Predicting the existence of heavy nuclei using machine learning

A collaboration between the Facility for Rare Isotope Beams (FRIB) and the Department of Statistics and Probability (STT) at Michigan State University (MSU) estimated the boundaries of nuclear existence by applying statistical ...

Theories describe dynamically disordered solid materials

Theoretical physicists at Linköping University have developed a computational method to calculate the transition from one phase to another in dynamically disordered solid materials. This is a class of materials that can ...

Discovering new particles using black holes

Some theories that go beyond the Standard Model of particle physics predict the existence of new ultralight particles, with masses far below the lightest known particles in nature. These particles have such very weak interactions ...

International team of physicists continues search for new physics

Dark matter, which is thought to account for nearly a quarter of matter in the universe (but has yet to be observed), has perplexed physicists for decades. They're constantly looking for something surprising to show up in ...

Computer simulation sheds new light on colliding stars

Unprecedented detail of the aftermath of a collision between two neutron stars depicted in a 3-D computer model created by a University of Alberta astrophysicist provides a better understanding of how some of the universe's ...

A model for describing the hydrodynamics of crowds

Precise simulations of the movement and behavior of crowds can be vital to the production of digital sequences or the creation of large structures for crowd management. However, the ability to quantitatively predict the collective ...

Producing four top quarks at once to explore the unknown

For several decades, particle physicists have been trying to better understand nature at the smallest distances by colliding particles at the highest energies. While the Standard Model of particle physics has successfully ...

AI speeds up climate computations

Realistic climate simulations require huge reserves of computational power. An LMU study now shows that new algorithms allow interactions in the atmosphere to be modeled more rapidly without loss of reliability.

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