The following image is an example of a fluorescence signal trace from the photomultiplier tube detector (PMT). Each spike represents one fluorescently labeled cell.
This signal model is definitely not additive white Gaussian, although the noise term is Gaussian. The signals are low variance but the point is that when the signal amplitude increases, the noise variance rapidly decreases. In the given example, above the intensity value of $0.6$ all three Gaussian signals are almost noise free. Around $0.4-0.5$ intensity of the noise is reduced but can be seen if zoomed. Below $0.4$ the noise is significantly apparent.
How can this signal-noise relation be properly modeled? For example for additive noise we have $$x[n]=s[n]+w[n]$$ where $x$ is the received signal $s$ is the error free signal and $w$ is the Gaussian noise.
My aim is to simulate such signals in Matlab using a theoretical model. However, I was not able to see and/or find any model via google search or on some related books.
The physics behind this is due to detection of laser signal via photodiodes or PMTs. I was thinking that the noise source was probably the quantum noise but I am not sure because for the quantum noise, the noise variance does not decrease when the total number of photons $N$ increases. Instead it increases as $\sqrt N$.
What I tried was dividing the noise values by $k+s[n]^2$ before adding to the signal, where $k$ is some constant. I did this over additive Gaussian noise model. The results were okay. But I am not able to justify this theoretically. It is just my intuitive understanding.