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wiki:astronomy:observational_astronomy:snr [2024/11/04 19:25] – Roy Prouty | wiki:astronomy:observational_astronomy:snr [2024/11/04 21:06] (current) – Roy Prouty | ||
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=== Telescope Systemics Equation === | === Telescope Systemics Equation === | ||
- | For any given CCD-like detector, the light frame reported by the detector is the direct sum of a few disparate sources of counts. | + | As discussed in [[wiki: |
$$L_{ij}(\lambda, | $$L_{ij}(\lambda, | ||
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What's worse is that there is no guarantee that each pixel must abide the same random distribution as its neighbors. There may be pixel-level or optical system-level defects ensuring this. So each pixel in each frame is a sampling of some unknown probability distribution. | What's worse is that there is no guarantee that each pixel must abide the same random distribution as its neighbors. There may be pixel-level or optical system-level defects ensuring this. So each pixel in each frame is a sampling of some unknown probability distribution. | ||
- | === Signals === | + | === Stacks of Signals === |
- | The measurement of counts in the pixels of each light frame are clearly owing to a few separate phenomenon occurring in unison at the time of observation. The generation of photoelectrons from incident light, the liberation of electrons owing to their temperature, | + | The measurement of counts in the pixels of each light frame are clearly owing to a few separate phenomenon occurring in unison at the time of observation. The generation of photoelectrons from incident light, the liberation of electrons owing to their temperature, |
These processes can be isolated and sampled repeatedly, allowing us to build-out a rough distribution of measured counts. For each the Light and the Dark, we can take (separately) an ensemble average and recover what is called the " | These processes can be isolated and sampled repeatedly, allowing us to build-out a rough distribution of measured counts. For each the Light and the Dark, we can take (separately) an ensemble average and recover what is called the " | ||
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Therefore, a sequence of $N$ Light frames, | Therefore, a sequence of $N$ Light frames, | ||
- | Being more clear, consider a the small array of pixels constituting a single frame read-out from a detector with pixels labeled $i$ & $j$. | + | Being more clear, consider a the small array of pixels constituting a single frame read-out from a detector with pixels labeled $i$ & $j$. {{ : |
- | {{ : | + | |
- | We can imagine rotating this array onto its side ... | + | We can imagine rotating this array onto its side ... {{ : |
- | {{ : | + | |
- | And finally, considering that each pixel of each frame captured is a sampling of a probability distribution, | + | And finally, considering that each pixel of each frame captured is a sampling of a probability distribution, |
- | {{ : | + | |
+ | If each pixel value (the counts) from the $i,j$-th pixel is a random sampling of a [[wiki: | ||
+ | |||
+ | So we can treat each " | ||
+ | |||
+ | ---- | ||
+ | |||
+ | ==== Noise ==== | ||
+ | |||
+ | For a Normal Distribution, | ||
+ | |||
+ | Further, for the sources of unwanted signal, they each come with their own signal and noise -- trusted to be Normally Distributed. So in the construction of the calibrated frame, only the *signal* from unwanted signal sources can be removed. The uncertainty associated with these signals cannot be separated -- they were measured at the same time! All one can hope to do here is either (a) not collect unwanted signal & noise in the first place or (b) acknowledge the uncertainty in the final result. | ||
+ | |||
+ | {{ : | ||
+ | |||
+ | {{ : | ||
+ | In the above, despite the average Dark Signal being removed, the uncertainty associated with the collection of the Dark cannot be disentangled from the uncertainty in the source signal -- so it remains. The distribution of dark signal is therefore centered on $0$, but with some spread about $0$. | ||
+ | |||
+ | ==== Signal to Noise Ratio ==== | ||
+ | In returning to the ultimate goal of claiming one hypothesis true over another ... The Null Hypothesis is that the signal is indistinguishable from the noise. The Alternate Hypothesis is that the signal is distinguishable from the noise. We can construct the statistical model of the Alternate Hypothesis with $\mathcal{Norm}(\mu, | ||
+ | |||
+ | So the figure of merit here is the Signal-to-Noise Ratio (SNR). In short, this quantity is the number of standard deviations the average calibrated signal is from the uncertainty in that average signal *collected*.\\ \\ | ||
+ | |||
+ | **At the UMBC Observatory, | ||