Astronomers have created a galactic masterpiece: an ultra-detailed image that reveals previously unseen features in the Sculptor Galaxy. Using the European Southern Observatory’s Very Large Telescope (ESO’s VLT), they observed this nearby galaxy in thousands of colors simultaneously. By capturing vast amounts of data at every single location, they created a galaxy-wide snapshot of the lives of stars within Sculptor.

“Galaxies are incredibly complex systems that we are still struggling to understand,” says ESO researcher Enrico Congiu, who led a new Astronomy & Astrophysics study on Sculptor. Reaching hundreds of thousands of light-years across, galaxies are extremely large, but their evolution depends on what’s happening at much smaller scales.

“The Sculptor Galaxy is in a sweet spot,” says Congiu. “It is close enough that we can resolve its internal structure and study its building blocks with incredible detail, but at the same time, big enough that we can still see it as a whole system.”

A galaxy’s building blocks — stars, gas and dust — emit light at different colors. Therefore, the more shades of color there are in an image of a galaxy, the more we can learn about its inner workings. While conventional images contain only a handful of colors, this new Sculptor Galaxy image is rendered in thousands of colors, revealing intricate details that would have been lost otherwise.

This extraordinary image not only showcases the beauty and complexity of the Sculptor Galaxy but also serves as a testament to human ingenuity and scientific curiosity. By pushing the boundaries of what we thought was possible with astronomical observations, researchers continue to expand our understanding of the cosmos and inspire new generations of scientists and space enthusiasts alike.

The European Southern Observatory (ESO) enables scientists worldwide to discover the secrets of the Universe for the benefit of all. We design, build and operate world-class observatories on the ground — which astronomers use to tackle exciting questions and spread the fascination of astronomy — and promote international collaboration for astronomy.

Established as an intergovernmental organisation in 1962, today ESO is supported by 16 Member States (Austria, Belgium, Czechia, Denmark, France, Finland, Germany, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, Sweden, Switzerland and the United Kingdom), along with the host state of Chile and with Australia as a Strategic Partner.

ESO’s headquarters and its visitor centre and planetarium, the ESO Supernova, are located close to Munich in Germany, while the Chilean Atacama Desert, a marvelous place with unique conditions to observe the sky, hosts our telescopes. ESO operates three observing sites: La Silla, Paranal and Chajnantor.

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.