“Ghostly” neutrinos used for the first time to peer inside protons at Fermilab accelerator

A beam of neutrinos has for the first time been used to investigate the structure of protons.

The ground-breaking research led by University of Rochester scientists is part of international collaboration MINERvA (Main Injector Neutrino ExpeRiment to study v-A interactions) at Fermilab (Fermi National Accelerator Laboratory) 100 metres under the city of Batavia in US state Illinois.

MINERvA is the world’s first experiment to use high-intensity neutrino beams to study the interaction of the particles with different atomic nuclei, from helium to lead.

Neutrinos don’t play nice.

They were first postulated to exist by Wolfgang Pauli in 1930, named by Enrico Fermi in 1934, but only experimentally “discovered” by Frederick Reines and Clyde Cowan in 1956 as part of Project Poltergeist.

The project was so named because neutrinos were known then, as they are now, as “ghost particles,” owing to the fact that they are very difficult to detect.

They have no charge, so they don’t interact electromagnetically, and they have nearly no mass. But neutrinos are among the most abundant particles in the universe and learning about them could tell us a lot about the cosmos.

Read more: First galaxy neutrinos discovered by Antarctica observatory

Indeed, learn about neutrinos is what MINERvA was attempting to do, not study protons.

“While we were studying neutrinos as part of the MINERvA experiment, I realised a technique I was using might be applied to investigate protons,” says Dr Tejin Cai, now at York University, who conducted the research as a PhD student at Rochester. “We weren’t sure at first if it would work, but we ultimately discovered we could use neutrinos to measure the size and shape of the protons that make up the nuclei of atoms. It’s like using a ghost ruler to make a measurement.”

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Atomic nuclei are tiny [roughly 1 million billionths of a metre in diameter – 0.001 percent the diameter of the whole atom] and difficult to measure.

To understand what is happening in an atomic nucleus, scientists have to bombard it with a beam of high-energy particles. By measuring how far and at what angles the particles bounce off the nucleus, physicists can gain insight into the structure of the nucleus and, sometimes, the protons and neutrons which make up the nucleus.

“This is a very indirect way of measuring something, but it allows us to relate the structure of an object—in this case, a proton – to how many deflections we see in different angles,” says Rochester professor of physics Kevin McFarland.

In fact, such a technique – using accelerated electrons – allowed physicists to measure the proton’s size for the first time in the 1950s at Stanford University’s linear accelerator.

The new method involving neutrinos won’t give a sharper image of protons, but may for the first time give scientists information about how neutrinos and protons interact.

“You are looking at the same flower, but you can see different structures under the different kinds of light,” McFarland analogises. “Our image isn’t more precise, but the neutrino measurement provides us with a different view.”

“Our previous methods for predicting neutrino scattering from protons all used theoretical calculations, but this result directly measures that scattering,” adds Cai.

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The researchers bypassed the fact that neutrinos only interact with ordinary matter one in a trillion times by performing a “chemical trick” – they bombarded not individual atoms, but hydrocarbon molecules.

“The hydrogen and carbon are chemically bonded together, so the detector sees interactions on both at once,” Cai says. “I realised that a technique I was using to study interactions on carbon could also be used to see hydrogen all by itself once you subtract the carbon interactions.”

Performing calculations over nine years, the team was able to collect information about proton internal structure and key insights into neutrino interactions.

“The result of the analysis and the new techniques developed highlight the importance of being creative and collaborative in understanding data,” Cai adds.