Max-Planck-Institut für Radioastronomie

🌌 Milestone Unlocked: APEX Integrates with the Global mm-VLBI Array! 🌌 Excited to share a milestone in mm-VLBI astronomical observation! The APEX telescope in Chile has now fully joined the Global mm-VLBI Array (GMVA) at the 3mm wavelength—a big step forward for high-resolution space exploration. With the recent addition of a 3mm receiver at APEX, we achieved a remarkable first: detecting interferometric fringes between APEX and telescopes across the globe. This success, validated by MPIfR’s correlator, was observed during the October 11–13 sessions and confirmed today. 📷 The attached plot shows this achievement, with the detection between APEX and the VLBA Owens Valley telescope in California—a remarkable 8,000 km link. This accomplishment marks a significant advance in our journey toward ultra-precise astronomical imaging, enabling us to explore the universe with newfound clarity. Congratulations to the APEX and GMVA teams for their hard work and dedication! 🛰️✨ #Astronomy #APEX #GMVA #VLBI #SpaceExploration #Milestone

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🎓 Du hast deinen Schulabschluss (demnächst) in der Tasche? 🛠 Eine Werkstatt ist genau die richtige Arbeitsumgebung für dich? ⚙ Du findest, Metall ist ein spannender Werkstoff? Und wie klingt das für dich: Ermögliche mit deiner Arbeit, dass die Geheimnisse des Universums gelüftet werden können. ✨ Passt für dich? Dann bewirb dich für eine Ausbildung zur Industriemechanikerin/zum Industriemechaniker Fachrichtung Feingerätebau bei uns! 📅 Für den Standort Bonn kannst du dich bis zum 08.09.2024 bewerben: https://lnkd.in/e9AdBgXy 📅 Für den Standort Effelsberg kannst du dich bis zum 29.09.2024 bewerben: https://lnkd.in/eDJ7mvZY Wir freuen uns auf deine Bewerbung!

APEX is now part of the Global mm-VLBI Array!  After weeks of preparation, we finally got the data stable in phase and recording radio interferometric scans over the last few hours with the brand new receive constructed with this purpose. We're now observing regularly in the GMVA session 2024/II, together with about 20 antennas around the world. We're waiting for positive fringe detection with other telescopes at the MPIfR correlator in Bonn. When that happens, we'll know we've done it! See two images, one taken this morning during the final preparations, and on-screen from the observation of quasar 3C345. Thrilled to be playing a part in making some exciting new discoveries. #APEX #GMVA #mmVLBI #MPIfR

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” This year’s two Nobel Prize laureates in physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures. When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through connections that can be likened to synapses and which can be made stronger or weaker. The network is trained, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward. John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with. Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning. Learn more Press release: https://bit.ly/4gCTwm9 Popular information: https://bit.ly/3Bnhr9d Advanced information: https://bit.ly/3TKk1MM