Abstract
When interpreting spectropolarimetric observations of the solar atmosphere, wavelength variations of the emergent intensity and polarization translate into information on the depth stratification of physical parameters. We aim to quantify how the information content contained in a representative set of polarized spectra depends on the spectral resolution and spectral sampling. We use a state-of-the-art numerical simulation of a sunspot to synthesize polarized spectra of magnetically sensitive neutral iron lines. We then apply various degrees of spectral degradation to the synthetic spectra and analyze the impact on its dimensionality using PCA and wavelet decomposition. Finally, we apply the SIR code to the degraded synthetic data, to assess the effect of spectral resolution on the inferred parameters. We find that regions with strong magnetic fields where convection is suppressed produce less complex Stokes profiles. On the other hand, regions with strong gradients give rise to more complex Stokes profiles that are more affected by spectral degradation. The degradation also makes the inversion problem more ill-defined, so inversion models with a larger number of free parameters overfit and give wrong estimates. The impact of spectral degradation depends on multiple factors, including spectral resolution, noise level, line spread function (LSF) shape, complexity of the solar atmosphere, and the degrees of freedom in our inversion methods. Having a finely sampled spectrum may be more beneficial than achieving a higher signal-to-noise ratio per wavelength bin. Considering the inclusion of different spectral lines that can counter these effects, and calibrating the effective degrees of freedom in modeling strategies, are also important considerations. These strategies are crucial for the accurate interpretation and have the potential to offer more cost-effective solutions.
