Bias and dark current from a graph of mean signal vs exposure time

In summary: The dark current can then be calculated by finding the y-intercept of the graph, which represents the bias. In summary, by taking the average of all the mean signals, you can get a rough estimate of the bias per pixel. However, this only works if the dark current is very low. In cases where the dark current is high or the exposures are long, relying on the mean value alone may result in significant errors. Instead, you can use the slope and y-intercept of the graph to calculate the bias and dark current more accurately.
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beans123
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I am attempting to find the bias and dark current from a graph of mean signal (per pixel) vs exposure time (sec) for 8 different dark frames.
I have a graph of mean signal (per pixel) vs exposure time (sec) for 8 different dark frames. I am being asked to find the bias in ADUs/pixel and the dark current in ADUs/sec/pixel and I am very confused on how I could get it. I know that the average of all of the mean signals is a rough guide as to what I should get for the bias per pixel but I'm very confused as to how to find the dark current from the graph.
 
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beans123 said:
I know that the average of all of the mean signals is a rough guide as to what I should get for the bias per pixel
Only if the dark current is very low. For example, if the bias is 100 ADU and the dark current is 0.1 ADU/s, and you take an exposure of 10 seconds, the mean pixel value of approximately 101 ADU is very close to the bias. But if the exposures are very long or the dark current is very large then you might wind up with a bias of 100 ADU but a mean pixel value of 150 ADU, in which case you would be off by 50% if you just went with the mean value.

beans123 said:
I have a graph of mean signal (per pixel) vs exposure time (sec) for 8 different dark frames.
Do these dark frames have the same exposure time, or different? If different, you can find the slope of the graph and extrapolate backwards to zero seconds exposure time to find the bias.
 
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FAQ: Bias and dark current from a graph of mean signal vs exposure time

What is bias and dark current?

Bias and dark current are two types of noise that can affect the accuracy of a signal in scientific measurements. Bias is a constant offset in the signal, while dark current is a small electrical current that is present in the sensor even in the absence of light.

How do bias and dark current affect the mean signal?

Bias and dark current can both add to the signal, resulting in an overestimation of the true signal. This can lead to inaccurate measurements and affect the reliability of the data.

How can we reduce bias and dark current in our measurements?

One way to reduce bias and dark current is by using a longer exposure time. This allows more light to reach the sensor, reducing the impact of dark current. Additionally, cooling the sensor can also reduce dark current.

What is the relationship between exposure time and bias/dark current?

As exposure time increases, the impact of bias and dark current decreases. This is because more light is reaching the sensor, which reduces the relative impact of the small dark current.

Can we completely eliminate bias and dark current from our measurements?

No, it is not possible to completely eliminate bias and dark current. However, by using techniques such as longer exposure times and cooling the sensor, we can reduce their impact and improve the accuracy of our measurements.

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