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Black Holes Could Hold a Surprising Secret About Our UniverseTake gravity and mix it with quantum mechanics.

Results from two leading dark matter experiments—XENONnT and PandaX-4T—rule out an enigmatic signal detected in 2020 and set new constraints on dark matter particle candidates consisting of light fermions, respectively.

Over the past decade, physicists have repeatedly scrutinized tanks containing tons of liquid xenon, hoping to spot the flashes of light that might indicate a collision between a dark matter particle and a xenon atom (see Viewpoint: Dark Matter Still at Large). Most of these studies were dedicated to detecting so-called weakly interacting massive particles (WIMPs), a leading dark matter candidate with a mass greater than 10 GeV. Now researchers have sifted through a new set of data for a much lighter prize: fermionic dark matter with a mass of a few tens of MeV [1]. Although the team found no signal beyond the expected background level, they have set the strongest constraints yet on models of sub-GeV fermionic dark matter.

The dataset is the first obtained by the PandaX-4T experiment at the China Jinping Underground Laboratory. The PandaX team searched this data for evidence of a beyond-the-standard-model interaction in which a fermionic dark matter particle is absorbed by the nucleus of a xenon atom. After the absorption, the xenon nucleus should recoil while emitting either a neutrino or an antineutrino. The interaction should also cause an energy deposition in the form of photons and electrons, which would register on photodetectors at the ends of the tank. Unlike the scattering of WIMPs, which is predicted to produce a broad-spectrum energy deposition, the absorption by nuclei of fermionic dark matter particles should deposit energy only in a narrow range.

The data collected so far represent the equivalent of exposing 0.6 tons of liquid xenon to hypothetical fermionic dark matter for one year. When PandaX-4T concludes in 2025, it will have achieved a cumulative exposure 10 times greater, generating even stronger constraints on theory.

See how NASA’s DART mission may help us save life on Earth by showing us how to avert a future doomsday from an asteroid striking Earth.

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Joscha Bach is a cognitive scientist focused on cognitive architectures, mental representation, emotion, social modeling, and learning.

Currently the Principal AI Engineer, Cognitive Computing at Intel Labs, having authored the book “Principles of Synthetic Intelligence”, his focus is how to build machines that can perceive, think and learn.

Continue reading “AGI-22 | Joscha Bach — It from no Bit: Basic Cosmology from an AI Perspective” »

Because our universe is so vast, it seems impossible that anything else could exist. According to experts, we may be in a 4-dimensional black hole.

The singularity, an endlessly hot and dense point in space, was the birthplace of our universe. According to scientists like James Beecham at CERN, black holes in our cosmos might be described in the same way that they are in the scientific community.

When enormous stars die and collapse into an impossibly dense mass, they form black holes from which even the smallest amount of light cannot escape. According to NASA, the event horizon is the border in space beyond which no light can leave or any object can return.

Black holes are one of the more well-known features of space, a science fiction staple, and something we still don’t know much about. We do know…

In other words, the study suggests black holes might actually burrow into a kind of multidimensional object called a brane, and give birth to an entirely new universe in another colossally big bang.

A new study has revealed that researchers have used artificial intelligence to create a map that allows them to predict the distribution of dark matter throughout the universe.

The new study has been published in the Astrophysical Journal and shows that researchers have taken a different approach to creating a model of the distribution of dark matter. So far, researchers know that dark matter makes up 80% of the universe, and creating a model of the distribution of dark matter allows cosmologists to construct what is called a “cosmic web”.

With this cosmic web, cosmologists and researchers will be able to see how dark matter impacts the motion of galaxies in the past, present, and future. Researchers in the new study used machine learning, a branch of artificial intelligence, to construct a new model. The AI was fed a large set of galaxy simulations that include galaxies, dark matter, visible matter, and gases.