My conclusion is that use of the masked pixels is a good method to determine read noise if one keeps the exposures less than 1 second or so, avoiding excessive thermal noise that contaminates the read noise. The result is the same as the more laborious regression method plotting the variance of noise against the ADU numbers and performing linear regression.
Yes, excellent work as always Bill. There is something that doesn't sit right with me in your ImagePlus graph, though:
Isn't this graph in DN, and therefore so would be the square root of the y intercept (read noise) at 4.13 DN? Converting it to e- at a k of 3.46 e-/DN, the read noise would be 14.3 e-, which doesn't jibe with other data we have on the 'e'. On the other hand, if one takes the values in your table and concentrates in the .5-5% range (or 84-838DN) one minimizes any residual influence of PRNU and RN in the curve, getting a better overall fit (R^2=0.9999). These values are all referred to a 14 bit DN:
Gain (K) is 3.24 and read noise is 3.2e-, just short of 1 DN. These values appear to me to be more accurate. What do you think?
[EDIT] I've updated the table with a little more information. Notes:
1. Shot Noise is calculated subtracting the newly found Read Noise from the combined Shot+Read Noise in quadrature all the way down.
2. The PRNU column (above the Shot Noise Dominated Box, where Read noise is immaterial, 1 vs 60^2) calculates PRNU subtracting the combined Shot+Read noise from Total Noise in quadrature.
3. Below the Box (where PRNU is much smaller than Shot and Read Noise) Read Noise in DN is calculated by using fitted gain k to determine shot noise from the square root of the signal in DN times k, then subracting it from the combined Shot+Read noise in quadrature and re-converting it to DN. The average value for Read Noise so calculated is 1.08 DNs or 3.5 e-. We are still an order of magnitude away from SNR=1, RN is too small a component of Shot+Read, and Shot Noise and Read+Shot Noise are too close, so any small measurement variation results in quite a large variance of results with this method. PRNU also skews the estimates, even if marginally. I normally would not use these levels for this purpose, but it gives us an indication.
4. The gain column K is simply the ratio of Signal to Shot Noise in 1 squared.