ELISA: A Bayesian X/γ-ray Spectral Analysis Toolkit

@misc{xue2026elisa,
  title = {{ELISA: A Bayesian X/$γ$-ray Spectral Analysis Toolkit}},
  author = {Wang-Chen Xue and Sheng-Lun Xie and Chao Zheng and Shao-Lin Xiong and Xiao-Bo Li and Yong Chen and Peng Zhang and Yan-Qiu Zhang and Jia-Cong Liu and Chen-Wei Wang and Wen-Jun Tan and Yue Wang and Jin-Peng Zhang and Zheng-Hang Yu},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.14587},
  eprint = {2610.14587},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.14587},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - ELISA: A Bayesian X/γ-ray Spectral Analysis Toolkit
AU  - Wang-Chen Xue
AU  - Sheng-Lun Xie
AU  - Chao Zheng
AU  - Shao-Lin Xiong
AU  - Xiao-Bo Li
AU  - Yong Chen
AU  - Peng Zhang
AU  - Yan-Qiu Zhang
AU  - Jia-Cong Liu
AU  - Chen-Wei Wang
AU  - Wen-Jun Tan
AU  - Yue Wang
AU  - Jin-Peng Zhang
AU  - Zheng-Hang Yu
PY  - 2026
DA  - 2026/10/10
PB  - arXiv
DO  - 10.48550/arxiv.2610.14587
UR  - https://arxiv.org/abs/2610.14587
AB  - Forward-folded X/γ-ray spectral fitting increasingly benefits from Bayesian workflows to better capture counting statistics, background uncertainties, and complex parameter degeneracies, while fully reproducible end-to-end implementations are still not always straightforward to deploy in routine analyses. We present ELISA (Efficient Library for Spectral Analysis), an open-source Python toolkit that streamlines the full spectral analysis loop for X/γ-ray astronomy: data loading, model construction, inference, and model validation and comparison. ELISA couples a JAX-accelerated forward model with a unified fitting interface, supporting both rapid optimization and modern Bayesian sampling. Built-in diagnostics make convergence checks and predictive assessment a standard part of the workflow, motivating principled model refinement when needed. Using a spectral fitting example of the Insight-HXMT Crab observation, we find substantial improvements in sampling efficiency compared with XSPEC's default MCMC implementation. ELISA is available at https://github.com/wcxve/elisa and on PyPI at https://pypi.org/project/astro-elisa.
ER  -