Tag Archives: science communication

Bonus Info on the LHC and Beyond

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Three of my science journalism pieces went up last week!

(This is a total coincidence. One piece was a general explainer “held in reserve” for a nice slot in the schedule, one was a piece I drafted in February, while the third I worked on in May. In journalism, things take as long as they take.)

The shortest piece, at Quanta Magazine, was an explainer about the two types of particles in physics: bosons, and fermions.

I don’t have a ton of bonus info here, because of how tidy the topic is, so just two quick observations.

First, I have the vague impression that Bose, bosons’ namesake, is “claimed” by both modern-day Bangladesh and India. I had friends in grad school who were proud of their fellow physicist from Bangladesh, but while he did his most famous work in Dhaka, he was born and died in Calcutta. Since both were under British India for most of his life, these things likely get complicated.

Second, at the end of the piece I mention a “world on a wire” where fermions and bosons are the same. One example of such a “wire” is a string, like in string theory. One thing all young string theorists learn is “bosonization”: the idea that, in a 1+1-dimensional world like a string, you can re-write any theory with fermions as a theory with bosons, as well as vice versa. This has important implications for how string theory is set up.

Next, in Ars Technica, I had a piece about how LHC physicists are using machine learning to untangle the implications of quantum interference.

As a journalist, it’s really easy to fall into a trap where you give the main person you interview too much credit: after all, you’re approaching the story from their perspective. I tried to be cautious about this, only to be stymied when literally everyone else I interviewed praised Aishik Ghosh to the skies and credited him with being the core motivating force behind the project. So I shrugged my shoulders and followed suit. My understanding is that he has been appropriately rewarded and will soon be a professor at Georgia Tech.

I didn’t list the inventors of the NSBI method that Ghosh and co. used, but names like Kyle Cranmer and Johann Brehmer tend to get bandied about. It’s a method that was originally explored for a more general goal, trying to characterize what the Standard Model might be missing, while the work I talk about in the piece takes it in a new direction, closer to the typical things the ATLAS collaboration looks for.

I also did not say nearly as much as I was tempted to about how the ATLAS collaboration publishes papers, which was honestly one of the most intriguing parts of the story for me. There is a huge amount of review that goes on inside ATLAS before one of their papers reaches the outside world, way more than there ever is in a journal’s peer review process. This is especially true for “physics papers”, where ATLAS is announcing a new conclusion about the physical world, as ATLAS’s reputation stands on those conclusions being reliable. That means starting with an “internal note” that’s hundreds of pages long (and sometimes over a thousand), an editorial board that manages the editing process, disseminating the paper to the entire collaboration for comment, and getting specific experts and institute groups within the collaboration to read through the paper in detail. The process is a bit less onerous for “technical papers”, which describe a new method, not a new conclusion about the world. Still, it’s cumbersome enough that for those papers, often scientists don’t publish them “within ATLAS” at all, instead releasing them independently. The results I reported on are special because they involved a physics paper and a technical paper, both within the ATLAS collaboration process. Instead of just working with partial or simplified data, they wanted to demonstrate the method on a “full analysis”, with all the computation and human coordination that requires. Normally, ATLAS wouldn’t go through the whole process of publishing a physics paper without basing it on new data, but this was different: the method had the potential to be so powerful that the more precise results would be worth stating as physics results alone.

(Also, for the people in the comments worried about training a model on old data: that’s not what they did. In physics, they don’t try to train a neural network model to predict the results of colliders, such a model wouldn’t tell us anything useful. They run colliders to tell us whether what they see matches the analytic, Standard, model. The neural network is trained to predict not what the experiment will say, but what the Standard Model will say, as we can usually only figure that out through time-consuming simulations. So it’s trained on (new) simulations, not on experimental data.)

Finally, on Friday I had a piece in Physics Today about the European Strategy for Particle Physics (or ESPP), and in particular, plans for the next big collider.

