Tag Archives: PublicPerception

Explain/Teach/Advocate

When Your Theory Is Already Dead

Occasionally, people try to give “even-handed” accounts of crackpot physics, like people who claim to have invented anti-gravity devices. These accounts don’t go so far as to say that the crackpots are right, and will freely point out plausible doubts about the experiments. But at the end of the day, they’ll conclude that we still don’t really know the answer, and perhaps the next experiment will go differently. More tests are needed.

For someone used to engineering, or to sciences without much theory behind them, this might sound pretty reasonable. Sure, any one test can be critiqued. But you can’t prove a negative: you can’t rule out a future test that might finally see the effect.

That’s all well and good…if you have no idea what you’re doing. But these people, just like anyone else who grapples with physics, aren’t just proposing experiments. They’re proposing theories: models of the world.

And once you’ve got a theory, you don’t just have to care about future experiments. You have to care about past experiments too. Some theories…are already dead.

The "You're already dead" scene from the anime North Star — Warning: this is a link to TVTropes, enter only if you have lots of time on your hands

To get a little more specific, let’s talk about antigravity proposals that use scalar fields.

Scalar fields seem to have some sort of mysticism attached to them in the antigravity crackpot community, but for physicists they’re just the simplest possible type of field, the most obvious thing anyone would have proposed once they were comfortable enough with the idea of fields in the first place. We know of one, the Higgs field, which gives rise to the Higgs boson.

We also know that if there are any more, they’re pretty subtle…and as a result, pretty useless.

We know this because of a wide variety of what are called “fifth-force experiments“, tests and astronomical observations looking for an undiscovered force that, like gravity, reaches out to long distances. Many of these experiments are quite general, the sort of thing that would pick up a wide variety of scalar fields. And so far, none of them have seen anything.

That “so far” doesn’t mean “wait and see”, though. Each time physicists run a fifth-force experiment, they establish a limit. They say, “a fifth force cannot be like this“. It can’t be this strong, it can’t operate on these scales, it can’t obey this model. Each experiment doesn’t just say “no fifth force yet”, it says “no fifth force of this kind, at all”.

When you write down a theory, if you’re not careful, you might find it has already been ruled out by one of these experiments. This happens to physicists all the time. Physicists want to use scalar fields to understand the expansion of the universe, they use them to think about dark matter. And frequently, a model one physicist proposed will be ruled out, not by new experiments, but by someone doing the math and realizing that the model is already contradicted by a pre-existing fifth-force experiment.

So can you prove a negative? Sort of.

If you never commit to a model, if you never propose an explanation, then you can never be disproven, you can always wait for the experiment of your dreams to come true. But if you have any model, any idea, any explanation at all, then your explanation will have implications. Those implications may kill your theory in a future experiment. Or, they may have already killed it.

To Measure Something or to Test It

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Black holes have been in the news a couple times recently.

On one end, there was the observation of an extremely large black hole in the early universe, when no black holes of the kind were expected to exist. My understanding is this is very much a “big if true” kind of claim, something that could have dramatic implications but may just be being misunderstood. At the moment, I’m not going to try to work out which one it is.

In between, you have a piece by me in Quanta Magazine a couple weeks ago, about tests of whether black holes deviate from general relativity. They don’t, by the way, according to the tests so far.

And on the other end, you have the coverage last week of a “confirmation” (or even “proof”) of the black hole area law.

The black hole area law states that the total area of the event horizons of all black holes will always increase. It’s also known as the second law of black hole thermodynamics, paralleling the second law of thermodynamics that entropy always increases. Hawking proved this as a theorem in 1971, assuming that general relativity holds true.

(That leaves out quantum effects, which indeed can make black holes shrink, as Hawking himself famously later argued.)

The black hole area law is supposed to hold even when two black holes collide and merge. While the combination may lose energy (leading to gravitational waves that carry energy to us), it will still have greater area, in the end, than the sum of the black holes that combined to make it.

Ok, so that’s the area law. What’s this paper that’s supposed to “finally prove” it?

The LIGO, Virgo, and KAGRA collaborations recently published a paper based on gravitational waves from one particularly clear collision of black holes, which they measured back in January. They compare their measurements to predictions from general relativity, and checked two things: whether the measurements agreed with predictions based on the Kerr metric (how space-time around a rotating black hole is supposed to behave), and whether they obeyed the area law.

