Category Archives: Science Communication

This Week, at FirstPrinciples.org

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I’ve got a piece out this week in a new venue: FirstPrinciples.org, where I’ve written a profile of a startup called Vaire Computing.

Vaire works on reversible computing, an idea that tries to leverage thermodynamics to make a computer that wastes as little heat as possible. While I learned a lot of fun things that didn’t make it into the piece…I’m not going to tell you them this week! That’s because I’m working on another piece about reversible computing, focused on a different aspect of the field. When that piece is out I’ll have a big “bonus material post” talking about what I learned writing both pieces.

This week, instead, the bonus material is about FirstPrinciples.org itself, where you’ll be seeing me write more often in future. The First Principles Foundation was founded by Ildar Shar, a Canadian tech entrepreneur who thinks that physics is pretty cool. (Good taste that!) His foundation aims to support scientific progress, especially in addressing the big, fundamental questions. They give grants, analyze research trends, build scientific productivity tools…and most relevantly for me, publish science news on their website, in a section called the Hub.

The first time I glanced through the Hub, it was clear that FirstPrinciples and I have a lot in common. Like me, they’re interested both in scientific accomplishments and in the human infrastructure that makes them possible. They’ve interviewed figures in the open access movement, like the creators of arXiv and SciPost. On the science side, they mix coverage of the mainstream and reputable with outsiders challenging the status quo, and hot news topics with explainers of key concepts. They’re still new, and still figuring out what they want to be. But from my glimpse on the way, it looks like they’re going somewhere good.

Science Journalism Tasting Notes

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When you’ve done a lot of science communication you start to see patterns. You notice the choices people make when they write a public talk or a TV script, the different goals and practical constraints that shape a piece. I’ve likened it to watching an old kung fu movie and seeing where the wires are.

I don’t have a lot of experience doing science journalism, I can’t see the wires yet. But I’m starting to notice things, subtle elements like notes at a wine-tasting. Just like science communication by academics, science journalism is shaped by a variety of different goals.

First, there’s the need for news to be “new”. A classic news story is about something that happened recently, or even something that’s happening right now. Historical stories usually only show up as new “revelations”, something the journalist or a researcher recently dug up. This isn’t a strict requirement, and it seems looser in science journalism than in other types of journalism: sometimes you can have a piece on something cool the audience might not know, even if it’s not “new”. But it shapes how things are covered, it means that a piece on something old will often have something tying it back to a recent paper or an ongoing research topic.

Then, a news story should usually also be a “story”. Science communication can sometimes involve a grab-bag of different topics, like a TED talk that shows off a few different examples. Journalistic pieces often try to deliver one core message, with details that don’t fit the narrative needing to wait for another piece where they fit better. You might be tempted to round this off to saying that journalists are better writers than academics, since it’s easier for a reader to absorb one message than many. But I think it also ties to the structure. Journalists do have content with multiple messages, it just usually is not published as one story, but a thematic collection of stories.

Combining those two goals, there’s a tendency for news to focus on what happened. “First they had the idea, then there were challenges, then they made their discovery, now they look to the future.” You can’t just do that, though, because of another goal: pedagogy. Your audience doesn’t know everything you know. In order for them to understand what happened, there are often other things they have to understand. In non-science news, this can sometimes be brief, a paragraph that gives the background for people who have been “living under a rock”. In science news, there’s a lot more to explain. You have to teach something, and teaching well can demand a structure very different from the one-step-at a time narrative of what happened. Balancing these two is tricky, and it’s something I’m still learning how to do, as can be attested by the editors who’ve had to rearrange some of my pieces to make the story flow better.

News in general cares about being independent, about journalists who figure out the story and tell the truth regardless of what the people in power are saying. Science news is strange because, if a scientist gets covered at all, it’s almost always positive. Aside from the occasional scandal or replication crisis, science news tends to portray scientific developments as valuable, “good news” rather than “bad news”. If you’re a politician or a company, hearing from a journalist might make you worry. If you say the wrong thing, you might come off badly. If you’re a scientist, your biggest worry is that a journalist might twist your words into a falsehood that makes your work sound too good. On the other hand, a journalist who regularly publishes negative things about scientists would probably have a hard time finding scientists to talk to! There are basic journalistic ethics questions here that one probably learns about at journalism school and we who sneak in with no training have to learn another way.

These are the flavors I’ve tasted so far: novelty and narrative vs. education, positivity vs. accuracy. I’ll doubtless see more over the years, and go from someone who kind of knows what they’re doing to someone who can mentor others. With that in mind, I should get to writing!

