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A standard practice in extragalactic population studies is the fitting ofparametric models to galaxy images. From such fits, key structural parametersof galaxies such as total flux and effective radius (size) can be extracted.One of the most popular parametric forms is that of the S\'ersic profile, whichis flexible enough to reasonably fit the light distribution of nearly allgalaxies. Here we present pysersic, a Bayesian framework created to facilitatethe inference of structural parameters from galaxy images. Pysersic is writtenin pure Python, and is built using the jax framework, allowing for just-in-timecompilation, auto-differentiation and seamless execution on CPUs, GPUs or TPUs.Inference is performed with the numpyro package using gradient based methods,e.g., No U-Turn Sampling, for efficient and robust posterior estimation in onlya few minutes on a modern laptop. Pysersic is designed to have a user-friendlyinterface, allowing users to fit single or multiple sources in a few lines ofcode, while also being flexible enough for integration into current and futureanalysis pipelines. In addition to sampling, pysersic can produce pointestimates of the best model via optimization in several seconds, andapproximate the posterior via stochastic variational inference. The use of thenumpyro probabilistic language provides future extensibility to arbitrarymodels beyond the S\'ersic.

pysersic: A Python package for determining galaxy structural properties via Bayesian inference, accelerated with jax