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In Wednesday’s issue of Nature, a new paper describes a potentially useful way of measuring the interactions between normal matter and exotic particles, like antiprotons and unstable items like kaons or elements containing a strange quark. The work is likely to be useful, as we still don’t understand the asymmetry that has allowed matter to be the dominant form in our Universe.

But the study is probably most notable for the surprising way that it collected measurements. A small research team managed to put an antiproton in orbit around the nucleus of a helium atom that was part of some liquid helium chilled down to where it acted as a superfluid. The researchers then measured the light emitted by the antiproton’s orbital transitions.

Why would anyone want to do this?

There are many reasons you’d want to get precise measurements of this sort of thing. For one, the measurements will be sensitive to the properties of antimatter and strange quarks, which are short-lived and are often created in environments that make precision measurements challenging. In addition, this system involves interactions between antimatter and regular matter, which can be difficult to capture due to their violent ends. Finally, the specific interactions here—between an atomic nucleus and an object in the orbitals that surround it—are sensitive to properties that are fundamental to the Universe.

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The hardware that slows down antiprotons so they can be used in these sorts of experiments.

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Within mathematics, there is a vast and ever-expanding web of conjectures, theorems and ideas called the Langlands program. That program links seemingly disconnected subfields. It is such a force that some mathematicians say it—or some aspect of it—belongs in the esteemed ranks of the Millennium Prize Problems, a list of the top open questions in math. Edward Frenkel, a mathematician at the University of California, Berkeley, has even dubbed the Langlands program “a Grand Unified Theory of Mathematics.”

The program is named after Robert Langlands, a mathematician at the Institute for Advanced Study in Princeton, N.J. Four years ago, he was awarded the Abel Prize, one of the most prestigious awards in mathematics, for his program, which was described as “visionary.”

Langlands is retired, but in recent years the project has sprouted into “almost its own mathematical field, with many disparate parts,” which are united by “a common wellspring of inspiration,” says Steven Rayan, a mathematician and mathematical physicist at the University of Saskatchewan. It has “many avatars, some of which are still open, some of which have been resolved in beautiful ways.”

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Credit: Boris SV/Getty Images

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The physicist Richard Feynman believed that simplicity was the key to learning.

Feynman worked on the Manhattan Project when he was only 20 years old.

He went on to win the Nobel Prize in 1965 for his work in quantum electrodynamics, along with Julian Schwinger and Sin-Itiro Tomonaga.

Feynman believed that truth lies in simplicity and that things are easier to learn and retain when they’re simpler.

When your knowledge of something is full of complex explanations and terms taken from textbooks, you’re less likely to grasp it.

He’s famously been quoted as saying, “You must not fool yourself, and you are the easiest person to fool.”

The goal of learning is to understand the world better. But more often than not, the way we learn doesn’t help us to achieve this.

You end up memorizing something exactly as it’s written in a book or as the teacher explained it to you, so it doesn’t take long for this knowledge to disappear.

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Feynman believed it was easier to learn and retain information when it was simpler. Hinterhaus Productions/Getty Images

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