Ways Freelance Journalism Is Different From Academic Writing

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A while back, I was surprised when I saw the writer of a well-researched webcomic assume that academics are paid for their articles. I ended up writing a post explaining how academic publishing actually works.

Now that I’m out of academia, I’m noticing some confusion on the other side. I’m doing freelance journalism, and the academics I talk to tend to have some common misunderstandings. So academics, this post is for you: a FAQ of questions I’ve been asked about freelance journalism. Freelance journalism is more varied than academia, and I’ve only been doing it a little while, so all of my answers will be limited to my experience.

Q: What happens first? Do they ask you to write something? Do you write an article and send it to them?

Academics are used to writing an article, then sending it to a journal, which sends it out to reviewers to decide whether to accept it. In freelance journalism in my experience, you almost never write an article before it’s accepted. (I can think of one exception I’ve run into, and that was for an opinion piece.)

Sometimes, an editor reaches out to a freelancer and asks them to take on an assignment to write a particular sort of article. This happens more freelancers that have been working with particular editors for a long time. I’m new to this, so the majority of the time I have to “pitch”. That means I email an editor describing the kind of piece I want to write. I give a short description of the topic and why it’s interesting. If the editor is interested, they’ll ask some follow-up questions, then tell me what they want me to focus on, how long the piece should be, and how much they’ll pay me. (The last two are related, many places pay by the word.) After that, I can write a draft.

Q: Wait, you’re paid by the word? Then why not make your articles super long, like Victor Hugo?

I’m paid per word assigned, not per word in the finished piece. The piece doesn’t have to strictly stick to the word limit, but it should be roughly the right size, and I work with the editor to try to get it there. In practice, places seem to have a few standard size ranges and internal terminology for what they are (“blog”, “essay”, “short news”, “feature”). These aren’t always the same as the categories readers see online. Some places have a web page listing these categories for prospective freelancers, but many don’t, so you have to either infer them from the lengths of articles online or learn them over time from the editors.

Q: Why didn’t you mention this important person or idea?

Because pieces pay more by the word, it’s easier as a freelancer to sell shorter pieces than longer ones. For science news, favoring shorter pieces also makes some pedagogical sense. People usually take away only a few key messages from a piece, if you try to pack in too much you run a serious risk of losing people. After I’ve submitted a draft, I work with the editor to polish it, and usually that means cutting off side-stories and “by-the-ways” to make the key points as vivid as possible.

Q: Do you do those cool illustrations?

Academia has a big focus on individual merit. The expectation is that when you write something, you do almost all of the work yourself, to the extent that more programming-heavy fields like physics and math do their own typesetting.

Industry, including journalism, is more comfortable delegating. Places will generally have someone on-staff to handle illustrations. I suggest diagrams that could be helpful to the piece and do a sketch of what they could look like, but it’s someone else’s job to turn that into nice readable graphic design.

Q: Why is the title like that? Why doesn’t that sound like you?

Editors in journalistic outlets are much more involved than in academic journals. Editors won’t just suggest edits, they’ll change wording directly and even input full sentences of their own. The title and subtitle of a piece in particular can change a lot (in part because they impact SEO), and in some places these can be changed by the editor quite late in the process. I’ve had a few pieces whose title changed after I’d signed off on them, or even after they first appeared.

Q: Are your pieces peer-reviewed?

The news doesn’t have peer review, no. Some places, like Quanta Magazine, do fact-checking. Quanta pays independent fact-checkers for longer pieces, while for shorter pieces it’s the writer’s job to verify key facts, confirming dates and the accuracy of quotes.

Q: Can you show me the piece before it’s published, so I can check it?

That’s almost never an option. Journalists tend to have strict rules about showing a piece before it’s published, related to more political areas where they want to preserve the ability to surprise wrongdoers and the independence to find their own opinions. Science news seems like it shouldn’t require this kind of thing as much, it’s not like we normally write hit pieces. But we’re not publicists either.

In a few cases, I’ve had people who were worried about something being conveyed incorrectly, or misleadingly. For those, I offer to do more in the fact-checking stage. I can sometimes show you quotes or paraphrase how I’m describing something, to check whether I’m getting something wrong. But under no circumstances can I show you the full text.

Q: What can I do to make it more likely I’ll get quoted?

Pieces are short, and written for a general, if educated, audience. Long quotes are harder to use because they eat into word count, and quotes with technical terms are harder to use because we try to limit the number of terms we ask the reader to remember. Quotes that mention a lot of concepts can be harder to find a place for, too: concepts are introduced gradually over the piece, so a quote that mentions almost everything that comes up will only make sense to the reader at the very end.

