  In quantum computing, characterising the full noise profile of qubits can aid
the efforts towards increasing coherence times and fidelities by creating error
mitigating techniques specific to the type of noise in the system, or by
completely removing the sources of noise. Spin qubits in MOS quantum dots are
exposed to noise originated from the complex glassy behaviour of two-level
fluctuators, leading to non-trivial correlations between qubit properties both
in space and time. With recent engineering progress, large amounts of data are
being collected in typical spin qubit device experiments, and it is beneficiary
to explore data analysis options inspired from fields of research that are
experienced in managing large data sets, examples include astrophysics, finance
and climate science. Here, we propose and demonstrate wavelet-based analysis
techniques to decompose signals into both frequency and time components to gain
a deeper insight into the sources of noise in our systems. We apply the
analysis to a long feedback experiment performed on a state-of-the-art
two-qubit system in a pair of SiMOS quantum dots. The observed correlations
serve to identify common microscopic causes of noise, as well as to elucidate
pathways for multi-qubit operation with a more scalable feedback system.
