  In astrophysics a common goal is to infer the flux distribution of
populations of scientifically interesting objects such as pulsars or
supernovae. In practice, inference for the flux distribution is often conducted
using the cumulative distribution of the number of sources detected at a given
sensitivity. The resulting "$\log(N>S)$-$\log (S)$" relationship can be used to
compare and evaluate theoretical models for source populations and their
evolution. Under restrictive assumptions the relationship should be linear. In
practice, however, when simple theoretical models fail, it is common for
astrophysicists to use prespecified piecewise linear models. This paper
proposes a methodology for estimating both the number and locations of
"breakpoints" in astrophysical source populations that extends beyond existing
work in this field. An important component of the proposed methodology is a new
interwoven EM algorithm that computes parameter estimates. It is shown that in
simple settings such estimates are asymptotically consistent despite the
complex nature of the parameter space. Through simulation studies it is
demonstrated that the proposed methodology is capable of accurately detecting
structural breaks in a variety of parameter configurations. This paper
concludes with an application of our methodology to the Chandra Deep Field
North (CDFN) data set.
