Tag Archives: theoretical physics

Book Review: We Have No Idea

I have no idea how I’m going to review this book.

Ok fine, I have some idea.

Jorge Cham writes Piled Higher and Deeper, a webcomic with possibly the most accurate depiction of grad school available. Daniel Whiteson is a professor at the University of California, Irvine, and a member of the ATLAS collaboration (one of the two big groups that make measurements at the Large Hadron Collider). Together, they’ve written a popular science book covering everything we don’t know about fundamental physics.

Writing a book about what we don’t know is an unusual choice, and there was a real risk it would end up as just a superficial gimmick. The pie chart on the cover presents the most famous “things physicists don’t know”, dark matter and dark energy. If they had just stuck to those this would have been a pretty ordinary popular physics book.

Refreshingly, they don’t do that. After blazing through dark matter and dark energy in the first three chapters, the rest of the book focuses on a variety of other scientific mysteries.

The book contains a mix of problems that get serious research attention (matter-antimatter asymmetry, high-energy cosmic rays) and more blue-sky “what if” questions (does matter have to be made out of particles?). As a theorist, I’m not sure that all of these questions are actually mysterious (we do have some explanation of the weird “1/3” charges of quarks, and I’d like to think we understand why mass includes binding energy), but even in these cases what we really know is that they follow from “sensible assumptions”, and one could just as easily ask “what if” about those assumptions instead. Overall, these “what if” questions make the book unique, and it would be a much weaker book without them.

“We Have No Idea” is strongest when the authors actually have some idea, i.e. when Whiteson is discussing experimental particle physics. It gets weaker on other topics, where the authors seem to rely more on others’ popular treatments (their discussion of “pixels of space-time” motivated me to write this post). Still, they at least seem to have asked the right people, and their accounts are on the more accurate end of typical pop science. (Closer to Quanta than IFLScience.)

The book’s humor really ties it together, often in surprisingly subtle ways. Each chapter has its own running joke, initially a throwaway line that grows into metaphors for everything the chapter discusses. It’s a great way to help the audience visualize without introducing too many new concepts at once. If there’s one thing cartoonists can teach science communicators, it’s the value of repetition.

I liked “We Have No Idea”. It could have been more daring, or more thorough, but it was still charming and honest and fun. If you’re looking for a Christmas present to explain physics to your relatives, you won’t go wrong with this book.

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My Other Brain (And My Other Other Brain)

What does a theoretical physicist do all day? We sit and think.

Most of us can’t do all that thinking in our heads, though. Maybe Steven Hawking could, but the rest of us need to visualize what we’re thinking. Our memories, too, are all-too finite, prone to forget what we’re doing midway through a calculation.

So rather than just use our imagination and memory, we use another imagination, another memory: a piece of paper. Writing is the simplest “other brain” we have access to, but even by itself it’s a big improvement, adding weeks of memory and the ability to “see” long calculations at work.

But even augmented by writing, our brains are limited. We can only calculate so fast. What’s more, we get bored: doing the same thing mechanically over and over is not something our brains like to do.

Luckily, in the modern era we have access to other brains: computers.

As I write, the “other brain” sitting on my desk works out a long calculation. Using programs like Mathematica or Maple, or more serious programming languages, I can tell my “other brain” to do something and it will do it, quickly and without getting bored.

My “other brain” is limited too. It has only so much memory, only so much speed, it can only do so many calculations at once. While it’s thinking, though, I can find yet another brain to think at the same time. Sometimes that’s just my desktop, sitting back in my office in Denmark. Sometimes I have access to clusters, blobs of synchronized brains to do my bidding.

While I’m writing this, my “brains” are doing five different calculations (not counting any my “real brain” might be doing). I’m sitting and thinking, as a theoretical physicist should.

A Micrographia of Beastly Feynman Diagrams

Earlier this year, I had a paper about the weird multi-dimensional curves you get when you try to compute trickier and trickier Feynman diagrams. These curves were “Calabi-Yau”, a type of curve string theorists have studied as a way to curl up extra dimensions to preserve something called supersymmetry. At the time, string theorists asked me why Calabi-Yau curves showed up in these Feynman diagrams. Do they also have something to do with supersymmetry?

I still don’t know the general answer. I don’t know if all Feynman diagrams have Calabi-Yau curves hidden in them, or if only some do. But for a specific class of diagrams, I now know the reason. In this week’s paper, with Jacob Bourjaily, Andrew McLeod, and Matthias Wilhelm, we prove it.

We just needed to look at some more exotic beasts to figure it out.

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Like this guy!

Meet the tardigrade. In biology, they’re incredibly tenacious microscopic animals, able to withstand the most extreme of temperatures and the radiation of outer space. In physics, we’re using their name for a class of Feynman diagrams.

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A clear resemblance!

There is a long history of physicists using whimsical animal names for Feynman diagrams, from the penguin to the seagull (no relation). We chose to stick with microscopic organisms: in addition to the tardigrades, we have paramecia and amoebas, even a rogue coccolithophore.

The diagrams we look at have one thing in common, which is key to our proof: the number of lines on the inside of the diagram (“propagators”, which represent “virtual particles”) is related to the number of “loops” in the diagram, as well as the dimension. When these three numbers are related in the right way, it becomes relatively simple to show that any curves we find when computing the Feynman diagram have to be Calabi-Yau.

