un:poisson-distribution
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| - | ====== Poisson distribution ====== | ||
| - | A discrete [[probability distribution]] that expresses the probability of a given number of events occurring in a fixed interval of time or space when the events occur independently (non-correated) and at a constant given average rate. It is named after the French mathematician Siméon Denis Poisson (1781--1840). | ||
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| - | If the average (mean) rate is $\mu$, the probability of counting $x$ events in a single trial | ||
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| - | $$ P(x,\mu) = \frac{\mu^x}{x!} e^{-\mu} $$ | ||
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| - | where $e$ is [[Euler' | ||
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| - | [[https:// | ||
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| - | For example, if $\mu=2.8$ (green curve) raindrops are falling on a tin roof on average per second, then the probability of hearing 4 raindrops in the next second $P(4,2.8) \approx 0.15$. | ||
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| - | The variance of Poisson distribution $\sigma^2=\mu$ and, hence, the standard deviation $\sigma=\sqrt{\mu}$. | ||
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| - | The fractional uncertainty in counting $N$ events | ||
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| - | $$ \frac{\sigma}{\mu} \approx \frac{1}{\sqrt{N}}. $$ | ||
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| - | If $N$ photons have arrived in the detector of a telescope in $t$ seconds, then the //counts per second// $N/t$ has an uncertainty $\sigma/t \approx \sqrt{N}/ | ||
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| - | To decrease the uncertainty in measuring photons, we have to increase the number of photons by either increasing the size of the telescope of the exposure time. | ||
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