On January 26, I had the honor of visiting the European Organization for Nuclear Research (CERN), one of the largest and most complex scientific research facilities in the world. What was a neuroscientist like myself doing at a particle collider built for physics experiments? Physics is often called the “king of the sciences” because all other sciences are built on its conceptual foundation. (And the queen? Mathematics, of course). At CERN, an underground particle collider 27 kilometers in circumference called the Large Hadron Collider (LHC) uses magnetic fields to collide particles smaller than atoms and study the resulting interactions. These subatomic particles are the building blocks for all other matter, including carbon, oxygen, DNA, proteins, cells, and brains. Particle physicists also face challenges in communicating their technical, esoteric research to the public. By listening to these physicists, we can also learn how to communicate complex ideas in a clear, concise manner.
One scientist who is unafraid of such challenges is Dr. André David, an experimental particle physicist at CERN. After an invitation resulting from a lively discussion on Twitter, André was kind enough to give me a personal tour of the LHC, which included traveling underground into the depths of CERN where protons are collided at nearly the speed of light. Although time for questions was unfortunately cut short after the tour, André was kind enough to answer Knowing Neuron’s questions via email about the common ground shared by neuroscientists and physicists.
(Don’t recognize jargon? Hover over underlined words below to see their definitions!)
Joel: When explaining your research to the public, how do you weigh the need to explain your work in clear, everyday language with the often opposing need to preserve the integrity of your research and not over-simplify the concepts?
André: The quintessential scicomm question: to me there is a balance between keeping [the] audience engaged and staying accurate. If you lose the audience’s attention it does not matter what you’re saying because it won’t even get to them. It’s like the impedance matching problem. Physics!
Joel: This is definitely a valid point. But do you think some treatments of science in the popular media go too far in over-simplifying, or use such loose analogies as to almost spread misinformation?
André: Yes, but I don’t think this is specific to science communication, but communication in general. In an information-flooded world, accurate simplification is precious, and what in my opinion distinguishes the best communicators and writers.
Joel: On Twitter, we talked about the value of scientific images in communicating science to the public. How important is it that scientific images can be readily understood by anyone viewing them, including those outside of that particular field of research?
“In an information-flooded world, accurate simplification is precious …” – André David
André: There are many different kinds of images. Eventually everything can be reduced to a quantitative statistical significance. Is it important to have visuals that anyone can grasp? Yes. Do they need to be directly related to the final results and their implications? No.
Joel: On Twitter, we also talked about the aesthetic quality of scientific images. How important is the aesthetic quality of scientific images? Is aesthetic quality independent of how clearly the image can be understood? Or do the two go hand-in-hand somehow?
André: I am biased, since I am picky about building visualizations. I have, for instance, fought the rainbow palette. Eventually, I realized that there is a whole field of research behind this and that there are good practices that can be followed because human brains and ancillary organs (eyes, etc) are more susceptible to some stimuli than other stimuli. Some people are able to abstract beyond the visual representation and dig directly into the concepts. I think the public (and other science professionals) need to receive something more polished, something more universally understandable by humans, something that eases in the context needed to interpret what is being seen/heard/tasted/touched/smelled/felt.
Joel: So data visualization is both an art and a science. I’d also like to follow up on our Twitter conversation about the nature of data. How much can data be processed or transformed and still be considered data?
André: Data are data. Sorry if I sound like a prime-minister. Of course there’s raw data, there’s processed data, there’s reduced data, etc. They are all data and each has a role in inference. E.g., it is near impossible to take raw data and *bam* make a meaningful measurement out of it. To me, data never ceases to be data, provided that the inputs and the process used to obtain it are fully specified. Approximations included. Of course, it’s relevant if it still conveys information.
Joel: On Twitter, you shared with me a video of data from CERN that has been interpreted as music and performed by musicians. This seems different from normal sonification, in which there is a well defined function that specifies how the original data should be transformed into audio. I assume for interpretation as music, there is no such well defined transform. Can data still be considered data when it is interpreted in such a qualitative manner? Are the results when data is interpreted as music this way reproducible?
André: Results being reproducible does not depend as much on the format in which data is encoded (as long as no randomness is introduced) than it does on how it is processed. If I sonify something and then present it to people with wildly different hearing brain functions, you may reach different conclusions. But the data/information being provided is the same.
” I cannot presume to offer views to neuroscientists and biomedical researchers other than what makes science work: hypothesize, predict, measure, repeat. This is how we can blow the chaff from nature’s wheat.” – André David
Joel: In biomedical research, there is talk right now of a replication crisis, as many published findings cannot be replicated and are likely false positives. What is the state of reproducibility in physics right now? Are there any parallels in physics with what is happening is other fields such a biomedical research?
André: Great question. There is a fundamentally different thing between particle physics and biomedical research: we have an extremely solid null hypothesis, the standard model. This makes our life much easier. That said there is also a long-standing tradition of putting results on the arXiv before publishing (opening to criticism/discussion widely before paywalls) and we are very keen in performing blind analyses (comparable to registering studies before performing them). We also hold different standard as to what is significant: we claim discoveries when z=5 sigma, while in other fields p<0.05 is considered significant. Why is this a nuisance? This one never fails to help me get the point through: https://xkcd.com/882/
Joel: That’s a great XKCD. As a follow up question, I believe the LHC is the only facility in the world capable of detecting a Higgs Boson or generating quark-gluon plasma. Wouldn’t that make independent replication impossible, at least for the near future? How do you deal with that?
