SIMBAD references

Understanding the variability of Sagittarius A* (Sgr A*) requires coordinated, multiwavelength observations that span the electromagnetic spectrum. In this work, we focus on data from four key observatories: Chandra in the X-ray (2-8 keV), GRAVITY on the Very Large Telescope in the near-infrared (2.2 μm), Spitzer in the infrared (4.5 μm), and the Atacama Large Millimeter/submillimeter Array in the submillimeter (340 GHz). These multiband observations are essential for probing the physics of accretion and emission near the black hole's event horizon, yet they suffer from irregular sampling, band-dependent noise, and substantial data gaps. These limitations complicate efforts to robustly identify flares and measure cross-band time lags, key diagnostics of the physical processes driving variability. To address this challenge, we introduce a diffusion-based generative model, for interpolating sparse, multivariate astrophysical time series. This represents the first application of score-based diffusion models to astronomical time series. We also present the first transformer-based model for light-curve reconstruction that includes calibrated uncertainty estimates. The models are trained on simulated light curves constructed to match the statistical and observational characteristics of real Sgr A* data. These simulations capture correlated multiband variability, realistic observation cadences, and wavelength-specific noise. We compare our models against a multi-output Gaussian process. The diffusion model achieves superior accuracy and competitive calibration across both simulated and real datasets, demonstrating the promise of diffusion models for high-fidelity, uncertainty-aware reconstruction of multiwavelength variability in Sgr A*.