Open giuspugl opened 4 years ago
Hey Peppe, thanks for the PR. From what I understand your routine computes only the autospectra, storing on disk the total contribution from all the galaxy populations at some reference frequencies. Is that correct?
Yes the estimation is only for autospectra.To estimate the level of Poissonian noise you need to integrate as a function of flux ,S^2 *n(s) with n encodes the differential number counts coming from the updated Tucci model, published in Lagache et al. 2019 . The files in data folder encode the number counts
Do you think that it is possible to come up with a recipe for the cross spectra? I presume you need number counts and an SED for each population. BTW, we already have tools for several types of scaling as well as tools for summing several independent populations.
That's a good point ! I think we can follow the prescriptions presented in Reichardt et al 2012 (eq. 18) . Do you want me to keep committing in this PR to include Xspectra too ?
Sure, you can keep committing to this PR.
Merging #34 into fluxcut will decrease coverage by
8.11%
. The diff coverage is18.18%
.
@@ Coverage Diff @@
## fluxcut #34 +/- ##
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- Coverage 63.60% 55.49% -8.12%
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Files 4 4
Lines 294 355 +61
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+ Hits 187 197 +10
- Misses 107 158 +51
Impacted Files | Coverage Δ | |
---|---|---|
fgspectra/cross.py | 54.79% <ø> (ø) |
|
fgspectra/frequency.py | 50.00% <18.18%> (-28.88%) |
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This is a very first implementation of Cross Spectra. It relies on the definition of Cross spectra implemented in Reichardt et al 2012 (see eq.18 therein). A major difference is in the estimates of spectral indices, in this case. I used the spectral indices for Radio sources estimated from Planck Catalogue from 30 to 353 GHz.
First implementation of integrating radio source spectra. given a flux cut and freq, it looks for the closest number count model from Lagache et al 2019 (which is an updated version of Tucci et al. 2011) and returns the level of poissonian noise level due to undetected sources .