Before I even started working on this piece, I saw a thread by Patrick Koppenburg on some of the 263 documents submitted for the ESPP update. While my piece ended up mostly focused on the big circular collider plan that most of the field is converging on (the future circular collider, or FCC), Koppenburg’s thread was more wide-ranging, meant to illustrate the breadth of ideas under discussion. Some of that discussion is about the LHC’s current plans, like its “high-luminosity” upgrade that will see it gather data at much higher rates up until 2040. Some of it is assessing broader concerns, which it may surprise some of you to learn includes sustainability: yes, there are more or less sustainable ways to build giant colliders.

The most fun part of the discussion, though, concerns all of the other collider proposals.

Some report progress on new technologies. Muon colliders are the most famous of these, but there are other proposals that would specifically help with a linear collider. I never did end up understanding what Cooled Copper Colliders are all about, beyond that they let you get more energy in a smaller machine without super-cooling. If you know about them, chime in in the comments! Meanwhile, plasma wakefield acceleration could accelerate electrons on a wave of plasma. This has the disadvantage that you want to collide electrons and positrons, and if you try to stick a positron in plasma it will happily annihilate with the first electron it meets. So what do you do? You go half-and-half, with the HALHF project: speed up the electron with a plasma wakefield, accelerate the positron normally, and have them meet in the middle.

Others are backup plans, or “budget options”, where CERN could get a bit better measurements on some parameters if they can’t stir up the funding to measure the things they really want. They could put electrons and positrons into the LHC tunnel instead of building a new one, for a weaker machine that could still study the Higgs boson to some extent. They could use a similar experiment to produce Z bosons instead, which could serve as a bridge to a different collider project. Or, they could collider the LHC’s proton beam with an electron beam, for an experiment that mixes advantages and disadvantages of some of the other approaches.

While working on the piece, one resource I found invaluable was this colloquium talk by Tristan du Pree, where he goes through the 263 submissions and digs up a lot of interesting numbers and commentary. Read the slides for quotes from the different national inputs and “solo inputs” with comments from particular senior scientists. I used that talk to get a broad impression of what the community was feeling, and it was interesting how well it was reflected in the people I interviewed. The physicist based in Switzerland felt the most urgency for the FCC plan, while the Dutch sources were more cautious, with other Europeans firmly in the middle.

Going over the FCC report itself, one thing I decided to leave out of the discussion was the cost-benefit analysis. There’s the potential for a cute sound-bite there, “see, the collider is net positive!”, but I’m pretty skeptical of the kind of analysis they’re doing there, even if it is standard practice for government projects. Between the biggest benefits listed being industrial benefits to suppliers and early-career researcher training (is a collider unusually good for either of those things, compared to other ways we spend money?) and the fact that about 10% of the benefit is the science itself (where could one possibly get a number like that?), it feels like whatever reasoning is behind this is probably the kind of thing that makes rigor-minded economists wince. I wasn’t able to track down the full calculation though, so I really don’t know, maybe this makes more sense than it looks.

I think a stronger argument than anything along those lines is a much more basic point, about expertise. Right now, we have a community of people trying to do something that is not merely difficult, but fundamental. This isn’t like sending people to space, where many of the engineering concerns will go away when we can send robots instead. This is fundamental engineering progress in how to manipulate the forces of nature (extremely powerful magnets, high voltages) and process huge streams of data. Pushing those technologies to the limit seems like it’s going to be relevant, almost no matter what we end up doing. That’s still not putting the science first and foremost, but it feels a bit closer to an honest appraisal of what good projects like this do for the world.

Branching Out, and Some Ground Rules

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In January, my time at the Niels Bohr Institute ended. Instead of supporting myself by doing science, as I’d done the last thirteen or so years, I started making a living by writing, doing science journalism.

That work picked up. My readers here have seen a few of the pieces already, but there are lots more in the pipeline, getting refined by editors or waiting to be published. It’s given me a bit of income, and a lot of visibility.

That visibility, in turn, has given me new options. It turns out that magazines aren’t the only companies interested in science writing, and journalism isn’t the only way to write for a living. Companies that invest in science want a different kind of writing, one that builds their reputation both with the public and with the scientific community. And as I’ve discovered, if you have enough of a track record, some of those companies will reach out to you.

