What I Was Not Saying in My Last Post

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Science communication is a gradual process. Anything we say is incomplete, prone to cause misunderstanding. Luckily, we can keep talking, give a new explanation that corrects those misunderstandings. This of course will lead to new misunderstandings. We then explain again, and so on. It sounds fruitless, but in practice our audience nevertheless gets closer and closer to the truth.

Last week, I tried to explain physicists’ notion of a fundamental particle. In particular, I wanted to explain what these particles aren’t: tiny, indestructible spheres, like Democritus imagined. Instead, I emphasized the idea of fields, interacting and exchanging energy, with particles as just the tip of the field iceberg.

I’ve given this kind of explanation before. And when I do, there are two things people often misunderstand. These correspond to two topics which use very similar language, but talk about different things. So this week, I thought I’d get ahead of the game and correct those misunderstandings.

The first misunderstanding: None of that post was quantum.

If you’ve heard physicists explain quantum mechanics, you’ve probably heard about wave-particle duality. Things we thought were waves, like light, also behave like particles, things we thought were particles, like electrons, also behave like waves.

If that’s on your mind, and you see me say particles don’t exist, maybe you think I mean waves exist instead. Maybe when I say “fields”, you think I’m talking about waves. Maybe you think I’m choosing one side of the duality, saying that waves exist and particles don’t.

To be 100% clear: I am not saying that.

Particles and waves, in quantum physics, are both manifestations of fields. Is your field just at one specific point? Then it’s a particle. Is it spread out, with a fixed wavelength and frequency? Then it’s a wave. These are the two concepts connected by wave-particle duality, where the same object can behave differently depending on what you measure. And both of them, to be clear, come from fields. Neither is the kind of thing Democritus imagined.

The second misunderstanding: This isn’t about on-shell vs. off-shell.

Some of you have seen some more “advanced” science popularization. In particular, you might have listened to Nima Arkani-Hamed, of amplituhedron fame, talk about his perspective on particle physics. Nima thinks we need to reformulate particle physics, as much as possible, “on-shell”. “On-shell” means that particles obey their equations of motion, normally quantum calculations involve “off-shell” particles that violate those equations.

To again be clear: I’m not arguing with Nima here.

Nima (and other people in our field) will sometimes talk about on-shell vs off-shell as if it was about particles vs. fields. Normal physicists will write down a general field, and let it be off-shell, we try to do calculations with particles that are on-shell. But once again, on-shell doesn’t mean Democritus-style. We still don’t know what a fully on-shell picture of physics will look like. Chances are it won’t look like the picture of sloshing, omnipresent fields we started with, at least not exactly. But it won’t bring back indivisible, unchangeable atoms. Those are gone, and we have no reason to bring them back.

These Ain’t Democritus’s Particles

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Physicists talk a lot about fundamental particles. But what do we mean by fundamental?

The Ancient Greek philosopher Democritus thought the world was composed of fundamental indivisible objects, constantly in motion. He called these objects “atoms”, and believed they could never be created or destroyed, with every other phenomenon explained by different types of interlocking atoms.

The things we call atoms today aren’t really like this, as you probably know. Atoms aren’t indivisible: their electrons can be split from their nuclei, and with more energy their nuclei can be split into protons and neutrons. More energy yet, and protons and neutrons can in turn be split into quarks. Still, at this point you might wonder: could quarks be Democritus’s atoms?

In a word, no. Nonetheless, quarks are, as far as we know, fundamental particles. As it turns out, our “fundamental” is very different from Democritus’s. Our fundamental particles can transform.

Think about beta decay. You might be used to thinking of it in terms of protons and neutrons: an unstable neutron decays, becoming a proton, an electron, and an (electron-anti-)neutrino. You might think that when the neutron decays, it literally “decays”, falling apart into smaller pieces.

But when you look at the quarks, the neutron’s smallest pieces, that isn’t the picture at all. In beta decay, a down quark in the neutron changes, turning into an up quark and an unstable W boson. The W boson then decays into an electron and a neutrino, while the up quark becomes part of the new proton. Even looking at the most fundamental particles we know, Democritus’s picture of unchanging atoms just isn’t true.

