Tag Archives: physics

Whatever Happened to the Nonsense Merchants?

I was recently reminded that Michio Kaku exists.

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

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

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

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

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

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

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

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

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

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

Talking and Teaching

Someone recently shared with me an article written by David Mermin in 1992 about physics talks. Some aspects are dated (our slides are no longer sheets of plastic, and I don’t think anyone writing an article like that today would feel the need to put it in the mouth of a fictional professor (which is a shame honestly)), but most of it still holds true. I particularly recognized the self-doubt of being a young physicist sitting in a talk and thinking “I’m supposed to enjoy this?”

Mermin’s basic point is to keep things as light as possible. You want to convey motivation more than content, and background more than your own contributions. Slides should be sparse, both because people won’t be able to see everything but also because people can get frustrated “reading ahead” of what you say.

Mermin’s suggestion that people read from a prepared text was probably good advice for him, but maybe not for others. It can be good if you can write like he does, but I don’t think most people’s writing is that much better than what they say in talks (you can judge this by reading peoples’ papers!) Some are much clearer speaking impromptu. I agree with him that in practice people end up just reading from their slides, which indeed is bad, but reading from a normal physics paper isn’t any better.

I also don’t completely agree with him about the value of speech over text. Yes, putting text on your slides means people can read ahead (unless you hide some of the text, which is easier to do these days than in the days of overhead transparencies). But just saying things means that if someone’s attention lapses for just a moment, they’ll be lost. Unless you repeat yourself a lot (good practice in any case), you should avoid just saying anything you need your audience to remember, and make sure they can read it somewhere if they need it as well.

That said, “if they need it” is doing a lot of work here, and this is where I agree again with Mermin. Fundamentally, you don’t need to convey everything you think you do. (I don’t usually need to convey everything I think I do!) It’s a lesson I’ve been learning this year from pedagogy courses, a message they try to instill in everyone who teaches at the university. If you want to really convey something well, then you just can’t convey that much. You need to focus, pick a few things and try to get them across, and structure the rest of what you say to reinforce those things. When teaching, or when speaking, less is more.

At the Bohr Centennial

One hundred years ago, Niels Bohr received his Nobel prize. One hundred and one years ago, the Niels Bohr Institute opened its doors (it would have been one hundred and two, but pandemics are inconvenient things).

This year, also partly delayed by a pandemic, the Niels Bohr Institute is celebrating.

Using the fanciest hall the university has.

We’ve had a three-day conference, packed with Nobel prizewinners, people who don’t feel out of place among Nobel prizewinners, and for one morning’s ceremony the crown prince of Denmark. There were last-minute cancellations but also last-minute additions, including a moving speech by two Ukrainian PhD students. I don’t talk politics on this blog, so I won’t say much more about it (and you shouldn’t in the comments either, there are better venues), but I will say that was the only time I’ve seen a standing ovation at a scientific conference.

The other talks ran from reminiscences (Glashow struggled to get to the stage, but his talk was witty, even quoting Peter Woit apparently to try to rile David Gross in the front row (next to the Ukranian PhD students who must have found it very awkward)) to classic colloquium style talks (really interesting crisply described puzzles from astrochemistry to biophysics) to a few more “conference-ey” talks (t’Hooft, unfortunately). It’s been fun, but also exhausting, and as such that’s all I’m writing this week.

Serial Killers and Grad School Horror Stories

It’s time for my yearly Halloween post. My regular readers know what to expect: a horror trope and a physics topic, linked by a tortured analogy. And this year, the pun is definitely intended.

Horror movies have a fascination with serial killers. Over the years, they’ve explored every possible concept: from gritty realism to the supernatural, crude weapons to sophisticated traps, motivations straightforward to mysterious, and even killers who are puppets.

Yes I know Billy is not actually the killer in the Saw films

One common theme of all fictional serial killers is power. Serial killers are scary because they have almost all the power in a situation, turned to alien and unpredictable goals. The protagonists of a horror film are the underdogs, never knowing whether the killer will pull out some new ability or plan that makes everything they try irrelevant. Even if they get the opportunity to negotiate, the power imbalance means that they can’t count on getting what they need: anything the killer agrees will be twisted to serve their own ends.

Academics tell their own kind of horror stories. Earlier this month, the historian Brett Deveraux had a blog post about graduate school, describing what students go through to get a PhD. As he admits, parts of his story only apply to the humanities. STEM departments have more money, and pay their students a bit better. It’s not a lot better (I was making around $20,000 a year at Stony Brook), but it’s enough that I’ve never heard of a student taking out a loan to make ends meet. (At most, people took on tutoring jobs for a bit of extra cash.) We don’t need to learn new languages, and our degrees take a bit less time: six or seven years for an experimental physicist, and often five for a theoretical physicist. Finally, the work can be a lot less lonely, especially for those who work in a lab.