André: Yes, there’s only one facility, but we have two collaborations, each with its experiment in the LHC that have found a Higgs boson; we call them CMS and ATLAS. And both found [the Higgs Boson] to be produced with similar rates, with compatible masses, and appearing in searches for the same types of decays of the Higgs boson.
These collaborations are about 3000-strong each and are independent: different funding, different detectors, different software, etc. They are engaged in coopetition: they compete to be the best and the first, yet they need each other to make sure no false claims are made.
When we found this Higgs boson, I knew all that CMS was going to present. But I knew nothing of what ATLAS had seen. So, I was much more excited to see their results—that were completely independent of my contributions to the detector and analysis—than my own.
Joel: Also on the topic of reproducibility, many researchers found evidence of a pentaquark in 2003, only to have the results called into question. But recently, a new announcement was made that the LHC had discovered a pentaquark. What are your thoughts on this?
André: It’s human to look for patterns and to not be able to “unsee” them. And researchers are looking for correlations, causes, and deviations. To solve this, one can perform blind analyses.
But even if you do that, and all the methods and selection criteria are set before you even look at the data, you are still open to the possibility that some random fluctuation may look like something that you would consider legitimately interesting. This statistical effect is what we call look-elsewhere effect, or trials factor, and is not trivial to account for. In the pentaquark case above, researchers were looking for a narrow peak of unknown width and position appearing over some smooth background. By doing this search, one opens oneself to be excited by statistical fluctuations that mimic a peaking feature; and the broader your acceptance of what could look exciting is, the more likely it is that, by chance, you’ll get excited by a random fluctuation.
So, in this pentaquark case many people were looking for a peak, some found a huge peak, others found nothing. The community remained skeptical for lack of reproducibility. Science worked.
See also the GAME, where also the history of 5 sigma in particle physics is explained.
Joel: As someone working in particle physics, you might have a unique perspective on neuroscience and biomedical research. What views would you offer to scientists working in these fields?
André: I cannot presume to offer views to neuroscientists and biomedical researchers other than what makes science work: hypothesize, predict, measure, repeat. This is how we can blow the chaff from nature’s wheat.
Joel: How do you view the current relationship between physicists and neuroscientists? Many neuroimaging techniques, such as MRI (magnetic resonance imaging) and PET (positron emission tomography), use nuclear physics. Do you anticipate any breakthroughs or advances in physics in the near future that will allow for new neuroimaging methods?
André: The link between particle physics and medical imaging is now quite long and keeps improving. I am not aware of breakthrough technologies that would allow different types of information to be gathered without intrusion, but the technology boundaries that we push in order to make our detectors more accurate, more sensitive, or more precise, tend to trickle down the chain swiftly. One example I am aware of is that of photon-counting devices that are so sensitive (to single photons!) that the required X-ray dosages can be substantially reduced to get the same image quality. Beyond this, I think that particle physics and neurosciences may find a link through a different path in the future ahead, namely that of artificial intelligence and machine learning.
Joel: When we spoke at CERN you mentioned that you don’t like the term “antimatter” since it probably makes antimatter [matter with the opposite charge of normal matter] sound more exotic than it really is. I remember when I first learned about PET imaging in college, I was surprised that antimatter is used in neuroimaging. What other technologies besides PET wouldn’t work without antimatter?
André: Antimatter is really common: muons produced in the atmosphere by cosmic rays are both positive and negative. And there’s one every second going through a square meter at sea level.
Technologies that use particle annihilation besides PET? I am not well-versed, but there’s the ACE [Antiproton Cell Experiment] project, point 8 in this article from Symmetry Magazine.
If you consider the Tevatron accelerator [particle accelerator in the US] a technology, then it would not have run for ~20 years without antimatter, since it collided protons with antiprotons.
Oh, and remember that photons are their own antiparticle. So, are photons matter, antimatter, both, or none?
Joel: Those are great points. Switching gears a bit, do you have a favorite scientist or a hero whom researchers in other fields could also look up to?
André: Not quite any individual, no. But I do have a great admiration for women in STEM. It’s the twenty-first century and we still have a deep problem in our societies that makes it such that girls are discouraged from STEM learning. And those who either ignore of circumvent that many times have to endure all manners of unfounded discouragement.
Joel: Do you think that physics, in particular, is lagging behind other scientific disciplines, such as biology or neuroscience, in recruiting female scientists? If so, do you think physics has an image problem, sort of like the stereotype people attach to the characters in the American TV show The Big Bang Theory?
André: I don’t have the numbers neither the biology/neuroscience experience. I do know one female who loved physics, but because of high school mathematics decided to take biology. I am not sure it is the stereotypes of the male physicists, but that of girls being characterized, at an early stage, as not being good at mathematics. And I think this is where the root is: early in the education path.
Chalmers, M. (2015). Forsaken pentaquark particle spotted at CERN: exotic subatomic species confirmed at Large Hadron Collider after earlier false sightings. Nature, 523(7560), 267-269.
CMS collaboration. (2012). Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC. arXiv preprint arXiv:1207.7235.
Dorigo, T. (2014). Extraordinary Claims: the 0.000029% Solution. Retreived from https://indico.cern.ch/event/277650/contributions/629796/attachments/505859/698408/Extraordinary_Claims.pdf#page=10
Kwon, D. (2015). Ten things you might not know about antimatter. Symmetry magazine. Retreived from http://www.symmetrymagazine.org/article/april-2015/ten-things-you-might-not-know-about-antimatter
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