  The transverse emittance of a charged particle beam is an important figure of
merit for many accelerator applications, such as ultra-fast electron
diffraction, free electron lasers and the operation of new compact accelerator
concepts in general. One of the easiest to implement methods to determine the
transverse emittance is the phase advance scan method using a focusing element
and a screen. This method has been shown to work well in the thermal regime. In
the space charge dominated laminar flow regime, however, the scheme becomes
difficult to apply, because of the lack of a closed description of the beam
envelope including space charge effects. Furthermore, certain mathematical, as
well as beamline design criteria must be met in order to ensure accurate
results. In this work we show that it is possible to analyze phase advance scan
data using a fully connected neural network (FCNN), even in setups, which do
not meet these criteria. In a simulation study, we evaluate the perfomance of
the FCNN by comparing it to a traditional fit routine, based on the beam
envelope equation. Subsequently, we use a pre-trained FCNN to evaluate measured
phase advance scan data, which ultimately yields much better agreement with
numerical simulations. To tackle the confirmation bias problem, we employ
additional mask-based emittance measurement techniques.