In a science news piece, quotes can serve a couple different roles. They can give authority, an expert’s judgement confirming that something is important or real. They can convey excitement, letting the reader see a scientist’s emotions. And sometimes, they can give an explanation. This last only happens when the explanation is very efficient and clear. If the journalist can give a better explanation, they’re likely to use that instead.

So if you want to be quoted, keep that in mind. Try to say things that are short and don’t use a lot of technical jargon or bring in too many concepts at once. Convey judgement, which things are important and why, and convey passion, what drives you and excited you about a topic. I am allowed to edit quotes down, so I can take a piece of a longer quote that’s cleaner or cut a long list of examples from an otherwise compelling statement. I can correct grammar and get rid of filler words and obvious mistakes. But I can’t put words in your mouth, I have to work with what you actually said, and if you don’t say anything I can use then you won’t get quoted.

At Quanta This Week, With a Piece on Multiple Imputation

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I’ve got another piece in Quanta Magazine this week.

While my past articles in Quanta have been about physics, this time I’m stretching my science journalism muscles in a new direction. I was chatting with a friend who works for a pharmaceutical company, and he told me about a statistical technique that sounded ridiculous. Luckily, he’s a patient person, and after annoying him and a statistician family member for a while I understood that the technique actually made sense. Since I love sharing counterintuitive facts, I thought this would be a great story to share with Quanta’s readers. I then tracked down more statisticians, and annoyed them in a more professional way, finally resulting in the Quanta piece.

The technique is called multiple imputation, and is a way to deal with missing data. By filling in (“imputing”) missing information with good enough guesses, you can treat a dataset with missing data as if it was complete. If you do this imputation multiple times with the help of a source of randomness, you can also model how uncertain those guesses are, so your final statistical estimates are as uncertain as they ought to be. That, in a nutshell, is multiple imputation.

In the piece, I try to cover the key points: how the technique came to be, how it spread, and why people use it. To complement that, in this post I wanted to get a little bit closer to the technical details, and say a bit about why some of the workarounds a naive physicist would come up with don’t actually work.

If you’re anything like me, multiple imputation sounds like a very weird way to deal with missing data. In order to fill in missing data, you have to use statistical techniques to find good guesses. Why can’t you just use the same techniques to analyze the data in the first place? And why do you have to use a random number generator to model your uncertainty, instead of just doing propagation of errors?

It turns out, you can sort of do both of these things. Full Information Maximum Likelihood is a method where you use all the data you have, and only the data you have, without imputing anything or throwing anything out. The catch is that you need a model, one with parameters you can try to find the most likely values for. Physicists usually do have a model like this (for example, the Standard Model), so I assumed everyone would. But for many things you want to measure in social science and medicine, you don’t have any such model, so multiple imputation ends up being more versatile in practice.

(If you want more detail on this, you need to read something written by actual statisticians. The aforementioned statistician family member has a website here that compares and contrasts multiple imputation with full information maximum likelihood.)

What about the randomness? It turns out there is yet another technique, called Fractional Imputation. While multiple imputation randomly chooses different values to impute, fractional imputation gives each value a weight based on the chance for it to come up. This gives the same result…if you can compute the weights, and store all the results. The impression I’ve gotten is that people are working on this, but it isn’t very well-developed.

“Just do propagation of errors”, the thing I wanted to suggest as a physicist, is much less of an option. In many of these datasets, you don’t attribute errors to the base data points to begin with. And on the other hand, if you want to be more sophisticated, then something like propagation of errors is too naive. You have a variety of different variables, correlated with each other in different ways, giving a complicated multivariate distribution. Propagation of errors is already pretty fraught when you go beyond linear relationships (something they don’t tend to tell baby physicists), using it for this would be pushing it rather too far.

The thing I next wanted to suggest, “just carry the distribution through the calculation”, turns out to relate to something I’ve called the “one philosophical problem of my sub-field”. In the area of physics I’ve worked in, a key question is what it means to have “done” an integral. Here, one can ask what it means to do a calculation on a distribution. In both cases, the end goal is to get numbers out: physics predictions on the one hand, statistical estimates on the other. You can get those numbers by “just” doing numerics, using randomness and approximations to estimate the number you’re interested in. And in a way, that’s all you can do. Any time you “just do the integral” or “just carry around the distribution”, the thing you get in the end is some function: it could be a well-understood function like a sine or log, or it could be an exotic function someone defined for that purpose. But whatever function you get, you get numbers out of it the same way. A sine or a log, on a computer, is just an approximation scheme, a program that outputs numbers.

