  This research note presents a derivation and implementation of efficient and
scalable gradient computations using the celerite algorithm for Gaussian
Process (GP) modeling. The algorithms are derived in a "reverse accumulation"
or "backpropagation" framework and they can be easily integrated into existing
automatic differentiation frameworks to provide a scalable method for
evaluating the gradients of the GP likelihood with respect to all input
parameters. The algorithm derived in this note uses less memory and is more
efficient than versions using automatic differentiation and the computational
cost scales linearly with the number of data points.
