  We present a convolutional autoencoder to denoise pulses from a p-type point
contact high-purity germanium detector similar to those used in several rare
event searches. While we focus on training procedures that rely on detailed
detector physics simulations, we also present implementations requiring only
noisy detector pulses to train the model. We validate our autoencoder on both
simulated data and calibration data from an $^{241}$Am source, the latter of
which is used to show that the denoised pulses are statistically compatible
with data pulses. We demonstrate that our denoising method is able to preserve
the underlying shapes of the pulses well, offering improvement over traditional
denoising methods. We also show that the shaping time used to calculate energy
with a trapezoidal filter can be significantly reduced while maintaining a
comparable energy resolution. Under certain circumstances, our denoising method
can improve the overall energy resolution. The methods we developed to remove
electronic noise are straightforward to extend to other detector technologies.
Furthermore, the latent representation from the encoder is also of use in
quantifying shape-based characteristics of the signals. Our work has great
potential to be used in particle physics experiments and beyond.
