Space News & Blog Articles

Dark matter is notoriously difficult to study. It’s essentially invisible to astronomers since it can’t be seen directly. So astronomers rely on effects such as the gravitational lensing of light to map its presence in the universe. That method works well for other galaxies, but not so well for our own. To map dark matter in the Milky Way, we rely mostly on the motions of stars in our galaxy. Since dark matter attracts regular matter gravitationally, the method works well for areas of the galaxy where there are stars. Unfortunately, most of the stars lie along the galactic plane, making it difficult to map dark matter above and below that plane. But a recent study proposes a way to map more of our galaxy’s dark matter using runaway stars.

Looking to the future, astronomers are excited to see how machine learning – aka. deep learning and artificial intelligence (AI) – will enhance surveys. One field that is already benefitting in the search for extrasolar planets, where researchers rely on machine-learning algorithms to distinguish between faint signals and background noise. As this field continues to transition from discovery to characterization, the role of machine intelligence is likely to become even more critical.

Mercury is the speed champion in our Solar System. It orbits the Sun every 88 days, and its average speed is 47 km/s. Its average distance from the Sun is 58 million km (36 million mi), and it’s so fast it’s named after Mercury, the wing-footed God.

The State of Washington and the Province of BC are in a state of emergency following days of severe wind, rain, and flooding. The situation began when an “atmospheric river” (a plume of moisture) extended over the Pacific Northwest, triggering severe rainfall that caused already-rising rivers to overflow. This led to blocked roads, mudslides, fallen bridges, and thousands of animals drowning in farmland areas.