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.

(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.

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A model based on Brownian motion describes the tsunami-like propagation of chaotic behavior in a system of quantum particles.

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In daily life, “chaos” describes anything messy. In physics, the term has a more specific meaning: It refers to systems that, while subject to deterministic laws, are totally unpredictable because of an exponential sensitivity to initial conditions—think of the butterfly flapping its wings and causing a distant tornado. But how does the chaos observed in the classical, macroscopic world emerge from the quantum-mechanical laws that govern the microscopic world? A recently proposed explanation invokes quantum “information scrambling” [1, 3], in which information gets rapidly dispersed into quantum correlations among the particles of a system. This scrambling is a memory-loss mechanism that can cause the unpredictability of chaos. Developing a theory that fully describes information scrambling remains, however, a daunting task.

A team of researchers from Harvard University and Massachusetts Institute of Technology has found that they could use an optical tweezer array of laser-cooled molecules to observe ground state collisions between individual molecules. In their paper published in the journal Science, the group describes their work with cooled calcium monofluoride molecules trapped by optical tweezers, and what they learned from their experiments. Svetlana Kotochigova, with Temple University, has published a Perspective piece in the same journal issue outlining the work—she also gives an overview of the work being done with arrays of optical tweezers to better understand molecules in general.

As Kotochigova notes, the development of optical tweezers in the 1970s has led to groundbreaking science because it allows for studying atoms and molecules at an unprecedented level of detail. Their work involves using laser light to create a force that can hold extremely tiny objects in place as they are being studied. In more recent times, optical tweezers have grown in sophistication—they can now be used to manipulate arrays of molecules, which allows researchers to see what happens when they interact under very controlled conditions. As the researchers note, such arrays are typically chilled to keep their activity at a minimum as the molecules are being studied. In this new effort, the researchers chose to study arrays of cooled calcium monofluoride molecules because they have what the team describes as nearly diagonal Franck-Condon factors, which means they can be electronically excited by firing a laser at them, and then revert to an initial state after emission.

In their work, the researchers created arrays of tweezers by diffracting a single beam into many smaller beams, each of which could be rearranged to suit their purposes in real time. In the initial state, an unknown number of molecules were trapped in the array. The team then used light to force collisions between the molecules, pushing some of them out of the array until they had the desired number in each tweezer. They report that in instances where there were just two molecules present, they were able to observe natural ultracold collisions—allowing a clear view of the action.

The code used below is on GitHub.

In this project, we’ll be solving a problem familiar to any physics undergrad — using the Schrödinger equation to find the quantum ground state of a particle in a 1-dimensional box with a potential. However, we’re going to tackle this old standby with a new method: deep learning. Specifically, we’ll use the TensorFlow package to set up a neural network and then train it on random potential functions and their numerically calculated solutions.

Why reinvent the wheel (ground state)? Sure, it’s fun to see a new tool added to the physics problem-solving toolkit, and I needed the practice with TensorFlow. But there’s a far more compelling answer. We know basically everything there is to know about this topic already. The neural network, however, doesn’t know any physics. Crudely speaking, it just finds patterns. Suppose we examine the relative strength of connections between input neurons and output. The structure therein could give us some insight into how the universe “thinks” about this problem. Later, we can apply deep learning to a physics problem where the underlying theory is unknown. By looking at the innards of that neural network, we might learn something new about fundamental physical principles that would otherwise remain obscured from our view. Therein lies the true power of this approach: peering into the mind of the universe itself.

Researchers at the University of Illinois at Urbana-Champaign have replicated one of the most well-known electromagnetic effects in physics, the Hall Effect, using radio waves (photons) instead of electric current (electrons). Their technique could be used to create advanced communication systems that boost signal transmission in one direction while simultaneously absorbing signals going in the opposite direction.

The Hall Effect, discovered in 1879 by Edwin Hall, occurs because of the interaction between charged particles and electromagnetic fields. In an electric field, negatively charged particles (electrons) experience a force opposite to the direction of the field. In a magnetic field, moving electrons experience a force in the direction perpendicular to both their motion and the magnetic field. These two forces combine in the Hall Effect, where perpendicular electric and magnetic fields combine to generate an electric current. Light isn’t charged, so regular electric and magnetic fields can’t be used to generate an analogous “current of light.” However, in a recent paper published in Physical Review Letters, researchers have done exactly this with the help of what they call “synthetic electric and magnetic fields.”

Principal investigator Gaurav Bahl’s research group has been working on several methods to improve radio and optical data transmission as well as fiber optic communication. Earlier this year, the group exploited an interaction between light and sound waves to suppress the scattering of light from material defects and published its results in Optica. In 2018, team member Christopher Peterson was the lead author in a Science Advances paper which explained a technology that promises to halve the bandwidth needed for communications by allowing an antenna to send and receive signals on the same frequency simultaneously through a process called nonreciprocal coupling.

A ten-qubit system based on spins in impure diamond achieves coherence times of over a minute.

In the global race to build a quantum computer, it’s still unclear what material will make the best qubit. Companies have bet on a variety of architectures based on trapped ions, neutral atoms, superconducting circuits, and more. Now, Tim Taminiau of Delft University of Technology, Netherlands, and colleagues have demonstrated that they can manipulate magnetic spins inside diamond into the robust quantum states necessary for quantum computing. In their experiment, they entangle all possible pairs of a ten-qubit system and produce states in which seven different qubits are entangled simultaneously. They also show that individual qubits can retain quantum coherence for up to 75 s—a record for solid-state systems.

Experimenting at 4.1 million degrees Fahrenheit, physicists at Sandia National Laboratories’ Z machine have found that an astronomical model—used for 40 years to predict the sun’s behavior as well as the life and death of stars—underestimates the energy blockage caused by free-floating iron atoms, a major player in those processes.

The blockage effect, called opacity, is an element’s natural resistance to energy passing through it, similar to an opaque window’s resistance to the passage of light.

“By observing real-world discrepancies between theory and our experiments at Z, we were able to identify weaknesses in opacity figures inserted into solar models,” said Taisuke Nagayama, lead author on the Sandia groups’ latest publication in Physical Review Letters.

The fabric of space-time may get its robustness from a network of quantum particles, according to a principle called quantum error correction.