Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the question not just of whether they fit, but of whether they are wrong in ways we did not anticipate: the unknown unknowns. This challenge of model misspecification is not unique to machine learning. In physics, misspecification is sometimes exactly what we want to find: new discoveries appear as failures of existing models. At other times, we want such effects absorbed into the analysis without biasing the measurement. A robust analysis is one that absorbs the misspecifications we are not interested in, while preserving sensitivity to the ones we are. Machine learning can both amplify misspecification and provide new tools to address it. We discuss the challenges of model misspecification, diagnostics for detecting it, and strategies for mitigation. No single diagnostic can confirm that a model is correctly specified: detection and mitigation are two halves of an iterative loop, in which a battery of complementary diagnostics is applied, the model is updated, and the process repeated. Robustness against unknown unknowns is ultimately less about any single technique than about a disposition: a willingness to suspect one's own model, and to design analyses that can survive being wrong in ways one did not anticipate.
Acoustic mode frequencies in the Sun and Sun-like stars change due to magnetic activity, on time-scales much larger than the star's rotation and much smaller than its evolution. Given the poor S/N of the observed stellar p-modes, it is challenging to measure the changes of individual mode frequencies. Typically, power spectra of different time series segments are cross-correlated to estimate a mean p-mode frequency change, which ends up averaging over the individual mode contributions. We seek to enhance the cross-correlation method, by introducing a novel and computationally cheap method, thus enabling us to disentangle p-mode frequency changes for different spherical harmonic degree $\ell$. Assuming that the inclination angle and rotation rate are already measured, filters are designed, which enable the isolation of $δω_\ell$, frequency changes of modes with a given $\ell$, while preventing bias creeping in from neighbouring modes. Monte-Carlo simulations are performed to quantify uncertainty in the estimation of $δω_\ell$. We validate our method against well-studied solar data (SOHO/VIRGO and BiSON) and demonstrate its applicability to the solar-like Kepler star KIC 8006161.