| | 1 | == 2013-04-22 == |
| | 2 | |
| | 3 | === Armin's questions/points === |
| | 4 | |
| | 5 | * Q1: "One good test to see if everything is propagated correctly is to check the noise" |
| | 6 | * A1: I chose six horizontal stripes across the image, and calculated the image noise and variance median for each 100-pixel tall sample (partially because I have that code done and easy to use). To remove the effects of stars (which appear as horizontal excursions in the following plot), I used the median absolute deviation as the image noise statistic, converted in the plot to a Gaussian sigma. The variance image was measured using a simple median. No covariance term was applied, although a quick by-eye estimate suggests a covariance term of ~1.1. |
| | 7 | |
| | 8 | [[Image(stacking_noise_match.png)]] |
| | 9 | |
| | 10 | * Q2: "If you look at the data, it is clear that for low flux, the X2 levels out at a constant value, and for large flux value it increases. This means that c1=0.0" |
| | 11 | * A2: Agreed. Some consideration was made to switch to fitting a chi!^2 floor with an exponential rise with flux, but that should yield effectively the same result as the log-log fit used. |
| | 12 | * Q3: "What kind of errors do you assume when you fit stamp_chisqr versus stamp_flux?" |
| | 13 | * A3: We use no errors on this fit, so they are implicitly weighted equivalently. This is certainly why the fit is biased when low-flux/high chi!^2 points are included (such as in [[wiki:stacking_coverage.20130307/fit.0.png]]). The quick answer would be to use Poissonian error estimates, which would reduce the weight of these outliers. I think the log-log fits reduce the weights of these outliers more (log10(~200)/log10(~30) < sqrt(~200)/sqrt(~30)). |
| | 14 | |
