̽��ֱ�� of Cambridge - High-dimensional space

‘Saddle-shaped’ universe could undermine general relativity

sc604 — Mon, 22 May 2017 02:00:00 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, have used computer simulations to predict the existence of a so-called naked singularity, which interferes with Einstein’s general theory of relativity. This is the first time that a naked singularity, which causes the laws of physics to break down, has been predicted in three-dimensional space. ̽��ֱ��findings are reported in the journal Physical Review Letters.

Einstein’s general theory of relativity underpins our current understanding of gravity: everything from the estimation of the age of the stars in the universe, to the GPS signals we rely on to help us navigate, is based on his equations. In part, the theory tells us that matter warps its surrounding spacetime, and what we call gravity is the effect of that warp. In the 100 years since it was published, general relativity has passed every test that has been thrown at it, but one of its limitations is the existence of singularities.

A singularity is a point where gravity is so intense that space, time, and the laws of physics, break down. General relativity predicts that singularities exist at the centre of black holes, and that they are surrounded by an event horizon – the ‘point of no return’, where the gravitational pull becomes so strong that escape is impossible, meaning that they cannot be observed from the outside.

For more than 40 years, mathematicians have proposed that whenever singularities form, they will always be hidden from view in this way – this is known as the ‘cosmic censorship conjecture.’ If true, cosmic censorship means that outside of black holes, these singularities have no measurable effect on anything, and the predictions of general relativity remain valid.

In recent years, researchers have used computer simulations to predict the existence of ‘naked singularities’ – that is, singularities which exist outside an event horizon. Naked singularities would invalidate the cosmic censorship conjecture and, by extension, general relativity’s ability to explain the universe as a standalone theory. However, all of these predictions have been modelled on universes which exist in higher dimensions. For example, in 2016, two Cambridge PhD students predicted the existence of a naked singularity, but their predictions were based on a five-dimensional universe.

̽��ֱ��new research, by Toby Crisford and Jorge Santos from Cambridge’s Department of Applied Mathematics and Theoretical Physics, has predicted the existence of a naked singularity in a four-dimensional universe - three spatial dimensions, plus time - for the first time.

Their predictions show that a naked singularity can form in a special kind of curved space known as anti-de Sitter space, in which the universe has a distinctive ‘saddle’ shape. According to general relativity, universes can have various shapes, and anti-de Sitter space is one of these possible shapes.

Anti-de Sitter space has a very different structure to flat space. In particular it has a boundary which light can reach, at which point it is reflected back. “It’s a bit like having a spacetime in a box,” said Crisford. “At the boundary, the walls of the box, we have the freedom to specify what the various fields are doing, and we use this freedom to add energy to the system and eventually force the formation of a singularity.”

While the results are not directly applicable to our universe, as ‘forcing’ a singularity is not a procedure which is possible to simulate in flat space, they do open up new opportunities to study other theories to understand the universe. One such theory could involve quantum gravity, which provides new equations close to a singularity.

“ ̽��ֱ��naked singularity we see is likely to disappear if we were to include charged particles in our simulation – this is something we are currently investigating,” said Santos. “If true, it could imply a connection between the cosmic censorship conjecture and the weak gravity conjecture, which says that any consistent theory of quantum gravity must contain sufficiently charged particles. In anti-de Sitter space, the cosmic censorship conjecture might be saved by the weak gravity conjecture.”

Inset image: Image of (1 + 1)-dimensional anti-de Sitter space embedded in flat (1 + 2)-dimensional space. Credit: Wikimedia Commons.

Reference:
Toby Crisford and Jorge E. Santos. 'Violating the Weak Cosmic Censorship Conjecture in Four-Dimensional Anti–de Sitter Space.' Physical Review Letters (2017). DOI: 10.1103/PhysRevLett.118.181101.

Researchers have shown how singularities – which are normally only found at the centre of black holes and hidden from view – could exist in highly curved three-dimensional space.

It’s a bit like having spacetime in a box.

Toby Crisford

NASA/JPL-Caltech

Artist's concept of a supermassive black hole

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

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Lost in high-dimensional space: Study improves the cure for the “Curse Of Dimensionality”

tdk25 — Thu, 13 Oct 2016 08:05:16 +0000

Researchers have developed a new technique for making calculations in “high-dimensional space” – mathematical problems so wide-ranging in their scope, that they seem at first to be beyond the limits of human calculation.

In what sounds like the title of a rejected script for an Indiana Jones movie, the method improves on existing approaches to beat a well-known problem known as “ ̽��ֱ��Curse Of Dimensionality”. It was devised by a team of researchers at the ̽��ֱ�� of Cambridge.