The first check isn’t so different in purpose from the work I wrote about in Quanta Magazine, just using different methods. In both studies, physicists are looking for deviations from the laws of general relativity, triggered by the highly curved environments around black holes. These deviations could show up in one way or another in any black hole collision, so while you would ideally look for them by scanning over many collisions (as the paper I reported on did), you could do a meaningful test even with just one collision. That kind of a check may not be very strenuous (if general relativity is wrong, it’s likely by a very small amount), but it’s still an opportunity, diligently sought, to be proven wrong.

The second check is the one that got the headlines. It also got first billing in the paper title, and a decent amount of verbiage in the paper itself. And if you think about it for more than five minutes, it doesn’t make a ton of sense as presented.

Suppose the black hole area law is wrong, and sometimes black holes lose area when they collide. Even if this happened sometimes, you wouldn’t expect it to happen every time. It’s not like anyone is pondering a reverse black hole area law, where black holes only shrink!

Because of that, I think it’s better to say that LIGO measured the black hole area law for this collision, while they tested whether black holes obey the Kerr metric. In one case, they’re just observing what happened in this one situation. In the other, they can try to draw implications for other collisions.

That doesn’t mean their work wasn’t impressive, but it was impressive for reasons that don’t seem to be getting emphasized. It’s impressive because, prior to this paper, they had not managed to measure the areas of colliding black holes well enough to confirm that they obeyed the area law! The previous collisions looked like they obeyed the law, but when you factor in the experimental error they couldn’t say it with confidence. The current measurement is better, and can. So the new measurement is interesting not because it confirms a fundamental law of the universe or anything like that…it’s interesting because previous measurements were so bad, that they couldn’t even confirm this kind of fundamental law!

That, incidentally, feels like a “missing mood” in pop science. Some things are impressive not because of their amazing scale or awesome implications, but because they are unexpectedly, unintuitively, really really hard to do. These measurements shouldn’t be thought of, or billed, as tests of nature’s fundamental laws. Instead they’re interesting because they highlight what we’re capable of, and what we still need to accomplish.

The Rocks in the Ground Era of Fundamental Physics

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It’s no secret that the early twentieth century was a great time to make progress in fundamental physics. On one level, it was an era when huge swaths of our understanding of the world were being rewritten, with relativity and quantum mechanics just being explored. It was a time when a bright student could guide the emergence of whole new branches of scholarship, and recently discovered physical laws could influence world events on a massive scale.

Put that way, it sounds like it was a time of low-hanging fruit, the early days of a field when great strides can be made before the easy problems are all solved and only the hard ones are left. And that’s part of it, certainly: the fields sprung from that era have gotten more complex and challenging over time, requiring more specialized knowledge to make any kind of progress. But there is also a physical reason why physicists had such an enormous impact back then.

The early twentieth century was the last time that you could dig up a rock out of the ground, do some chemistry, and end up with a discovery about the fundamental laws of physics.

When scientists like Curie and Becquerel were working with uranium, they didn’t yet understand the nature of atoms. The distinctions between elements were described in qualitative terms, but only just beginning to be physically understood. That meant that a weird object in nature, “a weird rock”, could do quite a lot of interesting things.

And once you find a rock that does something physically unexpected, you can scale up. From the chemistry experiments of a single scientist’s lab, countries can build industrial processes to multiply the effect. Nuclear power and the bomb were such radical changes because they represented the end effect of understanding the nature of atoms, and atoms are something people could build factories to manipulate.

Scientists went on to push that understanding further. They wanted to know what the smallest pieces of matter were composed of, to learn the laws behind the most fundamental laws they knew. And with relativity and quantum mechanics, they could begin to do so systematically.

US particle physics has a nice bit of branding. They talk about three frontiers: the Energy Frontier, the Intensity Frontier, and the Cosmic Frontier.

Some things we can’t yet test in physics are gated by energy. If we haven’t discovered a particle, it may be because it’s unstable, decaying quickly into lighter particles so we can’t observe it in everyday life. If these particles interact appreciably with particles of everyday matter like protons and electrons, then we can try to make them in particle colliders. These end up creating pretty much everything up to a certain mass, due to a combination of the tendency in quantum mechanics for everything that can happen to happen, and relativity’s $E=mc^2$ . In the mid-20th century these particle colliders were serious pieces of machinery, but still small enough to make industrial: now, there are so-called medical accelerators in many hospitals based on their designs. But current particle accelerators are a different beast, massive facilities built by international collaborations. This is the Energy Frontier.

