Including systematic effects

best knowledge on experiment

For simplicity we analyse here only the case of a single experiment. In the case of many experiments, we only need to iterate the Bayesian inference, as has often been shown in these notes.

Following the general lines given in Section , the problem can be solved by considering the conditional probability, obtaining :

The case of absolutely precise knowledge of is recovered when is a Dirac delta.

Let us treat in some more detail the case of null observation
(
). For each possible value of
one has an exponential of expected value
[see Eq. ()]. Each of the exponentials is weighted
with
. This means that, if
is rather symmetrical around its
barycentre (expected value), in a first approximation
the more and less steep exponentials will compensate, and the
result of integral () will be close to
calculated in the barycentre of , i.e. in its
nominal value
:

Data | Data dData | ||

Data | Data |

(9.12) | |||

d | (9.13) |

is negative up to , indicating that systematic errors normally distributed tend to increase the upper limit. But the size of the effect is very tiny, and depends on the probability level chosen for the upper limit. This can be seen better in the lower plot of Fig. , which shows the integral of the difference of the two functions. The maximum difference is for . As far as the upper limits are concerned, we obtain (the large number of -- non-significatant--digits is only to observe the behaviour in detail):

at | |||

at | |||

at | |||

at |

An uncertainty of 10% due to systematics produces only a 1% variation of the limits.

To simplify the calculation (and also to get a feeling of what is going on) we can use some approximations.

- Since the dependence of the upper limit of from
is given by
EdWe need to solve an integral simpler than in the previous case. For the above example of we obtain at 90% and at 95%.
- Finally, as a real rough approximation, we can take into account
the small asymmetry of around the value obtained at the
nominal value of averaging the two values
of at
from
:

We obtain numerically identical results to the previous approximation.

As a more general remark, one should not forget that the upper limit has the meaning of an uncertainty and not of a value of quantity. Therefore, as nobody really cares about an uncertainty of 10 or 20% on the uncertainty, the same is true for upper/lower limits. At the per cent level it is mere numerology (I have calculated it at the level just for mathematical curiosity).