  The objective Bayesian treatment of a model representing two independent
Poisson processes, labelled as "signal" and "background" and both contributing
additively to the total number of counted events, is considered. It is shown
that the reference prior for the parameter of interest (the signal intensity)
can be well approximated by the widely (ab)used flat prior only when the
expected background is very high. On the other hand, a very simple
approximation (the limiting form of the reference prior for perfect prior
background knowledge) can be safely used over a large portion of the background
parameters space. The resulting approximate reference posterior is a Gamma
density whose parameters are related to the observed counts. This limiting form
is simpler than the result obtained with a flat prior, with the additional
advantage of representing a much closer approximation to the reference
posterior in all cases. Hence such limiting prior should be considered a better
default or conventional prior than the uniform prior. On the computing side, it
is shown that a 2-parameter fitting function is able to reproduce extremely
well the reference prior for any background prior. Thus, it can be useful in
applications requiring the evaluation of the reference prior for a very large
number of times. [The published version JINST 9 (2014) T10006 has a typo in the
normalization $N$ of eq.(2.6) that is fixed here.]