Still, there is a core in common, and that core once again is power. Universities have power, of course: and when you’re not a paying customer but an employee with your career on the line, that power can be quite scary. But the person with the most power over a PhD student is their advisor. Deveraux talks compellingly about the difference that power can make: how an advisor who is cruel, or indifferent, or just clueless, can make or break not just your career but your psychological well-being. The lucky students, like Deveraux and me, find supportive mentors who help us survive and move forward. The unlucky students leave with scars, even if those scars aren’t jigsaw-shaped.

Neither Deveraux or I have experience with PhD programs in Europe, which are quite different in structure from those in the US. But the power imbalance is still there, and still deadly, and so despite the different structure, I’ve seen students here break down, scarred in the same way.

Deveraux frames his post as advice for those who want to go to grad school, and his first piece of advice is “Have you tried wanting something else?” I try to echo that when I advise students. I don’t always succeed: there’s something exciting about a young person interested in the same topics we’re interested in, willing to try to make a life of it. But it is important to know what you’re getting into, and to know there’s a big world out there of other options. If, after all that, you decide to stick through it, just remember: power matters. If you give someone power over you, try to be as sure as you can that it won’t turn into a horror story.

Sandbox Collaboration

In science, every project is different. Sometimes, my collaborators and I have a clear enough goal, and a clear enough way to get there. There are always surprises along the way, of course, but nonetheless we keep a certain amount of structure. That can mean dividing tasks (“you find the basis, I’ll find the constraints”), or it can mean everyone doing the same work in parallel, like a group of students helping each other with homework.

Recently, I’ve experienced a different kind of collaboration. The goals are less clear, and the methods are more…playful.

Oh, are you building a sandcastle? Or a polylogarithm?

A big task improves with collaboration: you can divide it up. A delicate task improves with collaboration: you can check each other’s work. An unclear task also improves with collaboration: you can explore more ground.

Picture a bunch of children playing in a sandbox. The children start out sitting by themselves, each digging in the sand. Some are building castles, others dig moats, or search for buried treasure, or dinosaur bones. As the children play, their games link up: the moat protects the castle, the knights leave for treasure, the dinosaur awakens and attacks. The stories feed back on one another, and the game grows.

The project I’m working on now is a bit like that sandbox. Each of us has our own ideas about what we’d like to build, and each experiments with them. We see what works and what doesn’t, which castles hold and which fall over. We keep an eye on what each other are doing, and adjust: if that castle is close to done, maybe a moat would improve the view. Piece by piece, the unclear task becomes clearer. Our individual goals draw us in different directions, but what we discover in the end brings us back together, richer for our distant discoveries.

Working this way requires a lot of communication! In the past, I was mystified when I saw other physicists spend hours talking at a blackboard. I thought that must be a waste of time: surely they’d get more done if they sat at their desks and worked things out, rather than having to talk through every step. Now I realize they were likely part of a different kind of collaboration: not dividing tasks or working in parallel on a clear calculation, but exploring different approaches. In these collaborations, those long chats are a kind of calibration: by explaining what you’re trying to do, you see whether it makes sense to your collaborators. You can drop the parts that don’t make sense and build in some of your collaborators’ ideas. In the end you begin to converge, to something that everyone can endorse. Your sandcastles meet up, your stories become one story. When everything looks good, you’re ready to call over your mom (or in this case, the arXiv) and show it off.

Newtonmas in Uncertain Times

Three hundred and eighty-two years ago today (depending on which calendars you use), Isaac Newton was born. For a scientist, that’s a pretty good reason to celebrate.

Reason’s Greetings Everyone!

Last month, our local nest of science historians at the Niels Bohr Archive hosted a Zoom talk by Jed Z. Buchwald, a Newton scholar at Caltech. Buchwald had a story to tell about experimental uncertainty, one where Newton had an important role.

If you’ve ever had a lab course in school, you know experiments never quite go like they’re supposed to. Set a room of twenty students to find Newton’s constant, and you’ll get forty different answers. Whether you’re reading a ruler or clicking a stopwatch, you can never measure anything with perfect accuracy. Each time you measure, you introduce a little random error.

Textbooks worth of statistical know-how has cropped up over the centuries to compensate for this error and get closer to the truth. The simplest trick though, is just to average over multiple experiments. It’s so obvious a choice, taking a thousand little errors and smoothing them out, that you might think people have been averaging in this way through history.

They haven’t though. As far as Buchwald had found, the first person to average experiments in this way was Isaac Newton.

What did people do before Newton?

Well, what might you do, if you didn’t have a concept of random error? You can still see that each time you measure you get a different result. But you would blame yourself: if you were more careful with the ruler, quicker with the stopwatch, you’d get it right. So you practice, you do the experiment many times, just as you would if you were averaging. But instead of averaging, you just take one result, the one you feel you did carefully enough to count.

