Astronomy / Astrophysics

Edwin Hubble

Edwin Power Hubble was born on November 20, 1889 in Marshfield, Missouri, USA. The observations he made allowed him to find cepheid variable stars, which could be used to calculate the distances of the nebulae that host them and to prove that they were outside the Milky Way, contradicting the theory that was then more highly regarded. Other studies by Edwin Hubble concerned galaxies’ redshift.

An impact in the asteroid belt might have triggered the Great Ordovician Biodiversity Event on Earth

An article published in the journal “Science Advances” reports a study linking the fragmentation of an asteroid between Mars and Jupiter about 466 million years ago to what is known as the Great Ordovician Biodiversity Event (GOBE). A team of researchers led by geologist Birger Schmitz of Lund University, Sweden, analyzed micrometeorites from that era. The ones of L-type chondrite type that can be linked to the fragments of that asteroid date back to the time of the ice age that marked the beginning of that biodiversity.

Screenshot from an IllustrisTNG simulation (Image courtesy IllustrisTNG project)

An article published in the journal “Computational Astrophysics and Cosmology” presents the complete public release of the simulations coded as TNG100 and TNG300 of the IllustrisTNG project, a highly sophisticated simulation of the universe that has been improved over years of work. A team of researchers kept on improving its details and functionalities also by developing new interaction and exploration 2D and 3D tools to simulate two cubes of space of 100 and 300 million parsec side length.

Deep learning used to map storms on the planet Saturn

An article published in the journal “Nature Astronomy” reports the application of a deep learning algorithm to recognize storms on the planet Saturn creating a map of their components and characteristics. Ingo Waldmann of the University College London, England, and Caitlin Griffith of the University of Arizona, USA, developed the algorithm called PlanetNet based on the TensorFlow “engine” to analyze data collected by the Cassini space probe with an efficiency higher than traditional techniques thanks to the recognition of recurrent characteristics across various multiple data sets. PlanetNet could be adapted to analyze observations of other planets.

Neural networks to predict the mass of planets in formation

An article published in the journal “Astronomy and Astrophysics” shows an example of the use of a neural network and a deep learning algorithm to reduce the time to create simulations of star systems formation obtaining even better results. Yann Alibert of NCCR PlanetS and of the Swiss University of Bern and Julia Venturini of the International Space Science Institute (ISSI) of Bern and a PlanetS collaborator developed this new system that predicts the mass of a planet starting from the conditions in which it formed with a excellent accuracy and a much higher speed than models based on differential equations.