Some things in physics are gated by how rare they are. Some particles interact only very faintly with other particles, so to detect them, physicists have to scan a huge chunk of matter, a giant tank of argon or a kilometer of antarctic ice, looking for deviations from the norm. Over time, these experiments have gotten bigger, looking for more and more subtle effects. A few weird ones still fit on tabletops, but only because they have the tools to measure incredibly small variations. Most are gigantic. This is the Intensity Frontier.

Finally, the Cosmic Frontier looks for the unknown behind both kinds of gates, using the wider universe to look at events with extremely high energy or size.

Pushing these frontiers has meant cleaning up our understanding of the fundamental laws of physics up to these frontiers. It means that whatever is still hiding, it either requires huge amounts of energy to produce, or is an extremely rare, subtle effect.

That means that you shouldn’t expect another nuclear bomb out of fundamental physics. Physics experiments are already working on vast scales, to the extent that a secret government project would have to be smaller than publicly known experiments, in physical size, energy use, and budget. And you shouldn’t expect another nuclear power plant, either: we’ve long passed the kinds of things you could devise a clever industrial process to take advantage of at scale.

Instead, new fundamental physics will only be directly useful once we’re the kind of civilization that operates on a much greater scale than we do today. That means larger than the solar system: there wouldn’t be much advantage, at this point, of putting a particle physics experiment on the edge of the Sun. It means the kind of civilization that tosses galaxies around.

It means that right now, you won’t see militaries or companies pushing the frontiers of fundamental physics, unlike the way they might have wanted to at the dawn of the twentieth century. By the time fundamental physics is useful in that way, all of these actors will likely be radically different: companies, governments, and in all likelihood human beings themselves. Instead, supporting fundamental physics right now is an act of philanthropy, maintaining a practice because it maintains good habits of thought and produces powerful ideas, the same reasons organizations support mathematics or poetry. That’s not nothing, and fundamental physics is still often affordable as philanthropy goes. But it’s not changing the world, not the way physicists did in the early twentieth century.

Two Types of Scientific Fraud: for a Fee and for Power

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A paper about scientific fraud has been making the rounds in social media lately. The authors gather evidence of large-scale networks of fraudsters across multiple fields, from teams of editors that fast-track fraudulent research to businesses that take over journals, sell spots for articles, and then move on to a new target when the journal is de-indexed. I’m not an expert in this kind of statistical sleuthing, but the work looks impressively thorough.

Still, I think the authors overplay their results a bit. They describe themselves as revealing something many scientists underestimate. They point to what they label as misconceptions: that scientific fraud is usually perpetrated alone by individual unethical scientists, or that it is almost entirely a problem of the developing world, and present their work as disproving those misconceptions. Listen to them, and you might get the feeling that science is rife with corruption, that no result, or scientist, can be trusted.

As far as I can tell, though, those “misconceptions” they identify are true. Someone who believes that scientific fraud is perpetrated by loners is probably right, as is someone who believes it largely takes place outside of the first world.

As is often the case, the problem is words.

“Scientific Fraud” is a single term for two different things. The two both involve bad actors twisting scientific activity. But in everything else — their incentives, their geography, their scale, and their consequences — they are dramatically different.

One of the types of scientific fraud is largely about power.

In references 84-89 of the paper, the authors give examples of large-scale scientific fraud in Europe and the US. All (except one, which I’ll mention later) are about the career of a single researcher. Each of these people systematically bent the truth, whether with dodgy statistics, doctored images, or inflating citation counts. Some seemed motivated to promote a particular scientific argument, cutting corners to push a particular conclusion through. Others were purer cases of self-promotion. These people often put pressure on students, postdocs, and other junior researchers in their orbits, which increases the scale of their impact. In some cases, their work rippled out to convince other researchers, prolonging bad ideas and strangling good ones. These were people with power, who leveraged that power to increase their power.

There also don’t appear to be that many of them. These people are loners in a meaningful sense, cores of fraud working on their own behalf. They don’t form networks with each other, for the most part: because they work towards their own aggrandizement, they have no reason to trust anyone else doing the same. I have yet to see evidence that the number of these people is increasing. They exist, they’re a problem, they’re important to watch out for. But they’re not a crisis, and they shouldn’t shift your default expectations of science.

The other, quite different, type of scientific fraud is fraud for a fee.