Could there be some even lower level of reality that works the way Democritus imagined? It’s not impossible. But the key insight of modern particle physics is that there doesn’t need to be.

As far as we know, up quarks and down quarks are both fundamental. Neither is “made of” the other, or “made of” anything else. But they also aren’t little round indestructible balls. They’re manifestations of quantum fields, “ripples” that slosh from one sort to another in complicated ways.

When we ask which particles are fundamental, we’re asking what quantum fields we need to describe reality. We’re asking for the simplest explanation, the simplest mathematical model, that’s consistent with everything we could observe. So “fundamental” doesn’t end up meaning indivisible, or unchanging. It’s fundamental like an axiom: used to derive the rest.

Understanding Is Translation

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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.

Valentine’s Day Physics Poem 2020

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It’s Valentine’s Day, time for my traditional physics poem. I’m trying a new format this year, let me know what you think!

Cherish the Effective

Self-styled wise men waste away, pining for the Ultimate Theory.
I tell you now: spurn their fate.
Scorn the Ultimate.
Cherish the Effective.
 
When you dream of an Ultimate Theory, what do you see?
An answer to your every question?
Every worry,
Every weakness,
Resolved?
A thing of beauty?
A thing of
       your notion
              and only your notion
of beauty?
 
Nature, she has her own worries.
Science, she never stops asking.
Your fairytale ending?
You won’t get it.
And you’ll hurt, and be hurt, in the trying.
 
You need a theory that isn’t an ending.
A theory you
              only
                   start
                          understanding
But can always discover.
No rigid, final truth,
But gentle corrections.
And as you push
                The scale
                           The energy
Your theory always has room for something new.
 
A theory like that, we call Effective.
A theory you can live
                      your
                           life
                                with.
It’s worth more than you think.

You Can’t Anticipate a Breakthrough

A Scale of “Sure-Thing-Ness” for Experiments

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No experiment is a sure thing. No matter what you do, what you test, what you observe, there’s no guarantee that you find something new. Even if you do your experiment correctly and measure what you planned to measure, nature might not tell you anything interesting.

Still, some experiments are more sure than others. Sometimes you’re almost guaranteed to learn something, even if it wasn’t what you hoped, while other times you just end up back where you started.

The first, and surest, type of experiment, is a voyage into the unknown. When nothing is known about your target, no expectations, and no predictions, then as long as you successfully measure anything you’ll have discovered something new. This can happen if the thing you’re measuring was only recently discovered. If you’re the first person who manages to measure the reaction rates of an element, or the habits of an insect, or the atmosphere of a planet, then you’re guaranteed to find out something you didn’t know before.

If you don’t have a total unknown to measure, then you want to test a clear hypothesis. The best of these are the theory killers, experiments which can decisively falsify an idea. History’s most famous experiments take this form, like the measurement of the perihelion of Mercury to test General Relativity or Pasteur’s tests of spontaneous generation. When you have a specific prediction and not much wiggle room, an experiment can teach you quite a lot.

“Not much wiggle room” is key, because these tests can all to easily become theory modifiers instead. If you can tweak your theory enough, then your experiment might not be able to falsify it. Something similar applies when you have a number of closely related theories. Even if you falsify one, you can just switch to another similar idea. In those cases, testing your theory won’t always teach you as much: you have to get lucky and see something that pins your theory down more precisely.

Finally, you can of course be just looking. Some experiments are just keeping an eye out, in the depths of space or the precision of quantum labs, watching for something unexpected. That kind of experiment might never see anything, and never rule anything out, but they can still sometimes be worthwhile.

There’s some fuzziness to these categories, of course. Often when scientists argue about whether an experiment is worth doing they’re arguing about which category to place it in. Would a new collider be a “voyage into the unknown” (new energy scales we’ve never measured before), a theory killer/modifier (supersymmetry! but which one…) or just “just looking”? Is your theory of cosmology specific enough to be “killed”, or merely “modified”? Is your wacky modification of quantum mechanics something that can be tested, or merely “just looked” for?