So I’m branching out, from science journalism to science communications consulting, advising companies how to communicate science. I’ve started working with an exciting client, with big plans for the future. If you follow me on LinkedIn, you’ll have seen a bit about who they are and what I’ll be doing for them.

Here on the blog, I’d like to maintain a bit more separation. Blogging is closer to journalism, and in journalism, one ought to be careful about conflicts of interest. The advice I’ve gotten is that it’s good to establish some ground rules, separating my communications work from my journalistic work, since I intend to keep doing both.

So without further ado, my conflict of interest rules:

  • I will not write in a journalistic capacity about my consulting clients, or their direct competitors.
  • I will not write in a journalistic capacity about the technology my clients are investing in, except in extremely general terms. (For example, most businesses right now are investing in AI. I’ll still write about AI in general, but not about any particular AI technologies my clients are pursuing.)
  • I will more generally maintain a distinction between areas I cover journalistically and areas where I consult. Right now, this means I avoid writing in a journalistic capacity about:
    • Health/biomedical topics
    • Neuroscience
    • Advanced sensors for medical applications

I plan to update these rules over time as I get a better feeling for what kinds of conflict of interest risks I face and what my clients are comfortable with. I now have a Page for this linked in the top menu, clients and editors can check there to see my current conflict of interest rules.

Post on the Weak Gravity Conjecture for FirstPrinciples.org

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I have another piece this week on the FirstPrinciples.org Hub. If you’d like to know who they are, I say a bit about my impressions of them in my post on the last piece I had there. They’re still finding their niche, so there may be shifts in the kind of content they cover over time, but for now they’ve given me an opportunity to cover a few topics that are off the beaten path.

This time, the piece is what we in the journalism biz call an “explainer”. Instead of interviewing people about cutting-edge science, I wrote a piece to explain an older idea. It’s an idea that’s pretty cool, in a way I think a lot of people can actually understand: a black hole puzzle that might explain why gravity is the weakest force. It’s an idea that’s had an enormous influence, both in the string theory world where it originated and on people speculating more broadly about the rules of quantum gravity. If you want to learn more, read the piece!

Since I didn’t interview anyone for this piece, I don’t have the same sort of “bonus content” I sometimes give. Instead of interviewing, I brushed up on the topic, and the best resource I found was this review article written by Dan Harlow, Ben Heidenreich, Matthew Reece, and Tom Rudelius. It gave me a much better idea of the subtleties: how many different ways there are to interpret the original conjecture, and how different attempts to build on it reflect on different facets and highlight different implications. If you are a physicist curious what the whole thing is about, I recommend reading that review: while I try to give a flavor of some of the subtleties, a piece for a broad audience can only do so much.

There Is No Shortcut to Saying What You Mean

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Blogger Andrew Oh-Willeke of Dispatches from Turtle Island pointed me to an editorial in Science about the phrase scientific consensus.

The editorial argues that by referring to conclusions like the existence of climate change or vaccine safety as “the scientific consensus”, communicators have inadvertently fanned the flames of distrust. By emphasizing agreement between scientists, the phrase “scientific consensus” leaves open the question of how that consensus was reached. More conspiracy-minded people imagine shady backroom deals and corrupt payouts, while the more realistic blame incentives and groupthink. If you disagree with “the scientific consensus”, you may thus decide the best way forward is to silence those pesky scientists.

(The link to current events is left as an exercise to the reader, to comment on elsewhere. As usual, please no explicit discussion of politics on this blog!)

Instead of “scientific consensus”, the editorial suggests another term, convergence of evidence. The idea is that by centering the evidence instead of the scientists, the phrase would make it clear that these conclusions are justified by something more than social pressures, and will remain even if the scientists promoting them are silenced.

