Simple models of gravitational arcs are crucial to simulate large samples of
these objects with full control of the input parameters. These models also
provide crude and automated estimates of the shape and structure of the arcs,
which are necessary when trying to detect and characterize these objects on
massive wide area imaging surveys. We here present and explore the ArcEllipse,
a simple prescription to create objects with shape similar to gravitational
arcs. We also present PaintArcs, which is a code that couples this geometrical
form with a brightness distribution and adds the resulting object to images.
Finally, we introduce ArcFitting, which is a tool that fits ArcEllipses to
images of real gravitational arcs. We validate this fitting technique using
simulated arcs and apply it to CFHTLS and HST images of tangential arcs around
clusters of galaxies. Our simple ArcEllipse model for the arc, associated to a
S\'ersic profile for the source, recovers the total signal in real images
typically within 10%-30%. The ArcEllipse+S\'ersic models also automatically
recover visual estimates of length-to-width ratios of real arcs. Residual maps
between data and model images reveal the incidence of arc substructure. They
may thus be used as a diagnostic for arcs formed by the merging of multiple
images. The incidence of these substructures is the main factor preventing
ArcEllipse models from accurately describing real lensed systems.Comment: 12 pages, 11 figures, accepted for publication in A&