Sophie Stucki | Searcharxiv

arXiv2026

Decoding the Radial Velocity Signatures of Solar Faculae with 3D MHD Simulations

We model the solar radial velocity (RV) signal induced by faculae, the dominant contributor to RV variability in Sun-like stars. We use a representative case of a facular patch transiting the visible solar disk as the Sun rotates to disentangle various physical effects contributing to the RV signal. Our approach is based on 3D radiative magnetohydrodynamic (MHD) simulations of the solar photosphere and upper convection zone with the MURaM code and spectral synthesis with the MPS-ATLAS code. We show that the faculae-induced RV strongly depends on the facular position on the solar disk. Near disk centre, facular magnetic fields inhibit the convective blueshift and thus produce a relative redshift of the solar spectrum. Surprisingly, when located closer to the limb, namely at heliocentric angles greater than about $60^\circ$, faculae produce a relative blueshift. This transition from redshift to blueshift is caused by the effect of magnetic fields on horizontal flows, which dominate the signal near the limb, and on the visibility of these flows. In combination with solar rotation, this centre-to-limb dependence of the facular effect leads to a complex RV profile during the facular transit and, in particular, to a phase lag between the maximum of the RV signal and the facular crossing of the central meridian. We further show that, in contrast to stellar reflex motion, the facular signal strongly depends on the spectral line in which it is measured.

astro-ph.SR↗

arXiv2026

Mitigating stellar radial velocity jitter using orthogonal activity indices and a time-aware neural network

Despite recent advances in the precision of high-resolution spectrographs, the detection of Earth-like exoplanets is still limited by the effects of stellar activity, which introduce radial velocity variations at the metre-per-second level or larger. We present a framework to disentangle stellar effects from planetary signals by exploiting high-order distortions of the cross-correlation function (CCF; a measure of the average spectral line profile), thus moving beyond the commonly applied Gaussian fit approximation. We decomposed the CCF using a Gram-Schmidt orthogonal basis function, enabling the separation of pure line shifts from line-shape distortions. To model activity-induced contributions to the radial velocities, we have developed a time-aware convolutional attention network dubbed CANSTAR. This network was trained on synthetic line-shape distortion coefficients produced with the realistic stellar simulator StarSim to learn the temporal evolution of stellar activity features. We validated our framework using HARPS and CARMENES observations of two active stars, $ε$ Eridani and TZ Arietis. The network effectively mitigates stellar activity, reducing the radial velocity RMS to 52.5 % and 62.4 % of the uncorrected variability, respectively. This correction enables a more precise determination of the orbital parameters of TZ Arietis b compared to a Gaussian process regression. Our results demonstrate that neural networks that incorporate the temporal context can outperform state-of-the-art methods in complex activity regimes. Future improvements on StarSim that will allow us to train CANSTAR on 3D magnetohydrodynamic spectra and more complex instrumental modelling are expected to bridge the performance gap between synthetic and real data, offering a robust pathway towards detecting Earth-mass planets around Sun-like stars.

astro-ph.EP↗