(But we do still care about analytic results, we don’t “just” do numerics. That’s because understanding the analytics helps us do numerics better, we can get more precise numbers faster and more stably. If you’re just carrying around some arbitrarily wiggly distribution, it’s not clear you can do that.)

So at this point, I get it. I’m still curious to see how Fractional Imputation develops, and when I do have an actual model I’d lean to wanting to use Full Information Maximum Likelihood instead. (And there are probably some other caveats I may need to learn at some point!) But I’m comfortable with the idea that Multiple Imputation makes sense for the people using it.

Clickbait or Koan

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Last month, I had a post about a type of theory that is, in a certain sense, “immune to gravity”. These theories don’t allow you to build antigravity machines, and they aren’t totally independent of the overall structure of space-time. But they do ignore the core thing most people think of as gravity, the curvature of space that sends planets around the Sun and apples to the ground. And while that trait isn’t something we can use for new technology, it has led to extremely productive conversations between mathematicians and physicists.

After posting, I had some interesting discussions on twitter. A few people felt that I was over-hyping things. Given all the technical caveats, does it really make sense to say that these theories defy gravity? Isn’t a title like “Gravity-Defying Theories” just clickbait?

Obviously, I don’t think so.

There’s a concept in education called inductive teaching. We remember facts better when they come in context, especially the context of us trying to solve a puzzle. If you try to figure something out, and then find an answer, you’re going to remember that answer better than if you were just told the answer from the beginning. There are some similarities here to the concept of a Zen koan: by asking questions like “what is the sound of one hand clapping?” a Zen master is supposed to get you to think about the world in a different way.

When I post with a counterintuitive title, I’m aiming for that kind of effect. I know that you’ll read the title and think “that can’t be right!” Then you’ll read the post, and hear the explanation. That explanation will stick with you better because you asked that question, because “how can that be right?” is the solution to a puzzle that, in that span of words, you cared about.

Clickbait is bad for two reasons. First, it sucks you in to reading things that aren’t actually interesting. I write my blog posts because I think they’re interesting, so I hope I avoid that. Second, it can spread misunderstandings. I try to be careful about these, and I have some tips how you can be too:

  1. Correct the misunderstanding early. If I’m worried a post might be misunderstood in a clickbaity way, I make sure that every time I post the link I include a sentence discouraging the misunderstanding. For example, for the post on Gravity-Defying Theories, before the link I wrote “No flying cars, but it is technically possible for something to be immune to gravity”. If I’m especially worried, I’ll also make sure that the first paragraph of the piece corrects the misunderstanding as well.
  2. Know your audience. This means both knowing the normal people who read your work, and how far something might go if it catches on. Your typical readers might be savvy enough to skip the misunderstanding, but if they latch on to the naive explanation immediately then the “koan” effect won’t happen. The wider your reach can be, the more careful you need to be about what you say. If you’re a well-regarded science news piece, don’t write a title saying that scientists have built a wormhole.
  3. Have enough of a conclusion to be “worth it”. This is obviously a bit subjective. If your post introduces a mystery and the answer is that you just made some poetic word choice, your audience is going to feel betrayed, like the puzzle they were considering didn’t have a puzzly answer after all. Whatever you’re teaching in your post, it needs to have enough “meat” that solving it feels like a real discovery, like the reader did some real work to solve it.

I don’t think I always live up to these, but I do try. And I think trying is better than the conservative option, of never having catchy titles that make counterintuitive claims. One of the most fun aspects of science is that sometimes a counterintuitive fact is actually true, and that’s an experience I want to share.

At Quanta This Week, and Some Bonus Material

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When I moved back to Denmark, I mentioned that I was planning to do more science journalism work. The first fruit of that plan is up this week: I have a piece at Quanta Magazine about a perennially trendy topic in physics, the S-matrix.

It’s been great working with Quanta again. They’ve been thorough, attentive to the science, and patient with my still-uncertain life situation. I’m quite likely to have more pieces there in future, and I’ve got ideas cooking with other outlets as well, so stay tuned!

My piece with Quanta is relatively short, the kind of thing they used to label a “blog” rather than say a “feature”. Since the S-matrix is a pretty broad topic, there were a few things I couldn’t cover there, so I thought it would be nice to discuss them here. You can think of this as a kind of “bonus material” section for the piece. So before reading on, read my piece at Quanta first!

Welcome back!

