Black holes are arguably among the most mysterious objects that  Einstein’s General Relativity predicts. They remain more fascinating than ever because they are often found at the core of the paradoxes between quantum mechanics and general relativity.  The resolution of these paradoxes lead theoretical physicists to formulate deep conjectures about the fundamental structures of the underlying quantum theory of gravity. One of the most important of  such conjectures, known as the “Weak gravity Conjecture”, was proposed in this context  in 2006 by Arkani-Hamed and collaborators. The simplest version of this conjecture states that the charge-to-mass ratio of electrically charged black holes, at the end stage of their Hawking’s evaporation, must be larger than one (Q/M>1 in Planck units) in any possible instance of a universe ruled by quantum mechanics. Colloquially, the conjectures says that gravity must necessarily  be the weakest long-range force.

In a recent paper, to appear on the journal Physical Review Letters (preprint avaiable at https://arxiv.org/abs/1902.03250 and published in https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.123.251103), B. Bellazzini of the IPhT and his collaborators have finally provided a rigorous proof of this conjecture. This important result was obtained via an  ingenious “gedanken experiment” where photons are scattered in an universe where one of the space dimensions has been compactified on a circle, even thought eventually  the results  carry over our usual 4-dimensional spacetime. Establishing quantum properties of black holes corresponds to setting solid foundation on the fundamental properties of quantum gravity. Moreover, the same "gedanken" experiment was also used  in the same paper to study dark energy and other modified gravity theories of gravity that aim at explaining the acceleration of the universe.

Black holes are arguably among the most mysterious objects that  Einstein’s General Relativity predicts. They remain more fascinating than ever because they are often found at the core of the paradoxes between quantum mechanics and general relativity.  The resolution of these paradoxes lead theoretical physicists to formulate deep conjectures about the fundamental structures of the underlying quantum theory of gravity.

One of the most important of  such conjectures, known as the “Weak gravity Conjecture”, was proposed in this context  in 2006 by Arkani-Hamed and collaborators. The simplest version of this conjecture states that the charge-to-mass ratio of electrically charged black holes, at the end stage of their Hawking’s evaporation, must be larger than one (Q/M>1 in Planck units) in any possible instance of a universe ruled by quantum mechanics. Colloquially, the conjectures says that gravity must necessarily  be the weakest long-range force.

In a recent paper, to appear on the journal Physical Review Letters (preprint avaiable at https://arxiv.org/abs/1902.03250), B. Bellazzini of the IPhT and his collaborators have finally provided a rigorous proof of this conjecture. This important result was obtained via an  ingenious “gedanken experiment” where photons are scattered in an universe where one of the space dimensions has been compactified on a circle, even thought eventually  the results  carry over our usual 4-dimensional spacetime. Establishing quantum properties of black holes corresponds to setting solid foundation on the fundamental properties of quantum gravity.

Moreover, the same gedanken experiment was also used  in the same paper to study dark energy and other modified gravity theories of gravity that aim at explaining the acceleration of the universe.

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. Physical sciences are no exception. An article in Reviews of Moderns Physics co-authored by Lenka Zdeborová from IPhT (https://journals.aps.org/rmp/recent), covers the recent research on the interface between machine learning and physical sciences. It reviews conceptual developments in machine learning motivated by physical insights, as well as applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. It presents basic notion of machine learning methods and principles, subsequently it describes examples of how statistical physics is used to understand methods in machine learning. It then moves to describe applications of machine learning methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. It also highlights research and development into novel computing architectures aimed at accelerating machine learning.

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