In rough terms, the “Curse”, refers to the apparent impossibility of making calculations in situations where the number of variables, attributes, and possible outcomes is so large that it seems futile even to try to comprehend the problem in the first place.

A simple example is this: Imagine that you have a cup containing 100 grains of rice. You pick it up, shake it, and put it down again. ̽��ֱ��arrangement within the cup changes, but what are the chances of that arrangement occurring, relative to all other possibilities?

While most people would reasonably consider that problem not just impossible, but largely pointless, it illustrates the type of maths needed to make predictions about much bigger – and more meaningful – issues.

Those include, for example, trying to model the likely shape and impact of a decaying ecosystem, such as a developing area of deforestation, or the potential effect of different levels of demand on a power grid. More fundamentally, the same class of calculation would theoretically enable us to get to grips with the statistical probability of our own existence on Earth, or the chances that life might happen again, elsewhere in the Universe.

̽��ֱ��new study was led by Stefano Martiniani, a Gates Scholar at St John’s College, Cambridge, who carried out the work with colleagues in the Department of Chemistry and at Microsoft Research in Cambridge.

“There is a very large class of problems that can be solved through the sort of approach that we have devised,” Martiniani said. “It opens up a whole world of possibilities in the study of things like dynamical systems, chemical structure prediction, or artificial neural networks.”

Most people understand “dimensions” to mean height, width, depth and time, but in Mathematics the term is also used flexibly to describe the number of parameters needed to specify a “state” for any given problem. ̽��ֱ��more complicated the problem in question, the greater the space you need to express the parameters. They therefore become “high-dimensional spaces”.

Similarly, working out the likelihood of a particular outcome in a situation where all sorts of different variables apply – such as the grains of rice in a cup arranging themselves in a particular way – is a high-dimensional problem. Expressing and plotting the combined impact of the many parameters that might affect the outcome involves imagining a graph with multiple axes, as if working in numerous dimensions at once.

̽��ֱ��method devised by Martiniani and colleagues, like other approaches, begins by characterising such challenges as an “energy landscape”. ̽��ֱ��range of possible states in which a system such as the cup of rice may exist is envisaged as a landscape of mountains and valleys, in which the base of each valley is a stable state.

̽��ֱ��set of initial conditions leading to this stable state is called a “basin of attraction”. ̽��ֱ��fundamental theory is that, if the volume of each basin of attraction can be calculated, then this begins to provide some sort of indication of the probability of a given state’s occurrence.

To do that, researchers build computer software which models high dimensional systems, using the landscape analogy, and makes calculations within it.

̽��ֱ��simplest model is a brute force approach, which essentially takes a reading, shakes the system up, takes another reading, and repeats the process – many millions of times – in an attempt to establish the probability of certain outcomes. A more sophisticated strategy recurrently starts in the same place and measures the average distance within the energy landscape in which the system finds the same basin of attraction, through which the user gradually develops an appreciation of its volume.

“In most cases you are like a blindfolded person, walking around drunk in the energy landscape,” Martiniani said. “At any given moment, you only really know where you are and where you have just come from.”

In the new study, however, the team applied a different approach to the same kind of problem. Borrowing a technique widely used in biomolecular simulations, called the Multistate Bennett Acceptance Ratio, they developed a method which systematically tests the limits of one particular basin of attraction. Rather than gauging its volume by just taking an average from random samples, it looks for the furthest and least likely limits.

̽��ֱ��net result is a much more efficient sampling technique, which enables a much broader range of calculations in high-dimensional space.

To test this, the team modelled an imaginary 93-dimensional “system” made up of 32 soft spheres that could be packed together in multiple ways. They found that they were able to sample and quantify outcomes within that system that would only be found randomly one in every 10100 times. In other words, the chances of stumbling across those outcomes by chance would be one in ten duotrigintillion.

“In basic terms it goes where brute force sampling never will, because if you started to try, you would never finish,” Martiniani added. “Technically, the limits of the problems we can solve are now not those of the approach, but the computing power we need to simulate the underlying energy landscape. When addressing these kinds of problems in high-dimensional space, this should now be the technique of choice.”

̽��ֱ��study, Structural analysis of high-dimensional basins of attraction, is published in the journal Physical Review E.

Researchers have developed a new method for making effective calculations in “high-dimensional space” – and proved its worth by using it to solve a 93-dimensional problem.

In most cases you are like a blindfolded person, walking around drunk in the energy landscape.

Stefano Martiniani

Tom Ruen via Wikimedia Commons.

Projection of a 9-dimensional cube. High-dimensional spaces pose considerable problems when trying to make calculations and predictions – something that the new method devised by researchers aims to address.

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

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