A Pipeline for the AI-Assisted Analysis of Imaging X-ray Polarimetry Explorer Observations of Black Hole X-ray Binaries and Limits on Their Polarization Variability
Abstract
The Imaging X-ray Polarimetry Explorer (IXPE) has opened a new window into the high-energy Universe, with the capability to measure the arrival time, arrival direction, energy, and, for the first time with the required sensitivity, the polarization of X-rays from black holes, neutron stars, and other cosmic sources of X-rays. In this paper, we discuss the use of Artificial Intelligence (AI) agents for the analysis of IXPE data. The pipeline combines an immutable code base with evolving knowledge databases. The generation of analysis projects makes use of a literature data base to judge the novelty of the proposed analysis projects. We emphasize that the analysis requires two types of expert knowledge: detailed instructions about how to reduce the X-ray data, and expert knowledge about how to interpret the results. We employ several levels of auditors that scrutinize the analysis plan, the analysis scripts, and the analysis results for data and method adequacy. The pipeline includes a staggered approach to interpret the analysis results. We present exemplary results from the pipeline applied to IXPE observations of Black Hole X-ray Binaries (BHXRBs) including comparisons of results from the analysis pipeline with published results and a systematic search for polarization degree and polarization angle variability in long IXPE observations. We critically discuss the performance of the pipeline and future avenues for improving it.
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BibTeX
@misc{domine2026pipeline,
title = {{A Pipeline for the AI-Assisted Analysis of Imaging X-ray Polarimetry Explorer Observations of Black Hole X-ray Binaries and Limits on Their Polarization Variability}},
author = {B. Domine and B. Liu and H. Krawczynski and M. Kundu and Shravan Vengalil Menon and Fang Zhou},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14185},
eprint = {2610.14185},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14185},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A Pipeline for the AI-Assisted Analysis of Imaging X-ray Polarimetry Explorer Observations of Black Hole X-ray Binaries and Limits on Their Polarization Variability
AU - B. Domine
AU - B. Liu
AU - H. Krawczynski
AU - M. Kundu
AU - Shravan Vengalil Menon
AU - Fang Zhou
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.14185
UR - https://arxiv.org/abs/2610.14185
AB - The Imaging X-ray Polarimetry Explorer (IXPE) has opened a new window into the high-energy Universe, with the capability to measure the arrival time, arrival direction, energy, and, for the first time with the required sensitivity, the polarization of X-rays from black holes, neutron stars, and other cosmic sources of X-rays. In this paper, we discuss the use of Artificial Intelligence (AI) agents for the analysis of IXPE data. The pipeline combines an immutable code base with evolving knowledge databases. The generation of analysis projects makes use of a literature data base to judge the novelty of the proposed analysis projects. We emphasize that the analysis requires two types of expert knowledge: detailed instructions about how to reduce the X-ray data, and expert knowledge about how to interpret the results. We employ several levels of auditors that scrutinize the analysis plan, the analysis scripts, and the analysis results for data and method adequacy. The pipeline includes a staggered approach to interpret the analysis results. We present exemplary results from the pipeline applied to IXPE observations of Black Hole X-ray Binaries (BHXRBs) including comparisons of results from the analysis pipeline with published results and a systematic search for polarization degree and polarization angle variability in long IXPE observations. We critically discuss the performance of the pipeline and future avenues for improving it.
ER -