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PreprintarXiv licencearXiv

Solarprop 2.0: Modern charge-sign dependent solar modulation for everyone

  • Henning Gast
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Abstract

I present a greatly improved version of SOLARPROP, an open source framework for building models of charge-sign dependent solar modulation of cosmic rays in the heliosphere. The transport equation of charged particles is solved by converting it to a set of stochastic differential equations (SDEs). The code now includes the full set of SDEs in two spatial dimensions, allowing for magnetic fields with latitudinal components, and it has been generalized to accommodate models in three spatial dimensions. Due to code parallelization and optimization, a speedup of up to three orders of magnitude with respect to the original version is achieved. Python bindings have been added for ease of use. The code has been validated against the results of several model calculations found in the literature. While excellent agreement is found in many cases, important differences are observed for two model implementations. Possible root causes for the differences have been investigated.

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Cite

BibTeXRIS
BibTeX
@misc{gast2026solarprop,
  title = {{Solarprop 2.0: Modern charge-sign dependent solar modulation for everyone}},
  author = {Henning Gast},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.01360},
  eprint = {2610.01360},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.01360},
}
RIS
TY  - UNPB
TI  - Solarprop 2.0: Modern charge-sign dependent solar modulation for everyone
AU  - Henning Gast
PY  - 2026
DA  - 2026/10/01
PB  - arXiv
DO  - 10.48550/arxiv.2610.01360
UR  - https://arxiv.org/abs/2610.01360
AB  - I present a greatly improved version of SOLARPROP, an open source framework for building models of charge-sign dependent solar modulation of cosmic rays in the heliosphere. The transport equation of charged particles is solved by converting it to a set of stochastic differential equations (SDEs). The code now includes the full set of SDEs in two spatial dimensions, allowing for magnetic fields with latitudinal components, and it has been generalized to accommodate models in three spatial dimensions. Due to code parallelization and optimization, a speedup of up to three orders of magnitude with respect to the original version is achieved. Python bindings have been added for ease of use. The code has been validated against the results of several model calculations found in the literature. While excellent agreement is found in many cases, important differences are observed for two model implementations. Possible root causes for the differences have been investigated.
ER  -