Exploring Dynamics of Photospheric Spectral Lines for Upward-Propagating Acoustic Waves in Realistic 3D RHD Simulations of the Sun
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Turbulent Transport: Plays a critical role in astrophysical plasmas, such as the solar interior, spanning multiple scales and challenging traditional modeling approaches. Objective: Develop machine learning (ML) models—MLP and CNN—to predict subgrid Reynolds stress tensors from StellarBox 3D simulations of the solar atmosphere. Benchmarking: Compare ML-driven models against physics-based Gradient and Smagorinsky approaches.
Combining the near-daily Ozone Monitoring Instrument (OMI) and Tropospheric Monitoring Instrument (TROPOMI) measurements of solar spectra, we construct line indices (line-core to line-flanks ratios) for various transitions (mainly Fe I) in the 300–2390 nm spectral domain. The indices are supplemented by the wavelength-binned fluxes from OMI and Total and Spectral Solar Irradiance Sensor (TSIS-1). To study the short-term (solar-rotational) patterns, we normalize the indices and fluxes to the minimum-activity epoch, then de-trend them with 81 day running means. Comparisons of the de-trended TSIS-1 and OMI fluxes with the NASA-NOAA-LASP SSI (NNLSSI1) model show excellent agreement, to (0.5–2.2) × 10 -4 in the normalized and de-trended data. The data are subjected to a multiregression analysis against quantities representing the facular brightening and the sunspot darkening. The de-trended line indices and average fluxes show different sensitivities to these two solar magnetic-activity manifestations, with the fluxes being far more susceptible to the sunspot component. The de-trended line indices experience a rapid drop of activity levels towards longer wavelengths, albeit with a large rebound in the short-wave infrared (SWIR) domain that is caused by the ubiquitous, temperature-sensitive CO transitions. The wavelength-dependent activity also falls, however much slower, in the de-trended average fluxes. Qualitatively similar behavior is captured by semiempirical models.
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