  Time-domain observations are increasingly important in astronomy, and are
often the only way to study certain objects. The volume of time-series data is
increasing dramatically as new surveys come online - for example, the Vera
Rubin Observatory will produce 15 terabytes of data per night, and its Legacy
Survey of Space and Time (LSST) is expected to produce five-year lightcurves
for $>10^7$ sources, each consisting of 5 photometric bands. Historically,
astronomers have worked with Fourier-based techniques such as the Lomb-Scargle
periodogram or information-theoretic approaches; however, in recent years
Bayesian and data-driven approaches such as Gaussian Process Regression (GPR)
have gained traction. However, the computational complexity and steep learning
curve of GPR has limited its adoption. `pgmuvi` makes GPR of multi-band
timeseries accessible to astronomers by building on cutting-edge open-source
machine-learning libraries, and hence `pgmuvi` retains the speed and
flexibility of GPR while being easy to use. It provides easy access to GPU
acceleration and Bayesian inference of the hyperparameters (e.g. the periods),
and is able to scale to large datasets.
