Sparse linear dictionary reconstruction for removing microphonic noise from nuclear spectrometry measurements

Abstract

The deployment of precise and sensitive nuclear spectroscopy instrumentation, using high purity germanium (HPGe), requires sustained cryogenic temperatures that can be achieved with cryocooler heat pumps. However, these pumps introduce microphonic noise that broaden the peaks of the spectra. This work demonstrates the efficacy of L1-norm regularization for the purpose of removing microphonic noise generated by the cryocooler, while still recovering the correct detector pulse amplitudes. Data are first collected from an He3 neutron detector with an attached cryocooler which enables the controlled addition of microphonic noise. A dictionary is then created by combining two relatively incoherent matrices, the first is the Fourier basis and the second is a matrix created from normalized and shifted pulses. By applying this dictionary to background noise, the regularization parameter λ is obtained. Using an L1-regularization least squares solver, microphonic noise can be effectively separated, therefore reducing the variance of the detector pulses while preserving the correct amplitude.