Gamma-ray observations ranging from hundreds of MeV to tens of TeV are a valuable tool for studying particle acceleration and diffusion within our galaxy. Supernova remnants, pulsar wind nebulae, and star-forming regions are the main particle accelerators in our local Galaxy. Constructing a coherent physical picture of these astrophysical objects requires the ability to distinguish extended regions of gamma-ray emission, the ability to analyze small-scale spatial variation within these regions, and methods to synthesize data from multiple observatories across multiple wavebands. Imaging Atmospheric Cherenkov Telescopes (IACTs) provide fine angular resolution (<0.1 degree) for gamma-rays above 100 GeV. Typical data reduction methods rely on source-free regions in the field of view to estimate cosmic-ray background. This presents difficulties for sources with unknown extent or those which encompass a large portion of the IACT field of view (3.5 degrees for VERITAS). Maximum-likelihood-based techniques are well-suited for analysis of fields with multiple overlapping sources, diffuse background components, and combining data from multiple observatories. Such methods also offer an alternative approach to estimating the IACT cosmic-ray background and consequently an enhanced sensitivity to largely extended sources. In this proceeding, we report on the current status and performance of a maximum likelihood technique for the IACT VERITAS. In particular, we focus on how our method framework employs a dimension for gamma-hadron separation parameters in order to improve sensitivity on extended sources.
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