Oh-Willeke pointed me to another blog post responding to the editorial, which has a nice discussion of how the terms were used historically, showing their popularity over time. “Convergence of evidence” was more popular in the 1950’s, with a small surge in the late 90’s and early 2000’s. “Scientific consensus” rose in the 1980’s and 90’s, lining up with a time when social scientists were skeptical about science’s objectivity and wanted to explore the social reasons why scientists come to agreement. It then fell around the year 2000, before rising again, this time used instead by professional groups of scientists to emphasize their agreement on issues like climate change.

(The blog post then goes on to try to motivate the word “consilience” instead, on the rather thin basis that “convergence of evidence” isn’t interdisciplinary enough, which seems like a pretty silly objection. “Convergence” implies coming in from multiple directions, it’s already interdisciplinary!)

I appreciate “convergence of evidence”, it seems like a useful phrase. But I think the editorial is working from the wrong perspective, in trying to argue for which terms “we should use” in the first place.

Sometimes, as a scientist or an organization or a journalist, you want to emphasize evidence. Is it “a preponderance of evidence”, most but not all? Is it “overwhelming evidence”, evidence so powerful it is unlikely to ever be defeated? Or is it a “convergence of evidence”, evidence that came in slowly from multiple paths, each independent route making a coincidence that much less likely?

But sometimes, you want to emphasize the judgement of the scientists themselves.

Sometimes when scientists agree, they’re working not from evidence but from personal experience: feelings of which kinds of research pan out and which don’t, or shared philosophies that sit deep in how they conceive their discipline. Describing physicists’ reasons for expecting supersymmetry before the LHC turned on as a convergence of evidence would be inaccurate. Describing it as having been a (not unanimous) consensus gets much closer to the truth.

Sometimes, scientists do have evidence, but as a journalist, you can’t evaluate its strength. You note some controversy, you can follow some of the arguments, but ultimately you have to be honest about how you got the information. And sometimes, that will be because it’s what most of the responsible scientists you talked to agreed on: scientific consensus.

As science communicators, we care about telling the truth (as much as we ever can, at any rate). As a result, we cannot adopt blanket rules of thumb. We cannot say, “we as a community are using this term now”. The only responsible thing we can do is to think about each individual word. We need to decide what we actually mean, to read widely and learn from experience, to find which words express our case in a way that is both convincing and accurate. There’s no shortcut to that, no formula where you just “use the right words” and everything turns out fine. You have to do the work, and hope it’s enough.

Antimatter Isn’t Magic

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You’ve heard of antimatter, right?

For each type of particle, there is a rare kind of evil twin with the opposite charge, called an anti-particle. When an anti-proton meets a proton, they annihilate each other in a giant blast of energy.

I see a lot of questions online about antimatter. One recurring theme is people asking a very general question: how does antimatter work?

If you’ve just heard the pop physics explanation, antimatter probably sounds like magic. What about antimatter lets it destroy normal matter? Does it need to touch? How long does it take? And what about neutral particles like neutrons?

You find surprisingly few good explanations of this online, but I can explain why. Physicists like me don’t expect antimatter to be confusing in this way, because to us, antimatter isn’t doing anything all that special. When a particle and an antiparticle annihilate, they’re doing the same thing that any other pair of particles do when they do…basically anything else.

Instead of matter and antimatter, let’s talk about one of the oldest pieces of evidence for quantum mechanics, the photoelectric effect. Scientists shone light at a metal, and found that if the wavelength of the light was short enough, electrons would spring free, causing an electric current. If the wavelength was too long, the metal wouldn’t emit any electrons, no matter how much light they shone. Einstein won his Nobel prize for the explanation: the light hitting the metal comes in particle-sized pieces, called photons, whose energy is determined by the wavelength of the light. If the individual photons don’t have enough energy to get an electron to leave the metal, then no electron will move, no matter how many photons you use.

What happens to the photons after they hit the metal?

They go away. We say they are absorbed, an electron absorbs a photon and speeds up, increasing its kinetic energy so it can escape.

But we could just as easily say the photon is annihilated, if we wanted to.

In the photoelectric effect, you start with one electron and one photon, they come together, and you end up with one electron and no photon. In proton-antiproton annihilation, you start with a proton and an antiproton, they come together, and you end up with no protons or antiprotons, but instead “energy”…which in practice, usually means two photons.

