Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7909-2021
https://doi.org/10.5194/amt-14-7909-2021
Research article
 | 
21 Dec 2021
Research article |  | 21 Dec 2021

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data

Markus D. Petters

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Agarwal, S., Mierle, K., and Others: Ceres Solver, available at: http://ceres-solver.org, 2020. a
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Baart, M. L.: The Use of Auto-Correlation for Pseudo-Rank Determination in Noisy III-Conditioned Linear Least-Squares Problems, IMA Journal of Numerical Analysis, 2, 241–247, https://doi.org/10.1093/imanum/2.2.241, 1982. a
Bates, D. M., Lindstrom, M. J., Wahba, G., and Yandell, B. G.: GCVPACK – Routines for Generalized Cross Validation, Tech. Rep. Technical Report No. 775, University of Wisconsin, Department of Statistics, 1986. a, b
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B.: Julia: A Fresh Approach to Numerical Computing, SIAM Review, 59, 65–98, https://doi.org/10.1137/141000671, 2017. a
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Short summary
Inverse methods infer physical properties from a measured instrument response. Measurement noise often interferes with the inversion. This work presents a general, domain-independent, accessible, and computationally efficient software implementation of a common class of statistical inversion methods. In addition, a new method to invert data from humidified tandem differential mobility analyzers is introduced. Results show that the approach is suitable for inversion of large-scale datasets.
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