At Quanta I wrote a kind of cartoon of the S-matrix, asking you to think about it as a matrix of probabilities, with rows for input particles and columns for output particles. There are a couple different simplifications I snuck in there, the pop physicist’s “lies to children“. One, I already flag in the piece: the entries aren’t really probabilities, they’re complex numbers, probability amplitudes.

There’s another simplification that I didn’t have space to flag. The rows and columns aren’t just lists of particles, they’re lists of particles in particular states.

What do I mean by states? A state is a complete description of a particle. A particle’s state includes its energy and momentum, including the direction it’s traveling in. It includes its spin, and the direction of its spin: for example, clockwise or counterclockwise? It also includes any charges, from the familiar electric charge to the color of a quark.

This makes the matrix even bigger than you might have thought. I was already describing an infinite matrix, one where you can have as many columns and rows as you can imagine numbers of colliding particles. But the number of rows and columns isn’t just infinite, but uncountable, as many rows and columns as there are different numbers you can use for energy and momentum.

For some of you, an uncountably infinite matrix doesn’t sound much like a matrix. But for mathematicians familiar with vector spaces, this is totally reasonable. Even if your matrix is infinite, or even uncountably infinite, it can still be useful to think about it as a matrix.

Another subtlety, which I’m sure physicists will be howling at me about: the Higgs boson is not supposed to be in the S-matrix!

In the article, I alluded to the idea that the S-matrix lets you “hide” particles that only exist momentarily inside of a particle collision. The Higgs is precisely that sort of particle, an unstable particle. And normally, the S-matrix is supposed to only describe interactions between stable particles, particles that can survive all the way to infinity.

In my defense, if you want a nice table of probabilities to put in an article, you need an unstable particle: interactions between stable particles depend on their energy and momentum, sometimes in complicated ways, while a single unstable particle will decay into a reliable set of options.

More technically, there are also contexts in which it’s totally fine to think about an S-matrix between unstable particles, even if it’s not usually how we use the idea.

My piece also didn’t have a lot of room to discuss new developments. I thought at minimum I’d say a bit more about the work of the young people I mentioned. You can think of this as an appetizer: there are a lot of people working on different aspects of this subject these days.

Part of the initial inspiration for the piece was when an editor at Quanta noticed a recent paper by Christian Copetti, Lucía Cordova, and Shota Komatsu. The paper shows an interesting case, where one of the “logical” conditions imposed in the original S-matrix bootstrap doesn’t actually apply. It ended up being too technical for the Quanta piece, but I thought I could say a bit about it, and related questions, here.

Some of the conditions imposed by the original bootstrappers seem unavoidable. Quantum mechanics makes no sense if doesn’t compute probabilities, and probabilities can’t be negative, or larger than one, so we’d better have an S-matrix that obeys those rules. Causality is another big one: we probably shouldn’t have an S-matrix that lets us send messages back in time and change the past.

Other conditions came from a mixture of intuition and observation. Crossing is a big one here. Crossing tells you that you can take an S-matrix entry with in-coming particles, and relate it to a different S-matrix entry with out-going anti-particles, using techniques from the calculus of complex numbers.

Crossing may seem quite obscure, but after some experience with S-matrices it feels obvious and intuitive. That’s why for an expert, results like the paper by Copetti, Cordova, and Komatsu seem so surprising. What they found was that a particularly exotic type of symmetry, called a non-invertible symmetry, was incompatible with crossing symmetry. They could find consistent S-matrices for theories with these strange non-invertible symmetries, but only if they threw out one of the basic assumptions of the bootstrap.

This was weird, but upon reflection not too weird. In theories with non-invertible symmetries, the behaviors of different particles are correlated together. One can’t treat far away particles as separate, the way one usually does with the S-matrix. So trying to “cross” a particle from one side of a process to another changes more than it usually would, and you need a more sophisticated approach to keep track of it. When I talked to Cordova and Komatsu, they related this to another concept called soft theorems, aspects of which have been getting a lot of attention and funding of late.

In the meantime, others have been trying to figure out where the crossing rules come from in the first place.

There were attempts in the 1970’s to understand crossing in terms of other fundamental principles. They slowed in part because, as the original S-matrix bootstrap was overtaken by QCD, there was less motivation to do this type of work anymore. But they also ran into a weird puzzle. When they tried to use the rules of crossing more broadly, only some of the things they found looked like S-matrices. Others looked like stranger, meaningless calculations.

A recent paper by Simon Caron-Huot, Mathieu Giroux, Holmfridur Hannesdottir, and Sebastian Mizera revisited these meaningless calculations, and showed that they aren’t so meaningless after all. In particular, some of them match well to the kinds of calculations people wanted to do to predict gravitational waves from colliding black holes.