That’s all that happens, deep down at the root of things. The laws of physics are rules about inputs and outputs. Start with these particles, they come together, you end up with these other particles. Sometimes one of the particles stays the same. Sometimes particles seem to transform, and different kinds of particles show up. Sometimes some of the particles are photons, and you think of them as “just energy”, and easy to absorb. But particles are particles, and nothing is “just energy”. Each thing, absorption, decay, annihilation, each one is just another type of what we call interactions.

What makes annihilation of matter and antimatter seem unique comes down to charges. Interactions have to obey the laws of physics: they conserve energy, they conserve momentum, and they conserve charge.

So why can an antiproton and a proton annihilate to pure photons, while two protons can’t? A proton and an antiproton have opposite charge, a photon has zero charge. You could combine two protons to make something else, but it would have to have the same charge as two protons.

What about neutrons? A neutron has no electric charge, so you might think it wouldn’t need antimatter. But a neutron has another type of charge, called baryon number. In order to annihilate one, you’d need an anti-neutron, which would still have zero electric charge but would have the opposite baryon number. (By the way, physicists have been making anti-neutrons since 1956.)

On the other hand, photons actually have no charge. So do Higgs bosons. So one Higgs boson can become two photons, without annihilating with anything else. Each of these particles can be called its own antiparticle: a photon is also an antiphoton, a Higgs is also an anti-Higgs.

Because particle-antiparticle annihilation follows the same rules as other interactions between particles, it also takes place via the same forces. When a proton and an antiproton annihilate each other, they typically do this via the electromagnetic force. This is why you end up with light, which is an electromagnetic wave. Like everything in the quantum world, this annihilation isn’t certain. Is has a chance to happen, proportional to the strength of the interaction force involved.

What about neutrinos? They also appear to have a kind of charge, called lepton number. That might not really be a conserved charge, and neutrinos might be their own antiparticles, like photons. However, they are much less likely to be annihilated than protons and antiprotons, because they don’t have electric charge, and thus their interaction doesn’t depend on the electromagnetic force, but on the much weaker weak nuclear force. A weaker force means a less likely interaction.

Antimatter might seem like the stuff of science fiction. But it’s not really harder to understand than anything else in particle physics.

(I know, that’s a low bar!)

It’s just interactions. Particles go in, particles go out. If it follows the rules, it can happen, if it doesn’t, it can’t. Antimatter is no different.

AI Can’t Do Science…And Neither Can Other Humans

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Seen on Twitter:

I don’t know the context here, so I can’t speak to what Prof. Cronin meant. But it got me thinking.

Suppose you, like Prof. Cronin, were to insist that AI “cannot in principle” do science, because AI “is not autonomous” and “does not come up with its own problems to solve”. What might you mean?

You might just be saying that AI is bad at coming up with new problems to solve. That’s probably fair, at least at the moment. People have experimented with creating simple “AI researchers” that “study” computer programs, coming up with hypotheses about the programs’ performance and testing them. But it’s a long road from that to reproducing the much higher standards human scientists have to satisfy.

You probably don’t mean that, though. If you did, you wouldn’t have said “in principle”. You mean something stronger.

More likely, you might mean that AI cannot come up with its own problems, because AI is a tool. People come up with problems, and use AI to help solve them. In this perspective, not only is AI “not autonomous”, it cannot be autonomous.

On a practical level, this is clearly false. Yes, machine learning models, the core technology in current AI, are set up to answer questions. A user asks something, and receives the model’s prediction of the answer. That’s a tool, but for the more flexible models like GPT it’s trivial to turn it into something autonomous. Just add another program: a loop that asks the model what to do, does it, tells the model the result, and asks what to do next. Like taping a knife to a Roomba, you’ve made a very simple modification to make your technology much more dangerous.

You might object, though, that this simple modification of GPT is not really autonomous. After all, a human created it. That human had some goal, some problem they wanted to solve, and the AI is just solving the problem for them.