Before Newton, this was almost always what scientists did. If you were an astronomer mapping the stars, the positions you published would be the last of a long line of measurements, not an average of the rest. Some other tricks existed. Tycho Brahe for example folded numbers together pair by pair, averaging the first two and then averaging that average with the next one, getting a final result weighted to the later measurements. But, according to Buchwald, Newton was the first to just add everything together.

Even Newton didn’t yet know why this worked. It would take later research, theorems of statistics, to establish the full justification. It seems Newton and his later contemporaries had a vague physics analogy in mind, finding a sort of “center of mass” of different experiments. This doesn’t make much sense – but it worked, well enough for physics as we know it to begin.

So this Newtonmas, let’s thank the scientists of the past. Working piece by piece, concept by concept, they gave use the tools to navigate our uncertain times.

Halloween Post: Superstimuli for Physicists

For Halloween, this blog has a tradition of covering “the spooky side” of physics. This year, I’m bringing in a concept from biology to ask a spooky physics “what if?”

In the 1950’s, biologists discovered that birds were susceptible to a worryingly effective trick. By giving them artificial eggs larger and brighter than their actual babies, they found that the birds focused on the new eggs to the exclusion of their own. They couldn’t help trying to hatch the fake eggs, even if they were so large that they would fall off when they tried to sit on them. The effect, since observed in other species, became known as a supernormal stimulus, or superstimulus.

Can this happen to humans? Some think so. They worry about junk food we crave more than actual nutrients, or social media that eclipses our real relationships. Naturally, this idea inspires horror writers, who write about haunting music you can’t stop listening to, or holes in a wall that “fit” so well you’re compelled to climb in.

(And yes, it shows up in porn as well.)

But this is a physics blog, not a biology blog. What kind of superstimulus would work on physicists?

Abstruse goose knows what’s up

Well for one, this sounds a lot like some criticisms of string theory. Instead of a theory that just unifies some forces, why not unify all the forces? Instead of just learning some advanced mathematics, why not learn more, and more? And if you can’t be falsified by any experiment, well, all that would do is spoil the fun, right?

But it’s not just string theory you could apply this logic to. Astrophysicists study not just one world but many. Cosmologists study the birth and death of the entire universe. Particle physicists study the fundamental pieces that make up the fundamental pieces. We all partake in the euphoria of problem-solving, a perpetual rush where each solution leads to yet another question.

Do I actually think that string theory is a superstimulus, that astrophysics or particle physics is a superstimulus? In a word, no. Much as it might look that way from the news coverage, most physicists don’t work on these big, flashy questions. Far from being lured in by irresistible super-scale problems, most physicists work with tabletop experiments and useful materials. For those of us who do look up at the sky or down at the roots of the world, we do it not just because it’s compelling but because it has a good track record: physics wouldn’t exist if Newton hadn’t cared about the orbits of the planets. We study extremes because they advance our understanding of everything else, because they give us steam engines and transistors and change everyone’s lives for the better.

Then again, if I had fallen victim to a superstimulus, I’d say that anyway, right?

*cue spooky music*

Formal Theory and Simulated Experiment

There are two kinds of theoretical physicists. Some, called phenomenologists, make predictions about the real world. Others, the so-called “formal theorists”, don’t. They work with the same kinds of theories as the phenomenologists, quantum field theories of the sort that have been so successful in understanding the subatomic world. But the specific theories they use are different: usually, toy models that aren’t intended to describe reality.

Most people get this is valuable. It’s useful to study toy models, because they help us tackle the real world. But they stumble on another point. Sure, they say, you can study toy models…but then you should call yourself a mathematician, not a physicist.

I’m a “formal theorist”. And I’m very much not a mathematician, I’m definitely a physicist. Let me explain why, with an analogy.

As an undergrad, I spent some time working in a particle physics lab. The lab had developed a new particle detector chip, designed for a future experiment: the International Linear Collider. It was my job to test this chip.

Naturally, I couldn’t test the chip by flinging particles at it. For one, the collider it was designed for hadn’t been built yet! Instead, I had to use simulated input: send in electrical signals that mimicked the expected particles, and see what happens. In effect, I was using a kind of toy model, as a way to understand better how the chip worked.

I hope you agree that this kind of work counts as physics. It isn’t “just engineering” to feed simulated input into a chip. Not when the whole point of that chip is to go into a physics experiment. This kind of work is a large chunk of what an experimental physicist does.

As a formal theorist, my work with toy models is an important part of what a theoretical physicist does. I test out the “devices” of theoretical physics, the quantum-field-theoretic machinery that we use to investigate the world. Without that kind of careful testing on toy models, we’d have fewer tools to work with when we want to understand reality.

