Lifeboat Foundation

Particle physicists are planning the successor to CERN’s Large Hadron Collider – but how will they deal with the deluge of data from a future machine and the proliferation of theoretical models? Michela Massimi explains why a new scientific methodology called “model independence” could hold the answer.

It’s been an exciting few months for particle physicists. In May more than 600 researchers gathered in Granada, Spain, to discuss the European Particle Physics Strategy, while in June CERN held a meeting in Brussels, Belgium, to debate plans for the Future Circular Collider (FCC). This giant machine – 100 km in circumference and earmarked for the Geneva lab – is just one of several different projects (including those in astroparticle physics and machine learning) that particle physicists are working on to explore the frontiers of high-energy physics.

CERN’s Large Hadron Collider (LHC) has been collecting data from vast numbers of proton–proton collisions since 2010 – first at an energy of 8 TeV and then 13 TeV during its second run. These have enabled scientists on the ATLAS and CMS experiments at the LHC to discover the Higgs boson in 2012, while light has also been shed on other vital aspects of the Standard Model of particle physics.

Kepler exoplanet may have more water than Earth, but that doesn’t mean it’s habitable, say researchers.

The threats of cyberattack and hypersonic missiles are two examples of easily foreseeable challenges to our national security posed by rapidly developing technology. It is by no means certain that we will be able to cope with those two threats, let alone the even more complicated and unknown challenges presented by the general onrush of technology — the digital revolution or so-called Fourth Industrial Revolution — that will be our future for the next few decades.

Technology is about to upend our entire national security infrastructure.

(Phys.org)—Light behaves both as a particle and as a wave. Since the days of Einstein, scientists have been trying to directly observe both of these aspects of light at the same time. Now, scientists at EPFL have succeeded in capturing the first-ever snapshot of this dual behavior.

Quantum mechanics tells us that light can behave simultaneously as a particle or a wave. However, there has never been an experiment able to capture both natures of light at the same time; the closest we have come is seeing either wave or particle, but always at different times. Taking a radically different experimental approach, EPFL scientists have now been able to take the first ever snapshot of light behaving both as a wave and as a particle. The breakthrough work is published in Nature Communications.

Continue reading “The first ever photograph of light as both a particle and wave” »

TVs and radios blare that “artificial intelligence is coming,” and it will take your job and beat you at chess.

But AI is already here, and it can beat you — and the world’s best — at chess. In 2012, it was also used by Google to identify cats in YouTube videos. Today, it’s the reason Teslas have Autopilot and Netflix and Spotify seem to “read your mind.” Now, AI is changing the field of synthetic biology and how we engineer biology. It’s helping engineers design new ways to design genetic circuits — and it could leave a remarkable impact on the future of humanity through the huge investment it has been receiving ($12.3b in the last 10 years) and the markets it is disrupting.

Anders Ohlsson Delivery Manager at Sandvik Additive Manufacturing, shared his excitement for the new process in the Sandvik press release stating, “On seeing its potential, we began to wonder what else would be possible from 3D-printing complex shapes in a material that is three times stiffer than steel, with heat conductivity higher than copper, the thermal expansion close to Invar – and with a density close to aluminum.”

Today we are taking a look at how Sandvik created the first-ever 3D printed diamond composite.

The positronic brain was an extremely sophisticated computation device capable of artificial sentience, created by Dr. Noonian Soong, based on an idea from author Isaac Asimov. ( TNG : “ Datalore ”)

This device consisted of an artificial neural network, designed to imitate the humanoid brain. The construction of a positronic brain was extremely complex, and Dr. Soong was the only scientist to have done so successfully – on at least six occasions: two unspecified prototypes, B-4, Lore, Data, and Juliana Soong (although the latter three were the only truly stable units).

One of the difficulties in creating a stable positronic brain was determining how the electron resistance across the neural filaments was to be resolved. ( TNG : ” The Measure Of A Man ”)

Sub-nanometre resolution in 3D position measurements of light-emitting molecules has been achieved by physicists in Germany. Jörg Enderlein and colleagues at the University of Göttingen achieved the result by replacing metal films used in previous super-resolution techniques with single layers of graphene. Their innovation could allow researchers in a wide variety of fields to measure molecular positions to unprecedented degrees of accuracy.

Recently, the technique of single-molecule localization super-resolution microscopy (SMLM) has become an incredibly useful tool for researchers in fields ranging from fundamental physics to medical research. By analysing images of single light-emitting molecules, researchers can pinpoint the positions of their centres to within single atomic widths. However, SMLM faces one significant shortcoming: it can only locate molecules in 2D, giving no information about their positions along the out-of-plane axis.

This problem can be partially overcome through the technique of metal-induced energy transfer (MIET), which introduces a thin metal film to the setup. The idea is that the apparatus picks up changes in the molecule’s fluorescence that are caused by the molecule coupling to collective excitations of surface plasmons in the film. Since this light emission varies with distance from the film, researchers can use MIET to calculate the molecule’s distance relative to the film surface, allowing them to locate it along the third axis. Yet with current versions of the technique, the accuracy of this out-of-plane measurement is 3–5 times worse than that of lateral localization, in the plane of the film.