The distribution of stellar mass ratios, q, in binary systems provides critical insights into the dynamical history and star-formation processes of open clusters. Here, I re-evaluate the recently published mass ratio distribution (MRD) of a sample of spectroscopic binaries in the Hyades cluster, which was based on stellar positions in colour-magnitude diagrams. I demonstrate that the mass ratios derived in that work are statistically inconsistent with a random distribution of orbital inclinations. Furthermore, several systems yielded non-physical results. By applying a Richardson-Lucy deconvolution to the spectroscopic mass functions and assuming a random distribution of inclinations, I re-derive the MRD for this sample, showing that it is statistically different from a uniform distribution. I further find a significant dependence on the primary mass: systems with lower-mass primaries exhibit a peak near q~1, whereas more massive primaries show a distribution heavily skewed toward low mass ratios (q~0.15). These findings highlight the potential pitfalls of photometric mass ratio derivations and underscore the need for further verification with future data releases such as Gaia DR4.
We investigate the transferability of Earth weather foundation models to planetary atmospheres by adapting the GraphCast graph neural weather forecasting model to Mars. While GraphCast achieves state-of-the-art performance for terrestrial forecasting, its applicability to non-Earth environments remains unexplored. Using the Mars Climate Database (MCD), which provides global atmospheric fields across vertical altitude levels (similar to Earth pressure levels), we evaluate zero-shot and fine-tuned GraphCast predictions of Martian temperature and wind fields. Zero-shot forecasts produce a surprisingly accurate depiction of current conditions but fail to reproduce diurnal variability and rapidly decay toward climatological mean states. To address this limitation, we fine-tune GraphCast using MCD variables and top-of-atmosphere solar radiation forcing while holding humidity constant. Fine-tuning enables rapid learning of Martian thermal variability. Within as few as 10 training epochs, the model begins to capture the diurnal cycle and forecasts up to 10 days reproduce seasonal and vertical temperature structure. Prediction quality improves with training sample size and exhibits sensitivity to seasonal initialization. These results demonstrate that Earth-trained AI weather models can be adapted to simulate Martian atmospheric dynamics, providing a pathway toward rapid planetary weather prediction to support mission operations, dust storm risk mitigation, and future human exploration.