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Cited by 301

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Efficient and robust analysis of complex scattering data under noise in microwave resonators

Open access
Probst, S., Song, F. B., Bushev, P. A., Ustinov, A. V., Weides, M.·2015·Review of Scientific Instruments

Abstract

Superconducting microwave resonators are reliable circuits widely used for detection and as test devices for material research. A reliable determination of their external and internal quality factors is crucial for many modern applications, which either require fast measurements or operate in the single photon regime with small signal to noise ratios. Here, we use the circle fit technique with diameter correction and provide a step by step guide for implementing an algorithm for robust fitting and calibration of complex resonator scattering data in the presence of noise. The speedup and robustness of the analysis are achieved by employing an algebraic rather than an iterative fit technique for the resonance circle.

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Metadata

Authors
Probst, S., Song, F. B., Bushev, P. A., Ustinov, A. V., & Weides, M.
Venue
Review of Scientific Instruments
Published
2015-02-01
Volume
86
Issue
2
Pages
024706-024706
ISSN
  • 0034-6748 (ISSN-L)
  • 1089-7623
OpenAlex
W2049715514
Cited by
301
References
24
FWCI
3.05
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Efficient and robust analysis of complex scattering data under noise in microwave resonators. https://app.cochat.ai/papers/10.1063/1.4907935
The paper
Probst, S., Song, F. B., Bushev, P. A., Ustinov, A. V., & Weides, M. (2015). Efficient and robust analysis of complex scattering data under noise in microwave resonators. Review of Scientific Instruments. https://doi.org/10.1063/1.4907935

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.