That may be a fair description of current AI, but insisting it’s true in principle has some awkward implications. If you make a “physics AI”, just tell it to do “good physics”, and it starts coming up with hypotheses you’d never thought of, is it really fair to say it’s just solving your problem?

What if the AI, instead, was a child? Picture a physicist encouraging a child to follow in their footsteps, filling their life with physics ideas and rhapsodizing about the hard problems of the field at the dinner table. Suppose the child becomes a physicist in turn, and finds success later in life. Were they really autonomous? Were they really a scientist?

What if the child, instead, was a scientific field, and the parent was the general public? The public votes for representatives, the representatives vote to hire agencies, and the agencies promise scientists they’ll give them money if they like the problems they come up with. Who is autonomous here?

(And what happens if someone takes a hammer to that process? I’m…still not talking about this! No-politics-rule still in effect, sorry! I do have a post planned, but it will have to wait until I can deal with the fallout.)

At this point, you’d probably stop insisting. You’d drop that “in principle”, and stick with the claim I started with, that current AI can’t be a scientist.

But you have another option.

You can accept the whole chain of awkward implications, bite all the proverbial bullets. Yes, you insist, AI is not autonomous. Neither is the physicist’s child in your story, and neither are the world’s scientists paid by government grants. Each is a tool, used by the one, true autonomous scientist: you.

You are stuck in your skull, a blob of curious matter trained on decades of experience in the world and pre-trained with a couple billion years of evolution. For whatever reason, you want to know more, so you come up with problems to solve. You’re probably pretty vague about those problems. You might want to see more pretty pictures of space, or wrap your head around the nature of time. So you turn the world into your tool. You vote and pay taxes, so your government funds science. You subscribe to magazines and newspapers, so you hear about it. You press out against the world, and along with the pressure that already exists it adds up, and causes change. Biological intelligences and artificial intelligences scurry at your command. From their perspective, they are proposing their own problems, much more detailed and complex than the problems you want to solve. But from yours, they’re your limbs beyond limbs, sight beyond sight, asking the fundamental questions you want answered.

Bonus Material for “How Hans Bethe Stumbled Upon Perfect Quantum Theories”

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I had an article last week in Quanta Magazine. It’s a piece about something called the Bethe ansatz, a method in mathematical physics that was discovered by Hans Bethe in the 1930’s, but which only really started being understood and appreciated around the 1960’s. Since then it’s become a key tool, used in theoretical investigations in areas from condensed matter to quantum gravity. In this post, I thought I’d say a bit about the story behind the piece and give some bonus material that didn’t fit.

When I first decided to do the piece I reached out to Jules Lamers. We were briefly office-mates when I worked in France, where he was giving a short course on the Bethe ansatz and the methods that sprung from it. It turned out he had also been thinking about writing a piece on the subject, and we considered co-writing for a bit, but that didn’t work for Quanta. He helped me a huge amount with understanding the history of the subject and tracking down the right sources. If you’re a physicist who wants to learn about these things, I recommend his lecture notes. And if you’re a non-physicist who wants to know more, I hope he gets a chance to write a longer popular-audience piece on the topic!

If you clicked through to Jules’s lecture notes, you’d see the word “Bethe ansatz” doesn’t appear in the title. Instead, you’d see the phrase “quantum integrability”. In classical physics, an “integrable” system is one where you can calculate what will happen by doing an integral, essentially letting you “solve” any problem completely. Systems you can describe with the Bethe ansatz are solvable in a more complicated quantum sense, so they get called “quantum integrable”. There’s a whole research field that studies these quantum integrable systems.

My piece ended up rushing through the history of the field. After talking about Bethe’s original discovery, I jumped ahead to ice. The Bethe ansatz was first used to think about ice in the 1960’s, but the developments I mentioned leading up to it, where experimenters noticed extra variability and theorists explained it with the positions of hydrogen atoms, happened earlier, in the 1930’s. (Thanks to the commenter who pointed out that this was confusing!) Baxter gets a starring role in this section and had an important role in tying things together, but other people (Lieb and Sutherland) were involved earlier, showing that the Bethe ansatz indeed could be used with thin sheets of ice. This era had a bunch of other big names that I didn’t have space to talk about: C. N. Yang makes an appearance, and while Faddeev comes up later, I didn’t mention that he had a starring role in the 1970’s in understanding the connection to classical integrability and proposing a mathematical structure to understand what links all these different integrable theories together.

