- #1
marco1235
Good morning PF,
I'm feeling a bit doubtful about this issue. I'm working with optical detectors and I have to characterize them in terms of quantum efficiency and other similar things. Now suppose my detector is, ideally, a single large pixel, which I illuminate for a specific time. Then I store the recorded Nphotons and repeat the procedure for 10k times! At each iteration, due to the randomness of the process I can get 100 counts in the first step, 102 at the second, 95, 87, 101, 106, ... an so on.
I want to make an average of such 10k values, and that's fine. But how about the uncertainty associated with this repeated measure? I have two ways:
1) computing the standard deviation using std-like function (Matlab)
2) putting Navg as argument of the squared-root like in Poisson processes
I'm really stucked in this situation.
Hope someone could help me!
Have a nice day
I'm feeling a bit doubtful about this issue. I'm working with optical detectors and I have to characterize them in terms of quantum efficiency and other similar things. Now suppose my detector is, ideally, a single large pixel, which I illuminate for a specific time. Then I store the recorded Nphotons and repeat the procedure for 10k times! At each iteration, due to the randomness of the process I can get 100 counts in the first step, 102 at the second, 95, 87, 101, 106, ... an so on.
I want to make an average of such 10k values, and that's fine. But how about the uncertainty associated with this repeated measure? I have two ways:
1) computing the standard deviation using std-like function (Matlab)
2) putting Navg as argument of the squared-root like in Poisson processes
I'm really stucked in this situation.
Hope someone could help me!
Have a nice day