Max-Planck-Institut für Radioastronomie

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

Hi all! I’m very happy to share I’ve spent an awesome internship in pulsar astronomy at Max-Planck-Institut für Radioastronomie 🤩 it was a blast to be there 🥰 special thanks to my amazing supervisors Huanchen Hu and Nataliya Porayko and the whole team for making this time so unforgettable for me 💛 I want to use this opportunity to give you a short introduction to pulsars 💫 When a star dies, it can end up in a neutron star or in a black hole, depending on its mass. When it will get a neutron star, all its mass collapses into a muuuch smaller volume. And stars are always rotating. When you imagine an ice skater that pulls in his arm while rotating, he will rotate faster ⛸️ And the same happens to the neutron star! It speeds up and rotates extremely fast. When this rapidly rotating star emits light beams, it is a pulsar, since an observer on Earth sees frequently pulses, when a light beam crosses the axis of view 💫 Thus, we can conclude pulsars are very small and dense objects in the sky that rotate very rapidly and emit highly polarized light beams (often radio beams, but sometimes also X-ray and so on). Pulsars can occur alone or with a companion as binary pulsar. Meanwhile, we’ve found many pulsars. Making a map of pulsars looks like making a map of our galaxy! (https://lnkd.in/edVXJaSR) 🗺️ During my time at the institute, I worked with the observations of a very special pulsar: J0332+5434, located in the constellation Camelopardalis (giraffe). It is one of the brightest objects in the northern hemisphere that can be observed in radio spectrum ✨ I look forward to telling you more about my work in the following posts!