Imagine a pair of black holes passing close to each other, then scattering away in different directions. Unlike particles in a collider, we have no hope of catching the black holes themselves. They’re big classical objects, and they will continue far away from us. We do catch gravitational waves, emitted from the interaction of the black holes.

This different setup turns out to give the problem a very different character. It ends up meaning that instead of the S-matrix, you want a subtly different mathematical object, one related to the original S-matrix by crossing relations. Using crossing, Caron-Huot, Giroux, Hannesdottir and Mizera found many different quantities one could observe in different situations, linked by the same rules that the original S-matrix bootstrappers used to relate S-matrix entries.

The work of these two groups is just some of the work done in the new S-matrix program, but it’s typical of where the focus is going. People are trying to understand the general rules found in the past. They want to know where they came from, and as a consequence, when they can go wrong. They have a lot to learn from the older papers, and a lot of new insights come from diligent reading. But they also have a lot of new insights to discover, based on the new tools and perspectives of the modern day. For the most part, they don’t expect to find a new unified theory of physics from bootstrapping alone. But by learning how S-matrices work in general, they expect to find valuable knowledge no matter how the future goes.

If That Measures the Quantum Vacuum, Anything Does

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Sabine Hossenfelder has gradually transitioned from critical written content about physics to YouTube videos, mostly short science news clips with the occasional longer piece. Luckily for us in the unable-to-listen-to-podcasts demographic, the transcripts of these videos are occasionally published on her organization’s Substack.

Unluckily, it feels like the short news format is leading to some lazy metaphors. There are stories science journalists sometimes tell because they’re easy and familiar, even if they don’t really make sense. Scientists often tell them too, for the same reason. But the more careful voices avoid them.

Hossenfelder has been that careful before, but one of her recent pieces falls short. The piece is titled “This Experiment Will Measure Nothing, But Very Precisely”.

The “nothing” in the title is the oft-mythologized quantum vacuum. The story goes that in quantum theory, empty space isn’t really empty. It’s full of “virtual” particles, that pop in and out of existence, jostling things around.

This…is not a good way to think about it. Really, it’s not. If you want to understand what’s going on physically, it’s best to think about measurements, and measurements involve particles: you can’t measure anything in pure empty space, you don’t have anything to measure with. Instead, every story you can tell about the “quantum vacuum” and virtual particles, you can tell about interactions between particles that actually exist.

(That post I link above, by the way, was partially inspired by a more careful post by Hossenfelder. She does know this stuff. She just doesn’t always use it.)

Let me tell the story Hossenfelder’s piece is telling, in a less silly way:

In the earliest physics classes, you learn that light does not affect other light. Shine two flashlight beams across each other, and they’ll pass right through. You can trace the rays of each source, independently, keeping track of how they travel and bounce around the room.

In quantum theory, that’s not quite true. Light can interact with light, through subtle quantum effects. This effect is tiny, so tiny it hasn’t been measured before. But with ingenious tricks involving tuning three different lasers in exactly the right way, a team of physicists in Dresden has figured out how it could be done.

And see, that’s already cool, right? It’s cool when people figure out how to see things that have never been seen before, full stop.

But the way Hossenfelder presents it, the cool thing about this is that they are “measuring nothing”. That they’re measuring “the quantum vacuum”, really precisely.

And I mean, you can say that, I guess. But it’s equally true of every subtle quantum effect.

In classical physics, electrons should have a very specific behavior in a magnetic field, called their magnetic moment. Quantum theory changes this: electrons have a slightly different magnetic moment, an anomalous magnetic moment. And people have measured this subtle effect: it’s famously the most precisely confirmed prediction in all of science.

That effect can equally well be described as an effect of the quantum vacuum. You can draw the same pictures, if you really want to, with virtual particles popping in and out of the vacuum. One effect (light bouncing off light) doesn’t exist at all in classical physics, while the other (electrons moving in a magnetic field) exists, but is subtly different. But both, in exactly the same sense, are “measurements of nothing”.

So if you really want to stick on the idea that, whenever you measure any subtle quantum effect, you measure “the quantum vacuum”…then we’re already doing that, all the time. Using it to popularize some stuff (say, this experiment) and not other stuff (the LHC is also measuring the quantum vacuum) is just inconsistent.

Better, in my view, to skip the silly talk about nothing. Talk about what we actually measure. It’s cool enough that way.

