Lifeboat Foundation

With the upgraded GRAVITY-instrument at the Very Large Telescope Interferometer of the European Southern Observatory, a team of astronomers led by the Max Planck Institute for Extraterrestrial Physics has determined the mass of a black hole in a galaxy only 2 billion years after the Big Bang. With 300 million solar masses, the black hole is actually under-massive compared to the mass of its host galaxy. Researchers suspect what is happening here.

A paper on this work is published in the journal Nature.

In the more local universe, astronomers have observed tight relationships between the properties of galaxies and the mass of the supermassive black holes residing at their centers, suggesting that galaxies and black holes co-evolve. A crucial test would be to probe this relationship at early cosmic times, but for these far-away galaxies, traditional direct methods of measuring the black hole mass are either impossible or extremely difficult.

The notion of time travel has fascinated humans for thousands of years, but it’s always been a work of fiction – until now.

Scientists have discovered evidence of time travel for real, albeit at a microscopic level. Till Bohmer and Thomas Blochowicz are the lead authors of a new study, Time reversibility during the ageing of materials, which is published in Nature Physics.

The research from the two researchers at the Technical University of Darmstadt in Germany focuses on time effectively ‘shuffling’ in the structure of certain materials like glass.

LISA, a collaborative mission between ESA and NASA, aims to detect gravitational waves from space, offering new insights into the cosmos through advanced technology and international cooperation.

The first space-based observatory designed to detect gravitational waves has passed a major review and will proceed to the construction of flight hardware. On January 25, ESA (European Space Agency), announced the formal adoption of LISA, the Laser Interferometer Space Antenna, to its mission lineup, with launch slated for the mid-2030s. ESA leads the mission, with NASA serving as a collaborative partner.

Continue reading “How LISA — A Gravitational Wave Observatory in Space — Will Transform Our Cosmic Understanding” »

Innovative research introduces a practical, model-free method for exploring topological properties in materials, enhancing the scope and efficiency of topological studies.

The branch of mathematics known as topology has become a cornerstone of modern physics thanks to the remarkable – and above all reliable – properties it can impart to a material or system. Unfortunately, identifying topological systems, or even designing new ones, is generally a tedious process that requires exactly matching the physical system to a mathematical model.

Researchers at the University of Amsterdam and the École Normale Supérieure of Lyon have demonstrated a model-free method for identifying topology, enabling the discovery of new topological materials using a purely experimental approach.

Divice recipe for making spiking artificial neurons.

Neurons, which are made of biological tissue, exhibit cognitive properties that can be replicated in various material substrates. To create brain-inspired computational artificial systems, we can construct microscopic electronic neurons that mimic natural systems. In this paper, we discuss the essential material and device properties needed for a spiking neuron, which can be characterized using impedance spectroscopy and small perturbation equivalent circuit elements. We find that the minimal neuron system requires a capacitor, a chemical inductor, and a negative resistance. These components can be integrated naturally in the physical response of the device, instead of built from separate circuit elements. We identify the structural conditions for smooth oscillations that depend on certain dynamics of a conducting system with internal state variables. These state variables can be of diverse physical nature, such as properties of fluids, electronic solids, or ionic organic materials, implying that functional neurons can be built in various ways. We highlight the importance of detecting the Hopf bifurcation, a critical point in achieving spiking behavior, through spectral features of the impedance. To this end, we provide a systematic method of analysis in terms of the critical characteristic frequencies that can be obtained from impedance methods. Thus, we propose a methodology to quantify the physical and material properties of devices to produce the dynamic properties of neurons necessary for specific sensory-cognitive tasks. By replicating the essential properties of biological neurons in electronic systems, it may be possible to create brain-inspired computational systems with enhanced capabilities in information processing, pattern recognition, and learning. Additionally, understanding the physical and material properties of neurons can contribute to our knowledge of how biological neurons function and interact in complex neural networks. Overall, this paper presents a novel approach toward building brain-inspired artificial systems and provides insight into the important material and device considerations for achieving spiking behavior in electronic neurons.

Physicists’ search for a theory that explains all reality in one framework appeared to have stalled. But now they are reinvigorating the hunt by exploring a wild landscape of abstract geometry.

By Michael Brooks

Scientists have been pretty sure that our universe is expanding at a rapid rate, but no one exactly knew how. Now, a new theory suggests that our universe might be expanding by colliding and eventually absorbing “baby” parallel universes.

This theory was published in a science paper called the Journal of Cosmology and Astroparticle Physics in December 2023. In the study, scientists proposed the idea that the expansion of the universe may be a result of it constantly merging with other universes.

Famous physicists have brought to us novel theories that explain the world around us. AI can also do the same if we guide it to do so.

Researchers at the German institute trained an AI model to look into simpler interactions in larger complex systems, much like how physicists do.

The development of a new theory is typically associated with the greats of physics. You might think of Isaac Newton or Albert Einstein, for example. Many Nobel Prizes have already been awarded for new theories.

Researchers at Forschungszentrum Jülich have now programmed an artificial intelligence that has also mastered this feat. Their AI is able to recognize patterns in complex data sets and to formulate them in a physical theory. The findings are published in the journal Physical Review X.

In the following interview, Prof. Moritz Helias from Forschungszentrum Jülich’s Institute for Advanced Simulation (IAS-6) explains what the “Physics of AI” is all about and to what extent it differs from conventional approaches.