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Category: cosmology

Dwarf galaxies are enigmas wrapped in riddles. Although they are the smallest galaxies, they represent some of the biggest mysteries about our universe. While many dwarf galaxies surround our own Milky Way, there seem to be far too few of them compared with standard cosmological models, which raises a lot of questions about the nature of dark matter and its role in galaxy formation.

New theoretical modeling work from Andrew Wetzel, who holds a joint fellowship between Carnegie and Caltech, offers the most accurate predictions to date about the dwarf galaxies in the Milky Way’s neighborhood. Wetzel achieved this by running the highest-resolution and most-detailed simulation ever of a galaxy like our Milky Way. His findings, published by The Astrophysical Journal Letters, help to resolve longstanding debates about how these dwarf galaxies formed.

One of the biggest mysteries of dwarf galaxies has to do with , which is why scientists are so fascinated by them.

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Intel has planted some solid stakes in the ground for the future of deep learning over the last month with its acquisition of deep learning chip startup, Nervana Systems, and most recently, mobile and embedded machine learning company, Movidius.

These new pieces will snap into Intel’s still-forming puzzle for capturing the supposed billion-plus dollar market ahead for deep learning, which is complemented by its own Knights Mill effort and software optimization work on machine learning codes and tooling. At the same time, just down the coast, Nvidia is firming up the market for its own GPU training and inference chips as well as its own hardware outfitted with the latest Pascal GPUs and requisite deep learning libraries.

While Intel’s efforts have garnered significant headlines recently with that surprising pair of acquisitions, a move which is pushing Nvidia harder to demonstrate how GPU acceleration (thus far the dominant compute engine for model training), they still have some work to do to capture mindshare for this emerging market. Further complicating this is the fact that the last two years have brought a number of newcomers to the field—deep learning chip upstarts touting the idea that general purpose architectures (including GPUs) cannot compare to a low precision, fixed point, specialized approach. In fact, we could be moving into a “Cambrian explosion” for computer architecture–one that is brought about by the new requirements of deep learning. Assuming, of course, there are really enough applications and users in a short enough window that the chip startups don’t fall over waiting for their big bang.

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There are many theoretical models to explain such aspects of high energy physics as dark matter, theory of inflation, bariosynthesis, the Higgs mechanism, etc. The discovery of universal expansion is accelerating, precise measurements of characteristics of the cosmic microwave background, and indirect confirmations of the existence of dark matter have significantly advanced observational and theoretical cosmology. The connection between cosmological processes in the early universe and physics of elementary particles is getting clearer. Theories with additional compact measurements (multidimensional gravity) have contributed to the explanation of a series of phenomena in cosmology and the physics of elementary particles including inflation, baryon asymmetry, black holes and dark matter. Multidimensional gravity may become one of the basics of fundamental theoretical physics.

The development of colliders led to the discovery of a number of new particles, which was a great confirmation of the Standard Model ℠ of particle physics. The real SM triumph was the discovery of the Higgs boson in LHC experiments in CERN. However, despite the success of SM in , there is a series of questions and problems that can’t be explained by it—for example, baryon asymmetry, the origin of the Higgs field, the production of the early quasars, etc.

A theoretical direction, which is based on the idea of multidimensional gravity, is being developed at the MEPhI Department № 40 under the supervision of Professor S.G. Rubin. For the past several years, interesting results have been obtained on the basis of this research. In a thesis by Alexey Grobov titled “Effects of extra spaces in particle physics and cosmology,” multidimensional gravitational models contribute to better understanding of connections between astrophysics and microphysics phenomena.

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Two separate experiments at the Large Hadron Collider at the European Organisation for Nuclear Research, on the French-Swiss border, appear to confirm the existence of a subatomic particle, the Madala boson, that for the first time could shed light on one of the great mysteries of the universe — dark matter.

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String theory arrived in the public field in 1988 when a BBC radio series Desperately Seeking Superstrings was aired. Thanks to good marketing and its naturally curious name and characteristics, it is now part of popular discourse, mentioned in TV’s Big Bang Theory, Woody Allen stories, and countless science documentaries.

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Spinning black holes are capable of complex quantum information processes encoded in the X-ray photons emitted by the accretion disk.

The black holes sparked the public imagination for almost 100 years now. Their debated presence in the universe has been proven without a doubt by detecting the X-ray radiation coming from the center of the galaxies, a feature of massive black holes. Black holes emit X-ray radiation, light with high energy, due to the extreme gravity in their vicinity. The vast majority if not all of the known black holes were unveiled by detecting the X-ray radiation emitted by the stellar material accreting around black holes.

X-ray photons emitted near rotating black holes not only exposed the existence of these phantom-like astrophysical bodies, but also seem to carry hidden quantum messages.

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