and then open it in a browser, it plots the error function vs. electron mass as the error goes from negative to positive as it passes through zero:
This is using the nominal NIST/CODATA values for the other constants in the polynomial:
Y-axis is error function polynomial, X-axis is the electron mass:
Axis labeling is crude, looking into plotly.js and d3.js or some other js library.
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Red dot is default NIST/CODATA electron mass value
(Plotly is much easier to read!) |
Looking at LOG error (log(abs(error)) plotting with fine steps allows one to visually see the root for both electron mass and electron charge (assuming all other constants are constant and correct):
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| Electron Mass |
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| Electron Charge |
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| h, Plankck's constant |
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| c, speed of light |
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| Rh, r_h, Rydberg constant |
r_p and alpha cannot be fine tuned until after the other constant are corrected enough to correct for the initial errors. Then, limited regions of stability around nominal values can be checked. Ongoing.
Added the Newton-Raphson method to the electron mass error vs. electron mass code and plotted both the NIST/CODATA value in RED/orange and my value in GREEN* from the polynomial to an error of less than 0.00000000001e-31 kg:
Some refinements are needed to tolerance as it converges in like 1-2 iterations due to linear nature of error of interval, thus it is simple to calculate the electron mass required to get a value of a specified tolerance (near zero).
The basic pieces of a Javascript approach for minimizing error and solving for the values of the constants that give minimum error have been developed... Time to implement some of the algorithms mentioned previosly in the blog, like the one to iterate to find the value of each constant to give minimum error and iterate through and check for convergence...
*GREEN MEANS GO!