For any given experiment, it’s worth keeping in mind what you expect, and what would happen if you’re wrong. In science, we can’t do every experiment we want. We have to focus our resources and try to get results. Even if it’s never a sure thing.

Math Is the Art of Stating Things Clearly

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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.

What Do Theorists Do at Work?

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Picture a scientist at work. You’re probably picturing an experiment, test tubes and beakers bubbling away. But not all scientists do experiments. Theoretical physicists work on the mathematical side of the field, making predictions and trying to understand how to make them better. So what does it look like when a theoretical physicist is working?

The first thing you might imagine is that we just sit and think. While that happens sometimes, we don’t actually do that very often. It’s better, and easier, to think by doing something.

Sometimes, this means working with pen and paper. This should be at least a little familiar to anyone who has done math homework. We’ll do short calculations and draw quick diagrams to test ideas, and do a more detailed, organized, “show your work” calculation if we’re trying to figure out something more complicated. Sometimes very short calculations are done on a blackboard instead, it can help us visualize what we’re doing.

Sometimes, we use computers instead. There are computer algebra packages, like Mathematica, Maple, or Sage, that let us do roughly what we would do on pen and paper, but with the speed and efficiency of a computer. Others program in more normal programming languages: C++, Python, even Fortran, making programs that can calculate whatever they are interested in.

Sometimes we read. With most of our field’s papers available for free on arXiv.org, we spend time reading up on what our colleagues have done, trying to understand their work and use it to improve ours.

Sometimes we talk. A paper can only communicate so much, and sometimes it’s better to just walk down the hall and ask a question. Conversations are also a good way to quickly rule out bad ideas, and narrow down to the promising ones. Some people find it easier to think clearly about something if they talk to a colleague about it, even (sometimes especially) if the colleague isn’t understanding much.

And sometimes, of course, we do all the other stuff. We write up our papers, making the diagrams nice and the formulas clean. We teach students. We go to meetings. We write grant applications.

It’s been said that a theoretical physicist can work anywhere. That’s kind of true. Some places are more comfortable, and everyone has different preferences: a busy office, a quiet room, a cafe. But with pen and paper, a computer, and people to talk to, we can do quite a lot.

The Road to Reality

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I build tools, mathematical tools to be specific, and I want those tools to be useful. I want them to be used to study the real world. But when I build those tools, most of the time, I don’t test them on the real world. I use toy models, simpler cases, theories that don’t describe reality and weren’t intended to.

I do this, in part, because it lets me stay one step ahead. I can do more with those toy models, answer more complicated questions with greater precision, than I can for the real world. I can do more ambitious calculations, and still get an answer. And by doing those calculations, I can start to anticipate problems that will crop up for the real world too. Even if we can’t do a calculation yet for the real world, if it requires too much precision or too many particles, we can still study it in a toy model. Then when we’re ready to do those calculations in the real world, we know better what to expect. The toy model will have shown us some of the key challenges, and how to tackle them.

There’s a risk, working with simpler toy models. The risk is that their simplicity misleads you. When you solve a problem in a toy model, could you solve it only because the toy model is easy? Or would a similar solution work in the real world? What features of the toy model did you need, and which are extra?

The only way around this risk is to be careful. You have to keep track of how your toy model differs from the real world. You must keep in mind difficulties that come up on the road to reality: the twists and turns and potholes that real-world theories will give you. You can’t plan around all of them, that’s why you’re working with a toy model in the first place. But for a few key, important ones, you should keep your eye on the horizon. You should keep in mind that, eventually, the simplifications of the toy model will go away. And you should have ideas, perhaps not full plans but at least ideas, for how to handle some of those difficulties. If you put the work in, you stand a good chance of building something that’s useful, not just for toy models, but for explaining the real world.

Why You Might Want to Bootstrap

4 gravitons

Stories about physics from someone who's been there