The cases this paper investigates seem to fall into this category. They are businesses, offering the raw material of academic credit (papers, co-authorship, citations, publication) for cash. They’re paper mills, of various sorts. These are, at least from an academic perspective, large organizations, with hundreds or thousands of customers and tens of suborned editors or scientists farming out their credibility. As the authors of this paper argue, fraudsters of this type are churning out more and more papers, potentially now fueled by AI, adding up to a still small, but non-negligible, proportion of scientific papers in total.

Compared to the first type of fraud, though, buying credit in this way doesn’t give very much power. As the paper describes, many of the papers churned out by paper mills don’t even go into relevant journals: for example, they mention “an article about roasting hazelnuts in a journal about HIV/AIDS care”. An article like that isn’t going to mislead the hazelnut roasting community, or the HIV/AIDS community. Indeed, that would be counter to its purpose. The paper isn’t intended to be read at all, and ideally gets ignored: it’s just supposed to inflate a number.

These numbers are most relevant in the developing world, and when push comes to shove, almost all of the buyers of these services identified by the authors of this paper come from there. In many developing countries, a combination of low trust and advice from economists leads to explicit point systems, where academics are paid or hired explicitly based on criteria like where and how often they publish or how they are cited. The more a country can trust people to vouch for each other without corruption, the less these kinds of incentives have purchase. Outside of the developing world, involvement in paper mills and the like generally seems to involve a much smaller number of people, and typically as sellers, not buyers: selling first-world credibility in exchange for fees from many developing-world applicants.

(The one reference I mentioned above is an interesting example of this: a system built out of points and low trust to recruit doctors from the developing world to the US, gamed by a small number of co-authorship brokers.)

This kind of fraud doesn’t influence science directly. Its perpetrators aren’t trying to get noticed, but to keep up a cushy scam. You don’t hear their conclusions in the press, other scientists don’t see their work. Instead, they siphon off resources: cannibalizing journals, flooding editors with mass-produced crap, and filling positions and slurping up science budgets in the countries that can least afford them. As they publish more and more, they shouldn’t affect your expectations of the credibility of science: any science you hear about will be either genuine, or fraud from the other category. But they do make the science you hear about harder and harder to do.

(The authors point out one exception: what about AI? If a company trains a large language model on the current internet, will its context windows be long enough to tell that that supposedly legitimate paper about hazelnuts is in an HIV/AIDS journal? If something gets said often enough, copied again and again in papers sold by a mill, will an AI trained on all these papers be convinced? Presumably, someone is being paid good money to figure out how to filter AI-generated slop from training data: can they filter paper mill fraud as well?)

It’s a shame that we have one term, scientific fraud, to deal with these two very different things. But it’s important to keep in mind that they are different. Fraud for power and fraud for money can have very different profiles, and offer very different risks. If you don’t trust a scientific result, it’s worth understanding what might be at play.

Technology as Evidence

Newsworthiness Bias

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I had a chat about journalism recently, and I had a realization about just how weird science journalism, in particular, is.

Journalists aren’t supposed to be cheerleaders. Journalism and PR have very different goals (which is why I keep those sides of my work separate). A journalist is supposed to be uncompromising, to write the truth even if it paints the source in a bad light.

Norms are built around this. Serious journalistic outlets usually don’t let sources see pieces before they’re published. The source doesn’t have the final say in how they’re portrayed: the journalist reserves the right to surprise them if justified. Investigative journalists can be superstars, digging up damning secrets about the powerful.

When a journalist starts a project, the piece might turn out positive, or negative. A politician might be the best path forward, or a disingenuous grifter. A business might be a great investment opportunity, or a total scam. A popular piece of art might be a triumph, or a disappointment.

And a scientific result?

It might be a fraud, of course. Scientific fraud does exist, and is a real problem. But it’s not common, really. Pick a random scientific paper, filter by papers you might consider reporting on in the first place, and you’re very unlikely to find a fraudulent result. Science journalists occasionally report on spectacularly audacious scientific frauds, or frauds in papers that have already made the headlines. But you don’t expect fraud in the average paper you cover.

It might be scientifically misguided: flawed statistics, a gap in a proof, a misuse of concepts. Journalists aren’t usually equipped to ferret out these issues, though. Instead, this is handled in principle by peer review, and in practice by the scientific community outside of the peer review process.

Instead, for a scientific result, the most common negative judgement isn’t that it’s a lie, or a mistake. It’s that it’s boring.

