Calculate the RMS value?Curtnos said:I'm currently working with the 1d DEEP2 DEIMOS fits files (see http://deep.ps.uci.edu/deep3/specprimer.html) and am trying to define some exclusion criteria in Python 2.7 for the data based on the noise. What's the best way to quantify the noise in order to do this?
Thank you!
I should have added, a lot of the data is showing very odd and unpredictable behaviour. I'm unsure how to edit the original question, however I was hoping to find a more general method to both discount noisy data and bad data which fluctuates unpredictably. Some have sudden, repeated drops to zero flux, some have fluxes which appear symmetric about the flux = 0 line, some look like step functions, etc.berkeman said:Calculate the RMS value?
Signal to noise ratio (SNR) is a measure of the strength of a signal compared to the level of background noise. In the context of deimos data, SNR is important because it allows us to determine the quality and reliability of the data. A high SNR indicates a strong signal and low background noise, while a low SNR may indicate a weaker signal or high levels of noise that can affect the accuracy of the data.
SNR is calculated by dividing the average signal by the standard deviation of the background noise. In deimos data, the signal is typically the intensity of the desired signal, while the background noise is the average intensity of the surrounding area. This calculation results in a numerical value that represents the relative strength of the signal compared to the noise.
There are several factors that can affect the SNR in deimos data, including the sensitivity of the instrument, exposure time, atmospheric conditions, and the distance of the object being observed. The presence of electronic noise or artifacts in the data can also impact the SNR.
The ideal SNR value for deimos data can vary depending on the specific research goal and the type of data being collected. In general, a higher SNR indicates a better quality of data, but what is considered a good SNR can vary between different scientific fields and research objectives.
A low SNR can impact the accuracy and reliability of the analysis of deimos data. This is because a low SNR can make it difficult to distinguish the signal from the background noise, leading to potential errors and uncertainties in the data. In some cases, a low SNR may even make it impossible to extract meaningful information from the data.