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@INPROCEEDINGS{Daniel:1047025,
      author       = {Daniel, Davis Thomas and Bartsch, Christian Hippolyt and
                      Bereck, Franz Philipp and Scheurer, Christoph and Köcher,
                      Simone Swantje and Granwehr, Josef},
      title        = {{I}lt{P}y: {A} python library for inverse {L}aplace
                      transform of magnetic resonance data},
      reportid     = {FZJ-2025-04081},
      year         = {2025},
      abstract     = {Relaxation and diffusion processes offer rich information
                      about interactions anddynamics in materials and can be
                      correlated to chemical composition and structure.
                      [1]Experimental magnetic resonance data obtained from
                      relaxation and diffusionmeasurements are often analysed by
                      fitting a suitable mathematical function to the datafor
                      extracting underlying relaxation time and diffusion rate
                      constants. However,depending on the compositional
                      heterogeneity, processes may exhibit functionaldependencies
                      which are not governed by a single characteristic parameter
                      but adistribution. Therefore, experimentally measured data
                      may feature a superposition ofdifferent contributions, and
                      their disentanglement becomes challenging by
                      conventionaldata analysis methods. In such cases, inversion
                      algorithms allow for quantitativeanalysis by inverting the
                      data with a suitable kernel, mitigating the need for
                      possiblyambiguous assumptions regarding the number of
                      components or the shape of theunderlying distribution.
                      Herein, IltPy,[2] an open-source python library is
                      introduced forperforming regularized inverse Laplace
                      transforms (ILT) of one- and multi-dimensionaldata.
                      Conventional approaches to ILT of magnetic resonance data
                      require anassumption of signal contributions to be strictly
                      positive. [3] However, this approachsuppresses negative
                      contributions which may be physically relevant, particularly
                      insystems undergoing chemical exchange or
                      cross-relaxation.[4]IltPy implements regularized inverse
                      Laplace transform (ILT) without requiring non-negativity
                      (NN) constraints.[5] Tikhonov regularization in its
                      generalized form is used,and the solution is stabilized with
                      a uniform penalty and a zero-crossing penaltyallowing for
                      extraction of parameter distributions preserving both
                      positive and negativefeatures in the data without preferring
                      one of the signs. IltPy supports user-definedkernels and
                      validity of NN constraint can be tested by comparing
                      inversions with andwithout NN. For large data sets, singular
                      value decomposition is used to compressdata. For
                      experimental data with non-uniform noise or oscillatory
                      features, such asESEEM, IltPy supports weighted inversions.
                      Furthermore, resolution of multi-dimensional data may be
                      improved by regularization of non-inverted
                      dimensions.[2]IltPy is particularly suited for EPR and NMR
                      spectroscopic data. The performance ofthe library is
                      demonstrated using application examples from relaxation and
                      diffusiondata sets, revealing insights into interactions and
                      environments in complex systems.[1] Daniel, D.T. et. al.
                      Phys. Chem. Chem. Phys. 2023, 25, 12767–12776[2]
                      https://apps.fz-juelich.de/iltpy [Accessed 17.04.2025][3]
                      Provencher, S. Comput. Phys. Commun. 1982, 27, 213−227[4]
                      Rodts S, Bytchenkoff D. Journal of Magnetic Resonance. 2010,
                      205, 315-318.[5] Granwehr, J. et. al. Journal of chemical
                      theory and computation. 2012, 8,3473-3482},
      month         = {Sep},
      date          = {2025-09-15},
      organization  = {46. FGMR Annual Discussion Meeting
                       2025, Bonn (Germany), 15 Sep 2025 - 18
                       Sep 2025},
      subtyp        = {After Call},
      cin          = {IET-1},
      cid          = {I:(DE-Juel1)IET-1-20110218},
      pnm          = {1223 - Batteries in Application (POF4-122) / DFG project
                      G:(GEPRIS)441255373 - Design polymerbasierter organischer
                      Dünnschicht-Batterien hin zu IoT Anwendungen. (441255373)},
      pid          = {G:(DE-HGF)POF4-1223 / G:(GEPRIS)441255373},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1047025},
}