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| foregroundstelecon20201029 [2020/11/02 08:57] – hanany | foregroundstelecon20201029 [2021/01/21 09:57] (current) – hanany | ||
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| + | * Both Ragnhild and Mathieu have more results. Start with Ragnhild. | ||
| * {{ : | * {{ : | ||
| * After success with 90.91 she is working with 90.92 (SH in a follow up e-mail: how do we define success quantitatively? | * After success with 90.91 she is working with 90.92 (SH in a follow up e-mail: how do we define success quantitatively? | ||
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| * Slide 8: dust is fit very well (note: no small scale structure!) | * Slide 8: dust is fit very well (note: no small scale structure!) | ||
| * Slide 9: increased chi^2 when 90.91 skies are run with 90.92 underlying model! Explanation: | * Slide 9: increased chi^2 when 90.91 skies are run with 90.92 underlying model! Explanation: | ||
| + | * CL: 2% AME polarization is ' | ||
| + | * Kris: are we using AME temperature or just polarization? | ||
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| + | * Nov. 3 e-mail with SH: | ||
| + | * Regarding the chi^2: | ||
| + | * One sign of a «good» chi^2 is that there is no big scale patterns in the maps, but rather white noise fluctuations. There will almost always be big scale patterns along the galaxy plane, so this is why we mask this out. I usually smooth the chi^2 maps to easier see if there are any large scale patterns. | ||
| + | * When there are big scale patterns in the chi^2 maps, I try to recognize which foreground might have caused this pattern to see which foreground that needs to be fitted better. In the case of the real data from Pico, we would start by fitting the known foregrounds and then look at the chi^2 and the residuals if there might be signals that we’re not taken into account. | ||
| + | * We have 21 input maps, one pr frequency. When I’m fitting for 6 parameters (synchrotron amplitude and beta, dust amplitude, beta and temperature and cmb) as for model 90.91, I will have 21-6=15 degrees of freedom and thus expect the chi^2 for a well fitted model to have an average value of 15. When I’m fitting for 12 parameters (synchrotron amplitude, curvature and beta, dust amplitude, beta and temperature for two dust models, AME amplitude and peak frequency and cmb) as for model 90.92, I will have 21-12=9 degrees of freedom and expect the average chi^2 value to be 9. However, counting degrees of freedom is complicated when informative priors are used since a free parameter then doesn' | ||
| + | * The numerical value for the chi^2 is difficult to see in the unsmoothed chi^2 maps, I will try to make this clearer in future presentations. | ||
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| + | * SH: Slide 3: I notice that the masking now is 40%. There was less masking for 90.91. Shouldn' | ||
| + | * Response: I have been testing different masks to check for biases. I generate the masks based on the chi^2 maps, and before I am happy with my results I will not be able to have the final mask. I think it’s best to use the same mask for both models and all final results published in a paper. | ||