  It has always been a challenge for researchers to efficiently and accurately
post process experimental data which is distorted by the noise. Superconducting
microwave devices e.g. resonators, directional filters, beam-splitters etc.
operate at frequency of several GHz to THz and temperatures well below critical
temperature (Tc) with few exceptions like transition edge sensors where devices
are operated at temperatures close to Tc. These devices are measured usually
with vector network analyser in terms of scattering parameters. Two kinds of
errors, systematic and drift can easily be removed from the measurements taken
with VNA. However, random errors are not easy to address and remove due to
their unpredictability and randomness. In this manuscript we will present an
algorithm to post process experimental data to cope with measurements that have
been corrupted or useful spectral response is buried in spurious signal. We
have developed a robust and efficient algorithm, implemented in MATLAB, to
detect peaks in spectral response, remove baseline and finally estimate
parameters of two-port superconductor resonator using an Improved Nelder-Mead
Method for unconstrained multidimensional least square minimization. The
algorithm has been successfully tested and verified by processing spectral
response of half wavelength microwave transmission-line resonator successfully
isolating resonator response from noisy background. We were able to compute
loaded quality factor, resonance frequency from response data with high
reproducibility even from those experimental data sets where resonance spikes
were hardly visible.

Data Analysis, Statistics and Probability