The Apollo astronauts stumbled upon an unexpected treasure on the lunar surface – tiny, bright orange glass beads that had been frozen in time for billions of years. These minuscule, 1mm-wide capsules hold secrets about the moon’s explosive past, revealing a chapter of volcanic eruptions that shaped the satellite’s history.

Researchers led by Thomas Williams, Stephen Parman, and Alberto Saal from Brown University, in collaboration with WashU scientists, have employed cutting-edge techniques to study these ancient artifacts. Using instruments like NanoSIMS 50, atom probe tomography, scanning electron microscopy, transmission electron microscopy, and energy dispersive X-ray spectroscopy, they have gained unprecedented insights into the surface of the beads.

Each glass bead is a testament to the moon’s volcanic activity, where lava droplets solidified instantly in the cold vacuum surrounding the satellite. The colors, shapes, and chemical compositions of these tiny minerals are unlike anything found on Earth, serving as probes into the pressure, temperature, and chemical environment of lunar eruptions 3.5 billion years ago.

The study reveals that the style of volcanic eruptions changed over time, much like reading the journal of an ancient lunar volcanologist. These findings not only shed light on the moon’s past but also demonstrate the importance of preserving samples for future generations, as technology advances and new instruments become available to uncover hidden secrets.

The Milky Way’s central region has long been a subject of fascination for astronomers, but recent research led by Dr. James De Buizer at the SETI Institute and Dr. Wanggi Lim at IPAC at Caltech has revealed a surprising finding: massive star formation is occurring in this area at a lower rate than expected. The study primarily relied on observations from NASA’s retired SOFIA airborne observatory, focusing on three star-forming regions – Sgr B1, Sgr B2, and Sgr C – located at the heart of the Galaxy.

Contrary to previous assumptions that star formation is likely depressed near the Galactic Center, these areas have been found to produce stars with relatively low masses. Despite their dense clouds of gas and dust, conditions typically conducive to forming massive stars, these regions struggle to create such high-mass stars. Furthermore, they appear to lack sufficient material for continued star formation, suggesting that only one generation of stars is produced.

The researchers discovered over 60 presently-forming massive stars within the Galactic Center regions, but found that these areas formed fewer stars and topped out at lower stellar masses than similar-sized regions elsewhere in the Galaxy. The team’s study also suggested that extreme conditions in the Galactic Center, such as its rapid rotation and interaction with older stars and material falling towards the black hole, might be inhibiting gas clouds from forming stars.

However, Sgr B2 was found to be an exception among the studied areas, maintaining a reservoir of dense gas and dust despite having an unusually low rate of present massive star formation. The researchers proposed that this region may represent a new category of stellar nursery or challenge traditional assumptions about giant H II regions hosting massive star clusters.

The study’s findings have significant implications for our understanding of star formation in the Milky Way, highlighting the importance of continued research into the complex dynamics at play within the Galactic Center.

The research team leveraged high-throughput computing capabilities provided by the Center for High Throughput Computing (CHTC) to automate computing tasks across a network of thousands of computers. This innovation allowed them to analyze millions of simulations, making it possible to extract new insights from the data behind the Event Horizon Telescope images of black holes.

The neural network was trained on synthetic data files generated by CHTC, enabling the researchers to make a better comparison between the EHT data and models. The analysis revealed that the emission near the black hole is mainly caused by extremely hot electrons in the surrounding accretion disk, rather than a jet. Additionally, the magnetic fields in the accretion disk appear to behave differently from usual theories of such disks.

Lead researcher Michael Janssen stated that defying prevailing theory is exciting but sees their AI and machine learning approach as a first step towards further improvement and extension of associated models and simulations. The research has significant implications for our understanding of black holes and the cosmos, and it will be interesting to see how this knowledge evolves in the future.

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An international team of astronomers has made groundbreaking discoveries about the black hole at the center of our Milky Way using a neural network. By analyzing millions of synthetic simulations generated by the Center for High Throughput Computing (CHTC), they found that the black hole is spinning at nearly top speed, with its rotation axis pointing towards Earth.

The research team published their findings in three papers in Astronomy & Astrophysics, providing new insights into the behavior of black holes. The neural network was trained on synthetic data files generated by CHTC, enabling the researchers to make a better comparison between the Event Horizon Telescope (EHT) data and models.

Previous studies by the EHT Collaboration used only a handful of realistic synthetic data files, but the Madison-based CHTC enabled the astronomers to feed millions of such data files into a so-called Bayesian neural network. This allowed them to extract as much information as possible from the data and make a more accurate comparison with the models.

The researchers found that the emission near the black hole is mainly caused by extremely hot electrons in the surrounding accretion disk, rather than a jet. Additionally, the magnetic fields in the accretion disk appear to behave differently from usual theories of such disks.

Lead researcher Michael Janssen stated that defying prevailing theory is exciting but sees their AI and machine learning approach as a first step towards further improvement and extension of associated models and simulations. The research has significant implications for our understanding of black holes and the cosmos, and it will be interesting to see how this knowledge evolves in the future.

The Event Horizon Telescope project performed more than 12 million computing jobs in the past three years, using the Open Science Pool operated by PATh. This pool offers computing capacity contributed by more than 80 institutions across the United States, making it an ideal platform for large-scale simulations like those used in this research.

Scientific papers referenced

* Deep learning inference with the Event Horizon Telescope I: Calibration improvements and a comprehensive synthetic data library. By: M. Janssen et al. In: Astronomy & Astrophysics, 6 June 2025.
* Deep learning inference with the Event Horizon Telescope II: The Zingularity framework for Bayesian artificial neural networks. By: M. Janssen et al. In: Astronomy & Astrophysics, 6 June 2025.
* Deep learning inference with the Event Horizon Telescope III: Zingularity results from the 2017 observations and predictions for future array expansions. By: M. Janssen et al. In: Astronomy & Astrophysics, 6 June 2025.