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PreprintarXiv licencearXiv

MadStree: automated evaluation of tree-level de Sitter correlators

Abstract

We first derived the formulae of recursive reduction and dlog-form differential equations for arbitrary tree-level cosmological correlator, taking the external relations of massless bulk propagators into consideration. The differential equations could be systematically solved and provide the efficient evaluation of them. We have implemented this methodology as a user-friendly Mathematica package MadStree for computing arbitrary tree-level cosmological correlators.

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BibTeX
@misc{chen2026madstree,
  title = {{MadStree: automated evaluation of tree-level de Sitter correlators}},
  author = {Jiaqi Chen and Yi-Xiao Tao and Xinyi Wang and Dong Zhang},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.28526},
  eprint = {2610.28526},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.28526},
}
RIS
TY  - UNPB
TI  - MadStree: automated evaluation of tree-level de Sitter correlators
AU  - Jiaqi Chen
AU  - Yi-Xiao Tao
AU  - Xinyi Wang
AU  - Dong Zhang
PY  - 2026
DA  - 2026/10/20
PB  - arXiv
DO  - 10.48550/arxiv.2610.28526
UR  - https://arxiv.org/abs/2610.28526
AB  - We first derived the formulae of recursive reduction and dlog-form differential equations for arbitrary tree-level cosmological correlator, taking the external relations of massless bulk propagators into consideration. The differential equations could be systematically solved and provide the efficient evaluation of them. We have implemented this methodology as a user-friendly Mathematica package MadStree for computing arbitrary tree-level cosmological correlators.
ER  -