Ok, but you might object: an experimental physicist does eventually build the real experiment. They don’t just spend their career on simulated input. If someone only works on formal theory, shouldn’t that at least make them a mathematician, not a physicist?

Here’s the thing, though: after those summers in that lab, I didn’t end up as an experimental physicist. After working on that chip, I didn’t go on to perfect it for the International Linear Collider. But it would be rather bizarre if that, retroactively, made my work in that time “engineering” and not “physics”.

Oh, I should also mention that the International Linear Collider might not ever be built. So, there’s that.

Formal theory is part of physics because it cares directly about the goals of physics: understanding the real world. It is just one step towards that goal, it doesn’t address the real world alone. But neither do the people testing out chips for future colliders. Formal theory isn’t always useful, similarly, planned experiments don’t always get built. That doesn’t mean it’s not physics.

Understanding Is Translation

Kernighan’s Law states, “Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.” People sometimes make a similar argument about philosophy of mind: “The attempt of the mind to analyze itself [is] an effort analogous to one who would lift himself by his own bootstraps.”

Both points operate on a shared kind of logic. They picture understanding something as modeling it in your mind, with every detail clear. If you’ve already used all your mind’s power to design code, you won’t be able to model when it goes wrong. And modeling your own mind is clearly nonsense, you would need an even larger mind to hold the model.

The trouble is, this isn’t really how understanding works. To understand something, you don’t need to hold a perfect model of it in your head. Instead, you translate it into something you can more easily work with. Like explanations, these translations can be different for different people.

To understand something, I need to know the algorithm behind it. I want to know how to calculate it, the pieces that go in and where they come from. I want to code it up, to test it out on odd cases and see how it behaves, to get a feel for what it can do.

Others need a more physical picture. They need to know where the particles are going, or how energy and momentum are conserved. They want entropy to be increased, action to be minimized, scales to make sense dimensionally.

Others in turn are more mathematical. They want to start with definitions and axioms. To understand something, they want to see it as an example of a broader class of thing, groups or algebras or categories, to fit it into a bigger picture.

Each of these are a kind of translation, turning something into code-speak or physics-speak or math-speak. They don’t require modeling every detail, but when done well they can still explain every detail.

So while yes, it is good practice not to write code that is too “smart”, and too hard to debug…it’s not impossible to debug your smartest code. And while you can’t hold an entire mind inside of yours, you don’t actually need to do that to understand the brain. In both cases, all you need is a translation.

Math Is the Art of Stating Things Clearly

Why do we use math?

In physics we describe everything, from the smallest of particles to the largest of galaxies, with the language of mathematics. Why should that one field be able to describe so much? And why don’t we use something else?

The truth is, this is a trick question. Mathematics isn’t a language like English or French, where we can choose whichever translation we want. We use mathematics because it is, almost by definition, the best choice. That is because mathematics is the art of stating things clearly.

An infinite number of mathematicians walk into a bar. The first orders a beer. The second orders half a beer. The third orders a quarter. The bartender stops them, pours two beers, and says “You guys should know your limits.”

That was an (old) joke about infinite series of numbers. You probably learned in high school that if you add up one plus a half plus a quarter…you eventually get two. To be a bit more precise:

\sum_{i=0}^\infty \frac{1}{2^i} = 1+\frac{1}{2}+\frac{1}{4}+\ldots=2

We say that this infinite sum limits to two.

But what does it actually mean for an infinite sum to limit to a number? What does it mean to sum infinitely many numbers, let alone infinitely many beers ordered by infinitely many mathematicians?

You’re asking these questions because I haven’t yet stated the problem clearly. Those of you who’ve learned a bit more mathematics (maybe in high school, maybe in college) will know another way of stating it.

You know how to sum a finite set of beers. You start with one beer, then one and a half, then one and three-quarters. Sum N beers, and you get

\sum_{i=0}^N \frac{1}{2^i}

What does it mean for the sum to limit to two?

Let’s say you just wanted to get close to two. You want to get \epsilon close, where epsilon is the Greek letter we use for really small numbers.

For every \epsilon>0 you choose, no matter how small, I can pick a (finite!) N and get at least that close. That means that, with higher and higher N, I can get as close to two as a I want.

As it turns out, that’s what it means for a sum to limit to two. It’s saying the same thing, but more clearly, without sneaking in confusing claims about infinity.

These sort of proofs, with \epsilon (and usually another variable, \delta) form what mathematicians view as the foundations of calculus. They’re immortalized in story and song.

And they’re not even the clearest way of stating things! Go down that road, and you find more mathematics: definitions of numbers, foundations of logic, rabbit holes upon rabbit holes, all from the effort to state things clearly.

That’s why I’m not surprised that physicists use mathematics. We have to. We need clarity, if we want to understand the world. And mathematicians, they’re the people who spend their lives trying to state things clearly.