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Category: space – Page 702

During the 1930s, venerable theoretical physicist Albert Einstein returned to the field of quantum mechanics, which his theories of relativity helped to create. Hoping to develop a more complete theory of how particles behave, Einstein was instead horrified by the prospect of quantum entanglement — something he described as “spooky action at a distance.”

Despite Einstein’s misgivings, quantum entanglement has gone on to become an accepted part of quantum mechanics. And now, for the first time ever, a team of physicists from the University of Glasgow took an image of a form of quantum entanglement (aka Bell entanglement) at work. In so doing, they managed to capture the first piece of visual evidence of a phenomenon that baffled even Einstein himself.

The paper that described their findings, titled “Imaging Bell-type nonlocal behavior,” recently appeared in the journal Science Advances. The study was led by Dr. Paul-Antoine Moreau, a Leverhulme Early Career Fellow at the University of Glasgow, and included multiple researchers from Glasgow’s School of Physics & Astronomy.

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Feature image ‘Psychonaut’ courtesy of Tetramode.

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The last half century has seen humanity take its first, tentative steps into outer space. Initially, through American and Russian astronaut missions into Earth orbit and then to the Moon, though more recently robotic probes have ventured beyond our solar system entirely.

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The scientific revolution was ushered in at the beginning of the 17th century with the development of two of the most important inventions in history — the telescope and the microscope. With the telescope, Galileo turned his attention skyward, and advances in optics led Robert Hooke and Antonie van Leeuwenhoek toward the first use of the compound microscope as a scientific instrument, circa 1665. Today, we are witnessing an information technology-era revolution in microscopy, supercharged by deep learning algorithms that have propelled artificial intelligence to transform industry after industry.

One of the major breakthroughs in deep learning came in 2012, when the performance superiority of a deep convolutional neural network combined with GPUs for image classification was revealed by Hinton and colleagues [1] for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). In AI’s current innovation and implementation phase, deep learning algorithms are propelling nearly all computer vision-intensive applications, including autonomous vehicles (transportation, military), facial recognition (retail, IT, communications, finance), biomedical imaging (healthcare), autonomous weapons and targeting systems (military), and automation and robotics (military, manufacturing, heavy industry, retail).

It should come as no surprise that the field of microscopy would ripe for transformation by artificial intelligence-aided image processing, analysis and interpretation. In biological research, microscopy generates prodigious amounts of image data; a single experiment with a transmission electron microscope can generate a data set containing over 100 terabytes worth of images [2]. The myriad of instruments and image processing techniques available today can resolve structures ranging in size across nearly 10 orders of magnitude, from single molecules to entire organisms, and capture spatial (3D) as well as temporal (4D) dynamics on time scales of femtoseconds to seconds.

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In 2016, the European Space Agency announced a call for medium-size missions within their Cosmic Vision Program. In layman’s terms, “medium-size” means moderate-cost (less than 550 million euros, or $610 million) and low-risk, and this is achieved by keeping payloads small and by using proven, heritage technology for both spacecraft and payload. Alongside these common-sense conditions is a third and less tangible quality, that the project be scientifically robust. But when comparing excellent cases from vastly different fields, the merits of one scientific mission over another can seem subjective. It’s not enough to lament the dearth of data in said field, or to establish how a project will discover this or that, or even to show exactly how said “groundbreaking technology” will work. ESA wants a mission that will stir up an unprecedented level of excitement, support, and interest within the scientific community. Here is how they attempt to measure a project’s relevance.

“Each member state has a representative in the Science Programme Committee, and it’s their duty to define the content of the program,” said Luigi Colangeli, head of ESA’s Science Coordination Office. “Study groups work with the various proposals to arrive at something that is compatible with the boundary conditions, in this case, of a M-5, or medium-class mission. Right now, we are studying the evolution of the three missions. And then next year we will put together a peer review panel, who will analyze the three candidates and recommend the best selection to our Director of Science.”

Since the call went out four years ago, ESA have been whittling down proposals, from 25 at the beginning to only three now: Envision, Theseus, or SPICA. In February the EnVision conference took place at the National Centre for Space Studies (CNES) in Paris. EnVision is a low-altitude polar orbiter that is meant to perform high-resolution radar mapping, surface composition, and atmospheric studies of Venus. The purpose of the meeting was to call the Venus community to attention, because the clock is ticking. Consortium members, ESA representatives, and interested scientists from all over the world were in attendance.

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