Stories Backwards and Forwards

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You can always start with “once upon a time”…

I come up with tricks to make calculations in particle physics easier. That’s my one-sentence story, or my most common one. If I want to tell a longer story, I have more options.

Here’s one longer story:

I want to figure out what Nature is telling us. I want to take all the data we have access to that has anything to say about fundamental physics, every collider and gravitational wave telescope and ripple in the overall structure of the universe, and squeeze it as hard as I can until something comes out. I want to make sure we understand the implications of our current best theories as well as we can, to as high precision as we can, because I want to know whether they match what we see.

To do that, I am starting with a type of calculation I know how to do best. That’s both because I can make progress with it, and because it will be important for making these inferences, for testing our theories. I am following a hint in a theory that definitely does not describe the real world, one that is both simpler to work with and surprisingly complex, one that has a good track record, both for me and others, for advancing these calculations. And at the end of the day, I’ll make our ability to infer things from Nature that much better.

Here’s another:

Physicists, unknowing, proposed a kind of toy model, one often simpler to work with but not necessarily simpler to describe. Using this model, they pursued increasingly elaborate calculations, and time and time again, those calculations surprised them. The results were not random, not a disorderly mess of everything they could plausibly have gotten. Instead, they had structure, symmetries and patterns and mathematical properties that the physicists can’t seem to explain. If we can explain them, we will advance our knowledge of models and theories and ideas, geometry and combinatorics, learning more about the unexpected consequences of the rules we invent.

We can also help the physicists advance physics, of course. That’s a happy accident, but one that justifies the money and time, showing the rest of the world that understanding consequences of rules is still important and valuable.

These seem like very different stories, but they’re not so different. They change in order, physics then math or math then physics, backwards and forwards. By doing that, they change in emphasis, in where they’re putting glory and how they’re catching your attention. But at the end of the day, I’m investigating mathematical mysteries, and I’m advancing our ability to do precision physics.

(Maybe you think that my motivation must lie with one of these stories and not the other. One is “what I’m really doing”, the other is a lie made up for grant agencies.
Increasingly, I don’t think people work like that. If we are at heart stories, we’re retroactive stories. Our motivation day to day doesn’t follow one neat story or another. We move forward, we maybe have deep values underneath, but our accounts of “why” can and will change depending on context. We’re human, and thus as messy as that word should entail.)

I can tell more than two stories if I want to. I won’t here. But this is largely what I’m working on at the moment. In applying for grants, I need to get the details right, to sprinkle the right references and the right scientific arguments, but the broad story is equally important. I keep shuffling that story, a pile of not-quite-literal index cards, finding different orders and seeing how they sound, imagining my audience and thinking about what stories would work for them.

Small Shifts for Specificity

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Cosmologists are annoyed at a recent spate of news articles claiming the universe is 26.7 billion years old (rather than 13.8 billion as based on the current best measurements). To some of the science-reading public, the news sounds like a confirmation of hints they’d already heard: about an ancient “Methuselah” star that seemed to be older than the universe (later estimates put it younger), and recent observations from the James Webb Space Telescope of early galaxies that look older than they ought.

“The news doesn’t come from a telescope, though, or a new observation of the sky. Instead, it comes from this press release from the University of Ottawa: “Reinventing cosmology: uOttawa research puts age of universe at 26.7 — not 13.7 — billion years”.

(If you look, you’ll find many websites copying this press release almost word-for-word. This is pretty common in science news, where some websites simply aggregate press releases and others base most of their science news on them rather than paying enough for actual journalism.)

The press release, in turn, is talking about a theory, not an observation. The theorist, Rajendra Gupta, was motivated by examples like the early galaxies observed by JWST and the Methuselah star. Since the 13.8 billion year age of the universe is based on a mathematical model, he tried to find a different mathematical model that led to an older universe. Eventually, by hypothesizing what seems like every unproven physics effect he could think of, he found one that gives a different estimate, 26.7 billion. He probably wasn’t the first person to do this, because coming up with different models to explain odd observations is a standard thing cosmologists do all the time, and until one of the models is shown to explain a wider range of observations (because our best theories explain a lot, so they’re hard to replace), they’re just treated as speculation, not newsworthy science.

This is a pretty clear case of hype, and as such most of the discussion has been about what went wrong. Should we blame the theorist? The university? The journalists? Elon Musk?

Rather than blame, I think it’s more productive to offer advice. And in this situation, the person I think could use some advice is the person who wrote the press release.

So suppose you work for a university, writing their press releases. One day, you hear that one of your professors has done something very cool, something worthy of a press release: they’ve found a new estimate for the age of the universe. What do you do?