I vaguely gestured at black holes and quantum gravity, but didn’t have space for more than that. The connection there is to a topic you might have heard of before if you’ve read about string theory, called AdS/CFT, a connection between two kinds of world that are secretly the same: a toy model of gravity called Anti-de Sitter space (AdS) and a theory without gravity that looks the same at any scale (called a Conformal Field Theory, or CFT). It turns out that in the most prominent example of this, the theory without gravity is integrable! In fact, it’s a theory I spent a lot of time working with back in my research days, called N=4 super Yang-Mills. This theory is kind of like QCD, and in some sense it has integrability for similar reasons to those that Feynman hoped for and Korchemsky and Faddeev found. But it actually goes much farther, outside of the high-energy approximation where Korchemsky and Faddeev’s result works, and in principle seems to include everything you might want to know about the theory. Nowadays, people are using it to investigate the toy model of quantum gravity, hoping to get insights about quantum gravity in general.

One thing I didn’t get a chance to mention at all is the connection to quantum computing. People are trying to build a quantum computer with carefully-cooled atoms. It’s important to test whether the quantum computer functions well enough, or if the quantum states aren’t as perfect as they need to be. One way people have been testing this is with the Bethe ansatz: because it lets you calculate the behavior of special systems perfectly, you can set up your quantum computer to model a Bethe ansatz, and then check how close to the prediction your results are. You know that the theoretical result is complete, so any failure has to be due to an imperfection in your experiment.

I gave a quick teaser to a very active field, one that has fascinated a lot of prominent physicists and been applied in a wide variety of areas. I hope I’ve inspired you to learn more!

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.

Freelancing in [Country That Includes Greenland]

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(Why mention Greenland? It’s a movie reference.)

I figured I’d give an update on my personal life.

A year ago, I resigned from my position in France and moved back to Denmark. I had planned to spend a few months as a visiting researcher in my old haunts at the Niels Bohr Institute, courtesy of the spare funding of a generous friend. There turned out to be more funding than expected, and what was planned as just a few months was extended to almost a year.

I spent that year learning something new. It was still an amplitudes project, trying to make particle physics predictions more efficient. But this time I used Python. I looked into reinforcement learning and PyTorch, played with using a locally hosted Large Language Model to generate random code, and ended up getting good results from a classic genetic programming approach. Along the way I set up a SQL database, configured Docker containers, and puzzled out interactions with CUDA. I’ve got a paper in the works, I’ll post about it when it’s out.

All the while, on the side, I’ve been seeking out stories. I’ve not just been a writer, but a journalist, tracking down leads and interviewing experts. I had three pieces in Quanta Magazine and one in Ars Technica.

Based on that, I know I can make money doing science journalism. What I don’t know yet is whether I can make a living doing it. This year, I’ll figure that out. With the project at the Niels Bohr Institute over, I’ll have more time to seek out leads and pitch to more outlets. I’ll see whether I can turn a skill into a career.

So if you’re a scientist with a story to tell, if you’ve discovered something or accomplished something or just know something that the public doesn’t, and that you want to share: do reach out. There’s a lot that can be of interest, passion that can be shared.

At the same time, I don’t know yet whether I can make a living as a freelancer. Many people try and don’t succeed. So I’m keeping my CV polished and my eyes open. I have more experience now with Data Science tools, and I’ve got a few side projects cooking that should give me a bit more. I have a few directions in mind, but ultimately, I’m flexible. I like being part of a team, and with enthusiastic and competent colleagues I can get excited about pretty much anything. So if you’re hiring in Copenhagen, if you’re open to someone with ten years of STEM experience who’s just starting to see what industry has to offer, then let’s chat. Even if we’re not a good fit, I bet you’ve got a good story to tell.