The biases caused by interpolated table models and their impact on X-ray spectroscopy
Abstract
Models for X-ray spectroscopy involving complex relativistic or plasma physics are commonly pre-computed on a grid, In this paper, we aim to quantify the potential biases and distortions introduced by the computational error and propagated into parameter inference by interpolating linearly these complex, non-linear models. We analyse the performance of Bayesian sampling algorithms on mock inference problems. Then, we model real observations of an accreting black hole X-ray binary in both the soft and hard states. We find that linear interpolation can a) greatly bias the posterior distribution of model parameters and b) distort the likelihood space to such a degree that posterior distributions become artificially multimodal. This multi-modality causes a noticeable loss in computational efficiency in both sampling and fitting techniques. As a result, exploring a given parameter space until convergence requires an extremely large computational cost. These findings call for improving the computational techniques used when fitting modern X-ray data, for example through better sampling algorithms and/or more sophisticated interpolation schemes
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BibTeX
@misc{lucchini2026biases,
title = {{The biases caused by interpolated table models and their impact on X-ray spectroscopy}},
author = {Matteo Lucchini and Benjamin Ricketts and Phil Uttley and Daniela Huppenkothen and Fergus Baker and Gregoire Marcel and Guglielmo Mastroserio},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35174},
eprint = {2610.35174},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35174},
}
RIS
TY - UNPB
TI - The biases caused by interpolated table models and their impact on X-ray spectroscopy
AU - Matteo Lucchini
AU - Benjamin Ricketts
AU - Phil Uttley
AU - Daniela Huppenkothen
AU - Fergus Baker
AU - Gregoire Marcel
AU - Guglielmo Mastroserio
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.35174
UR - https://arxiv.org/abs/2610.35174
AB - Models for X-ray spectroscopy involving complex relativistic or plasma physics are commonly pre-computed on a grid, In this paper, we aim to quantify the potential biases and distortions introduced by the computational error and propagated into parameter inference by interpolating linearly these complex, non-linear models. We analyse the performance of Bayesian sampling algorithms on mock inference problems. Then, we model real observations of an accreting black hole X-ray binary in both the soft and hard states. We find that linear interpolation can a) greatly bias the posterior distribution of model parameters and b) distort the likelihood space to such a degree that posterior distributions become artificially multimodal. This multi-modality causes a noticeable loss in computational efficiency in both sampling and fitting techniques. As a result, exploring a given parameter space until convergence requires an extremely large computational cost. These findings call for improving the computational techniques used when fitting modern X-ray data, for example through better sampling algorithms and/or more sophisticated interpolation schemes
ER -