And certainly, a good science journalist can judge a paper as boring. But there is a key difference between doing that, and judging a politician as crooked or a popular work of art as mediocre. You can write an article about the lying candidate for governor, or the letdown Tarantino movie. But if a scientific result is boring, and nobody else has covered it…then it isn’t newsworthy.

In science, people don’t usually publish their failures, their negative results, their ho-hum obvious conclusions. That fills the literature with only the successes, a phenomenon called publication bias. It also means, though, that scientists try to make their results sound more successful, more important and interesting, than they actually are. Some of the folks fighting the replication crisis have coined a term for this: they call it importance hacking.

The same incentives apply to journalists, especially freelancers. Starting out, it was far from clear that I could make enough to live on. I felt like I had to make every lead count, to find a newsworthy angle on every story idea I could find, because who knew when I would find another one? Over time, I learned to balance that pull better. Now that I’m making most of my income from consulting instead, the pressure has eased almost entirely: there are things I’m tempted to importance-hack for the sake of friends, but nothing that I need to importance-hack to stay in the black.

Doing journalism on the side may be good for me personally at the moment, but it’s not really a model. Much like we need career scientists, even if their work is sometimes boring, we need career journalists, even if they’re sometimes pressured to overhype.

So if we don’t want to incentivize science journalists to be science cheerleaders, what can we do instead?

In science, one way to address publication bias is with pre-registered studies. A scientist sets out what they plan to test, and a journal agrees to publish the result, no matter what it is. You could imagine something like this for science journalism. I once proposed a recurring column where every month I would cover a random paper from arXiv.org, explaining what it meant to accomplish. I get why the idea was turned down, but I still think about it.

In journalism, the arts offer the closest parallel with a different approach. There are many negative reviews of books, movies, and music, and most of them merely accuse the art of being boring, not evil. These exist because they focus on popular works that people pay attention to anyway, so that any negative coverage has someone to convince. You could imagine applying this model to science, though it could be a bit silly. I’m envisioning a journalist who writes an article every time Witten publishes, rating some papers impressive and others disappointing, the same way a music journalist might cover every Taylor Swift album.

Neither of these models are really satisfactory. You could imagine an even more adversarial model, where journalists run around accusing random scientists of wasting the government’s money, but that seems dramatically worse.

So I’m not sure. Science is weird, and hard to accurately value: if we knew how much something mattered already, it would be engineering, not science. Journalism is weird: it’s public-facing research, where the public facing is the whole point. Their combination? Even weirder.

Microdosing Vibe Physics

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Have you heard of “vibe physics”?

The phrase “vibe coding” came first. People have been using large language models like ChatGPT to write computer code (and not the way I did last year). They chat with the model, describing what they want to do and asking the model to code it up. You can guess the arguments around this, from people who are convinced AI is already better than a human programmer to people sure the code will be riddled with errors and vulnerabilities.

Now, there are people claiming not only to do vibe coding, but vibe physics: doing theoretical physics by chatting with an AI.

I think we can all agree that’s a lot less plausible. Some of the people who do vibe coding actually know how to code, but I haven’t seen anyone claiming to do vibe physics who actually understands physics. They’re tech entrepreneurs in the most prominent cases, random people on the internet otherwise. And while a lot of computer code is a minor tweak on something someone has already done, theoretical physics doesn’t work that way: if someone has already come up with your idea, you’re an educator, not a physicist.

Still, I think there is something to keep in mind about the idea of “vibe physics”, related to where physics comes from.

Here’s a question to start with: go back a bit before the current chat-bot boom. There were a ton of other computational and mathematical tools. Theorem-proving software could encode almost arbitrary mathematical statements in computer code and guarantee their accuracy. Statistical concepts like Bayes’ rule described how to reason from evidence to conclusions, not flawlessly but as well as anyone reliably can. We had computer simulations for a wealth of physical phenomena, and approximation schemes for many others.

With all those tools, why did we still have human physicists?

That is, go back before ChatGPT, before large language models. Why not just code up a program that starts with the evidence and checks which mathematical model fits it best?

In principle, I think you really could have done that. But you could never run that program. It would take too long.

Doing science 100% correctly and reliably is agonizingly slow, and prohibitively expensive. You cannot check every possible model, nor can you check those models against all the available data. You must simplify your problem, somehow, even if it makes your work less reliable, and sometimes incorrect.

And for most of history, humans have provided that simplification.

