  As astronomical data grows in volume and complexity, the scalability of
analysis software becomes increasingly important. At the same time,
astrophysics analysis software relies heavily on open-source contributions, so
languages and tools that prioritize both performance and readability are
especially valuable. Julia, with its just-in-time compiler and high level
syntax, offers a compelling alternative to traditional languages like Python or
C.
  In this paper, we outline ShOpt.jl, a new software package for point spread
function (PSF) characterization written in Julia. ShOpt.jl features a number of
performance optimizations, such as multithreading, the use of preconditioners,
and the implementation of the memory-limited Broyden-Fletcher-Goldfarb-Shanno
algorithm, as well as the flexibility to choose between principal component
analysis, an autoencoder, and analytic profiles for PSF characterization. As
observatories like the James Webb Space Telescope bring astrophysics into a new
era of wide-field, high-resolution imaging, the challenges of PSF modeling
become more pronounced. Tools like ShOpt.jl provide the community with a
scalable, efficient, and accurate solution to these challenges, while also
demonstrating the potential of Julia as a language that meets the demands of
modern astrophysical research.