One thing you absolutely shouldn’t do is question the science. That just isn’t your job, and even if it were you don’t have the expertise to do that. Anyone who’s hoping that you will only write articles about good science and not bad science is being unrealistic, that’s just not an option.

If you can’t be more accurate, though, you can still be more precise. You can write your article, and in particular your headline, so that you express what you do know as clearly and specifically as possible.

(I’m assuming here you write your own headlines. This is not normal in journalism, where most headlines are written by an editor, not by the writer of a piece. But university press offices are small enough that I’m assuming, perhaps incorrectly, that you can choose how to title your piece.)

Let’s take a look at the title, “Reinventing cosmology: uOttawa research puts age of universe at 26.7 — not 13.7 — billion years”, and see if we can make some small changes to improve it.

One very general word in that title is “research”. Lots of people do research: astronomers do research when they collect observations, theorists do research when they make new models. If you say “research”, some people will think you’re reporting a new observation, a new measurement that gives a radically different age for the universe.

But you know that’s not true, it’s not what the scientist you’re talking to is telling you. So to avoid the misunderstanding, you can get a bit more specific, and replace the word “research” with a more precise one: “Reinventing cosmology: uOttawa theory puts age of universe at 26.7 — not 13.7 — billion years”.

“Theory” is just as familiar a word as “research”. You won’t lose clicks, you won’t confuse people. But now, you’ve closed off a big potential misunderstanding. By a small shift, you’ve gotten a lot clearer. And you didn’t need to question the science to do it!

You can do more small shifts, if you understand a bit more of the science. “Puts” is kind of ambiguous: a theory could put an age somewhere because it computes it from first principles, or because it dialed some parameter to get there. Here, the theory was intentionally chosen to give an older universe, so the title should hint at this in some way. Instead of “puts”, then, you can use “allows”: “Reinventing cosmology: uOttawa theory allows age of universe to be 26.7 — not 13.7 — billion years”.

These kinds of little tricks can be very helpful. If you’re trying to avoid being misunderstood, then it’s good to be as specific as you can, given what you understand. If you do it carefully, you don’t have to question your scientists’ ideas or downplay their contributions. You can do your job, promote your scientists, and still contribute to responsible journalism.

Whatever Happened to the Nonsense Merchants?

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I was recently reminded that Michio Kaku exists.

In the past, Michio Kaku made important contributions to string theory, but he’s best known for what could charitably be called science popularization. He’s an excited promoter of physics and technology, but that excitement often strays into inaccuracy. Pretty much every time I’ve heard him mentioned, it’s for some wildly overenthusiastic statement about physics that, rather than just being simplified for a general audience, is generally flat-out wrong, conflating a bunch of different developments in a way that makes zero actual sense.

Michio Kaku isn’t unique in this. There’s a whole industry in making nonsense statements about science, overenthusiastic books and videos hinting at science fiction or mysticism. Deepak Chopra is a famous figure from deeper on this spectrum, known for peddling loosely quantum-flavored spirituality.

There was a time I was worried about this kind of thing. Super-popular misinformation is the bogeyman of the science popularizer, the worry that for every nice, careful explanation we give, someone else will give a hundred explanations that are way more exciting and total baloney. Somehow, though, I hear less and less from these people over time, and thus worry less and less about them.

Should I be worried more? I’m not sure.

Are these people less popular than they used to be? Is that why I’m hearing less about them? Possibly, but I’d guess not. Michio Kaku has eight hundred thousand twitter followers. Deepak Chopra has three million. On the other hand, the usually-careful Brian Greene has a million followers, and Neil deGrasse Tyson, where the worst I’ve heard is that he can be superficial, has fourteen million.

(But then in practice, I’m more likely to reflect on content with even smaller audiences.)

If misinformation is this popular, shouldn’t I be doing more to combat it?

Popular misinformation is also going to be popular among critics. For every big-time nonsense merchant, there are dozens of people breaking down and debunking every false statement they say, every piece of hype they release. Often, these people will end up saying the same kinds of things over and over again.

If I can be useful, I don’t think it will be by saying the same thing over and over again. I come up with new metaphors, new descriptions, new explanations. I clarify things others haven’t clarified, I clear up misinformation others haven’t addressed. That feels more useful to me, especially in a world where others are already countering the big problems. I write, and writing lasts, and can be used again and again when needed. I don’t need to keep up with the Kakus and Chopras of the world to do that.

(Which doesn’t imply I’ll never address anything one of those people says…but if I do, it will be because I have something new to say back!)