A physicist isn’t going to consider every possible model. They’re going to consider models that are similar to models they studied, or similar to models others propose. They aren’t going to consider all the evidence. They’ll look at some of the evidence, the evidence other physicists are talking about and puzzled by. They won’t simulate the consequences of their hypotheses in exhaustive detail. Instead, they’ll guess, based on their own experience, a calculation that captures what they expect to be relevant.

Human physicists provided the unreliable part of physics, the heuristics. The “vibe physics”, if you will.

AI is also unreliable, also heuristic. But humans still do this better than AI.

Part of the difference is specificity. These AIs are trained on all of human language, and then perhaps fine-tuned on a general class of problems. A human expert has spent their life fine-tuning on one specific type of problem, and their intuitions, their heuristics, their lazy associations and vibes, all will be especially well-suited to problems of that type.

Another part of the difference, though, is scale.

When you talk to ChatGPT, it follows its vibes into paragraphs of text. If you turn on reasoning features, you make it check its work in the background, but it still is generating words upon words inside, evaluating those words, then generating more.

I suspect, for a physicist, the “control loop” is much tighter. Many potential ideas get ruled out a few words in. Many aren’t even expressed in words at all, just concepts. A human physicist is ultimately driven by vibes, but they check and verify those vibes, based on their experience, at a much higher frequency than any current AI system can achieve.

(I know almost nothing about neuroscience. I’m just basing this on what it can feel like, to grope through a sentence and have it assemble itself as it goes into something correct, rather than having to go back and edit it.)

As companies get access to bigger datacenters, I suspect they’ll try to make this loop tighter, to get AI to do something closer to what (I suspect, it appears) humans do. And then maybe AI will be able to do vibe physics.

Even then, though, you should not do vibe physics with the AI.

If you look at the way people describe doing vibe physics, they’re not using the AI for the vibes. They’re providing the vibes, and the AI is supposed to check things.

And that, I can confidently say, is completely ass-backwards. The AI is a vibe machine, it is great at vibes. Substituting your vibes will just make it worse. On the other hand, the AI is awful at checking things. It can find published papers sometimes, which can help you check something. But it is not set up to do the math, at least not unless the math can be phrased as a simple Python script or an IMO problem. In order to do anything like that, it has to call another type of software to verify. And you could have just used that software.

Theoretical physics is still not something everyone can do. Proposing a crackpot theory based on a few papers you found on Google and a couple YouTube videos may make you feel less confident than proposing a crackpot theory based on praise from ChatGPT and a list of papers it claims have something to do with your idea, which makes it more tempting. But it’s still proposing a crackpot theory. If you want to get involved, there’s still no substitute for actually learning how physics works.

Hype, Incentives, and Culture

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To be clear, hype isn’t just lying.

We have a word for when someone lies to convince someone else to pay them, and that word is fraud. Most of what we call hype doesn’t reach that bar.

Instead, hype lives in a gray zone of affect and metaphor.

Some hype is pure affect. It’s about the subjective details, it’s about mood. “This is amazing” isn’t a lie, or at least, isn’t a lie you can check. They might really be amazed!

Some hype relies on metaphor. A metaphor can’t really be a lie, because a metaphor is always incomplete. But a metaphor can certainly be misleading. It can associate something minor with something important, or add emotional valence that isn’t really warranted.

Hype lies in a gray zone…and precisely because it lives in a gray zone, not everything that looks like hype is intended to be type.

We think of hype as a consequence of incentives. Scientists hype their work to grant committees to get grants, and hype it more to the public for prestige. Companies hype their products to sell them, and their business plans to draw in investors.

But what looks like hype can also be language, and culture.

To many people in the rest of the world, the way Americans talk about almost everything is hype. Everything is bigger and nicer and cooler. This isn’t because Americans are under some sort of weird extra career incentives, though. It’s just how they expect to talk, how they learned to talk, how everyone around them normally talks.

Similarly, people in different industries are used to talking differently. Depending on what work you do, you interpret different metaphors in different ways. What might seem like an enthusiastic endorsement in one industry might be dismissive in another.

In the end, it takes two to communicate: a speaker, and an audience. Speakers want to get their audience excited, and hopefully, if they don’t want to hype, to understand something of the truth. That means understanding how the audience communicates enthusiasm, and how it differs from the speaker. It means understanding language, and culture.

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.

4 gravitons

Stories about physics from someone who's been there