SPECULATE: A synthetic spectral library, emulator and fitting package for accretion disc winds -- Infrastructure and application to CVs
Abstract
Disc winds are critical components of accreting astrophysical systems and their observations can yield a wide variety of spectral line profiles. Yet, inferring disc wind parameters remains challenging: the underlying parameter spaces are high-dimensional and degenerate, while the radiative-transfer simulations are imperfect and computationally expensive. This paper tackles bridging this gap. Here, we present 'Speculate', an open-source framework for rapidly emulating and fitting observations with synthetic accretion disc wind spectra, generated from the Monte Carlo radiative-transfer code Sirocco. Speculate includes the release of our cataclysmic variable (CV) training data, a library of 88,680 sparsely gridded Sirocco spectra spanning the ultraviolet and optical wave bands, a suite of pre-trained emulators, and its interactive Marimo user interface. Speculate compresses spectral grids using principal component analysis and then provides three approaches to interpolate the resulting latent representation: with a regular-grid interpolation, a neural network, or a Gaussian process. All three architectures enable Speculate to generate a Sirocco spectrum between 1−40 ms, thereby enabling efficient inference. The first two support rapid χ2 optimisation. The Gaussian process utilises a Bayesian inference engine coupled with a covariance-aware, flexible likelihood function, adapted and accelerated from the Starfish framework. This function helps absorb correlated discrepancies between simulations and data. Tests show all architectures reproduce unseen Sirocco spectra with broadly comparable accuracy and can recover their physical parameters. Subsequently, we apply Speculate to 5 representative nova-like CV spectra, reproducing several observed line features while also exposing mismatches where the current model grid omits relevant physics.
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BibTeX
@misc{wallis2026speculate,
title = {{SPECULATE: A synthetic spectral library, emulator and fitting package for accretion disc winds -- Infrastructure and application to CVs}},
author = {Austen G. W. Wallis and Christian Knigge and James H. Matthews and Knox S. Long and Madeleine-Mai Ward and Stuart A. Sim and Adam B. Hill and Amin Mosallanezhad and Nicolas Scepi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.07364},
eprint = {2610.07364},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.07364},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - SPECULATE: A synthetic spectral library, emulator and fitting package for accretion disc winds -- Infrastructure and application to CVs
AU - Austen G. W. Wallis
AU - Christian Knigge
AU - James H. Matthews
AU - Knox S. Long
AU - Madeleine-Mai Ward
AU - Stuart A. Sim
AU - Adam B. Hill
AU - Amin Mosallanezhad
AU - Nicolas Scepi
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.07364
UR - https://arxiv.org/abs/2610.07364
AB - Disc winds are critical components of accreting astrophysical systems and their observations can yield a wide variety of spectral line profiles. Yet, inferring disc wind parameters remains challenging: the underlying parameter spaces are high-dimensional and degenerate, while the radiative-transfer simulations are imperfect and computationally expensive. This paper tackles bridging this gap. Here, we present 'Speculate', an open-source framework for rapidly emulating and fitting observations with synthetic accretion disc wind spectra, generated from the Monte Carlo radiative-transfer code Sirocco. Speculate includes the release of our cataclysmic variable (CV) training data, a library of 88,680 sparsely gridded Sirocco spectra spanning the ultraviolet and optical wave bands, a suite of pre-trained emulators, and its interactive Marimo user interface. Speculate compresses spectral grids using principal component analysis and then provides three approaches to interpolate the resulting latent representation: with a regular-grid interpolation, a neural network, or a Gaussian process. All three architectures enable Speculate to generate a Sirocco spectrum between 1−40 ms, thereby enabling efficient inference. The first two support rapid χ^2 optimisation. The Gaussian process utilises a Bayesian inference engine coupled with a covariance-aware, flexible likelihood function, adapted and accelerated from the Starfish framework. This function helps absorb correlated discrepancies between simulations and data. Tests show all architectures reproduce unseen Sirocco spectra with broadly comparable accuracy and can recover their physical parameters. Subsequently, we apply Speculate to 5 representative nova-like CV spectra, reproducing several observed line features while also exposing mismatches where the current model grid omits relevant physics.
ER -