  We explore the anomaly detection framework based on Normalizing Flows (NF)
models introduced in \cite{PhysRevC.106.065802} to detect the presence of a
large (destabilising) dense matter phase transition in neutron star (NS)
observations of masses and radii, and relate the feasibility of detection with
parameters of the underlying mass-radius sequence, which is a functional of the
dense matter equation of state. Once trained on simulated data featuring
continuous $M(R)$ solutions (i.e., no phase transitions), NF is used to
determine the likelihood of a first-order phase transition in a given set of
$M(R)$ observations featuring a discontinuity, i.e., perform the anomaly
detection. Different mock test sets, featuring two branch solutions in the
$M(R)$ diagram, were parameterized by the NS mass at which the phase transition
occurs, $M_c$, and the radius difference between the heaviest hadronic star and
lightest hybrid star, $\Delta R$. We analyze the impact of these parameters on
the NF performance in detecting the presence of a first-order phase transition.
Among the results, we report that given a set of 15 stars with radius
uncertainty of $0.2$ km, a detection of a two-branch solution is possible with
95\% accuracy if $\Delta R > 0.4$ km.
