Expression of Shot noise when expanding ##a_{\ell m}## coefficients

Click For Summary
SUMMARY

The expression for the quantity ##o_{\ell}##, related to Dark Matter (DM), is defined as ##o_{\ell}=b_{s p}^2 C_{\ell}^{D M}+B_{s p}##, where ##B_{s p}## represents Poisson noise calculated as ##B_{s p}=\frac{1}{\bar{n}}##, with ##\bar{n}## being the average number of observed galaxies. The derivation starts from the variance of the coefficients ##C_{\ell}=\operatorname{Var}\left(a_{\ell m}\right)## and involves expanding the expression for ##o_{\ell}## to include terms related to both Dark Matter and a secondary signal represented by ##a_{\ell m}^P##. The challenge lies in justifying the equality of the term ##<\left(a_{\ell m}^P\right)^2>## with ##\frac{1}{\bar{n}}##, as this relates to the variance of a Poisson distribution.

PREREQUISITES
  • Understanding of Poisson noise and its properties
  • Familiarity with statistical variance and covariance
  • Knowledge of cosmological coefficients, specifically ##a_{\ell m}##
  • Basic grasp of signal processing in astrophysics
NEXT STEPS
  • Research the properties of Poisson distributions and their variances
  • Study the derivation of cosmological coefficients in the context of Dark Matter
  • Explore the relationship between signal variance and noise in astrophysical measurements
  • Examine advanced statistical methods for analyzing cosmological data
USEFUL FOR

Astrophysicists, cosmologists, and researchers in the field of dark matter studies who are involved in analyzing and interpreting cosmological data and noise characteristics.

fab13
Messages
300
Reaction score
7
TL;DR
I would like to prove that Shot noise follows a Poisson distribution.
I would like to arrive at the following expression for the quantity ##o_{\ell}## ( with "DM" for Dark Matter ):

##o_{\ell}=b_{s p}^2 C_{\ell}^{D M}+B_{s p}##

with Poisson noise ##B_{s p}=\frac{1}{\bar{n}}(\bar{n}## being the average number of galaxies observed). the index "sp" is for spectro. I think for now that ##B_{s p}## is the variance of a Poisson noise but see the following below to really confirm: To arrive at this same expression, I would like to start from ##{ }_{\ell m}^{a D M}## (DM for Dark matter) and ##a_{\ell m}^P## (" ##\mathrm{P}## " for fish).
So I start from the fact that ##C_{\ell}=\operatorname{Var}\left(a_{\ell m}\right)## :

##o_{\ell}=<\left(b_{s p} a_{\ell m}^{D M}+a_{\ell m}^P\right)^2>##

If we expand, we have: ##o_{\ell}=<b_{s p}^2\left(a_{\ell m}^{D M}\right)^2+2 b_{s p} a_{\ell m}^{D M}+\left(a_{\ell m}^P\right)^2>##

##o_{\ell}=b_{s p}^2 C_{\ell}^{D M}+2 b_{s p}<a_{\ell m}^{D M}><a_{\ell m}^P>+<\left(a_{\ell m}^P\right)^2>##

##=b_{s p}^2 C_{\ell}^{D M}+<\left(a_{\ell m}^P\right)^2>##

because we have ##<a_{\ell_m}^{D M}>=0##

The problem comes from the term ##<\left(a_{\ell m}^P\right)^2>## : I don't know how to justify that this term is equal to ##\frac{1}{\bar{n}}##

Indeed, if ##B_{s p}## is a fish noise, we should have, to make the correspondence, ##B_{s p}=<\left(a_{\ell m}^P\right)^2>-<## ##a_{\ell m}^P>2## which is different from: ##B_{s p}=<\left(a_{\ell m}^P\right)^2>=\operatorname{Var}\left(a_{\ell m}^P\right)##.

How to obtain the quantity ##B_{s p}## which seems a priori equal to ##\frac{1}{\bar{n}}## ?

If ##B_{s p}## is equal to ##<\left(a_{\ell m}^P\right)^2>##, how to make the link with a variance since a Poisson law is not centered ( I mean ##<a_{\ell m}^P>\neq 0## ?
 
Last edited:
Astronomy news on Phys.org
There is a typo showed in attachment : the factor "2" is acutally an exponent in ##<a_{\ell m}^P>^2##.
 

Attachments

  • Capture d’écran 2022-12-27 à 09.05.43.png
    Capture d’écran 2022-12-27 à 09.05.43.png
    10.3 KB · Views: 129
Also (sorry), the initial quantity at the beginning ( ##o_{\ell}##) is simply the total signal ##C_\ell## :

##o_{\ell}=b_{s p}^2 C_{\ell}^{D M}+B_{s p}##

is equal to :

##C_{\ell}=b_{s p}^2 C_{\ell}^{D M}+B_{s p}##
 

Similar threads

  • · Replies 0 ·
Replies
0
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 36 ·
2
Replies
36
Views
4K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 9 ·
Replies
9
Views
2K
Replies
1
Views
2K
  • · Replies 8 ·
Replies
8
Views
2K