This includes the most well-known case of Calabi-Yaus showing up in Feynman diagrams, in so-called “banana” or “sunrise” graphs. It’s closely related to some of the cases examined by mathematicians, and our argument ended up pretty close to one made back in 2009 by the mathematician Francis Brown for a different class of diagrams. Oddly enough, neither argument works for the “traintrack” diagrams from our last paper. The tardigrades, paramecia, and amoebas are “more beastly” than those traintracks: their Calabi-Yau curves have more dimensions. In fact, we can show they have the most dimensions possible at each loop, provided all of our particles are massless. In some sense, tardigrades are “as beastly as you can get”.

We still don’t know whether all Feynman diagrams have Calabi-Yau curves, or just these. We’re not even sure how much it matters: it could be that the Calabi-Yau property is a red herring here, noticed because it’s interesting to string theorists but not so informative for us. We don’t understand Calabi-Yaus all that well yet ourselves, so we’ve been looking around at textbooks to try to figure out what people know. One of those textbooks was our inspiration for the “bestiary” in our title, an author whose whimsy we heartily approve of.

Like the classical bestiary, we hope that ours conveys a wholesome moral. There are much stranger beasts in the world of Feynman diagrams than anyone suspected.

IGST 2018

Conference season in Copenhagen continues this week, with Integrability in Gauge and String Theory 2018. Integrability here refers to integrable theories, theories where physicists can calculate things exactly, without the perturbative approximations we typically use. Integrable theories come up in a wide variety of situations, but this conference was focused on the “high-energy” side of the field, on gauge theories (roughly, theories of fundamental forces like Yang-Mills) and string theory.

Integrability is one of the bigger sub-fields in my corner of physics, about the same size as amplitudes. It’s big enough that we can’t host the conference in the old Niels Bohr Institute auditorium.

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Instead, they herded us into the old agriculture school

I don’t normally go to integrability conferences, but when the only cost is bus fare there’s not much to lose. Integrability is arguably amplitudes’s nearest neighbor. The two fields have a history of sharing ideas, and they have similar reputations in the wider community, seen as alternately deep and overly technical. Many of the talks still went over my head, but it was worth getting a chance to see how the neighbors are doing.

Current Themes 2018

I’m at Current Themes in High Energy Physics and Cosmology this week, the yearly conference of the Niels Bohr International Academy. (I talked about their trademark eclectic mix of topics last year.)

This year, the “current theme” was broadly gravitational (though with plenty of exceptions!).

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For example, almost getting kicked out of the Botanical Garden

There were talks on phenomena we observe gravitationally, like dark matter. There were talks on calculating amplitudes in gravity theories, both classical and quantum. There were talks about black holes, and the overall shape of the universe. Subir Sarkar talked about his suspicion that the expansion of the universe isn’t actually accelerating, and while I still think the news coverage of it was overblown I sympathize a bit more with his point. He’s got a fairly specific worry, that we’re in a region that’s moving unusually with respect to the surrounding universe, that hasn’t really been investigated in much detail before. I don’t think he’s found anything definitive yet, but it will be interesting as more data accumulates to see what happens.

Of course, current themes can’t stick to just one theme, so there were non-gravitational talks as well. Nima Arkani-Hamed’s talk covered some results he’s talked about in the past, a geometric picture for constraining various theories, but with an interesting new development: while most of the constraints he found restrict things to be positive, one type of constraint he investigated allowed for a very small negative region, around thirty orders of magnitude smaller than the positive part. The extremely small size of the negative region was the most surprising part of the story, as it’s quite hard to get that kind of extremely small scale out of the math we typically invoke in physics (a similar sense of surprise motivates the idea of “naturalness” in particle physics).

There were other interesting talks, which I might talk about later. They should have slides up online soon in case any of you want to have a look.

Different Fields, Different Worlds

My grandfather is a molecular biologist. When we meet, we swap stories: the state of my field and his, different methods and focuses but often a surprising amount of common ground.

Recently he forwarded me an article by Raymond Goldstein, a biological physicist, arguing that biologists ought to be more comfortable with physical reasoning. The article is interesting in its own right, contrasting how physicists and biologists think about the relationship between models, predictions, and experiments. But what struck me most about the article wasn’t the content, but the context.

Goldstein’s article focuses on a question that seemed to me oddly myopic: should physical models be in the Results section, or the Discussion section?

As someone who has never written a paper with either a Results section or a Discussion section, I wondered why anyone would care. In my field, paper formats are fairly flexible. We usually have an Introduction and a Conclusion, yes, but in between we use however many sections we need to explain what we need to. In contrast, biology papers seem to have a very fixed structure: after the Introduction, there’s a Results section, a Discussion section, and a Materials and Methods section at the end.

At first blush, this seemed incredibly bizarre. Why describe your results before the methods you used to get them? How do you talk about your results without discussing them, but still take a full section to do it? And why do reviewers care how you divide things up in the first place?

It made a bit more sense once I thought about how biology differs from theoretical physics. In theoretical physics, the “methods” are most of the result: unsolved problems are usually unsolved because existing methods don’t solve them, and we need to develop new methods to make progress. Our “methods”, in turn, are often the part of the paper experts are most eager to read. In biology, in contrast, the methods are much more standardized. While papers will occasionally introduce new methods, there are so many unexplored biological phenomena that most of the time researchers don’t need to invent a new method: just asking a question no-one else has asked can be enough for a discovery. In that environment, the “results” matter a lot more: they’re the part that takes the most scrutiny, that needs to stand up on its own.

I can even understand the need for a fixed structure. Biology is a much bigger field than theoretical physics. My field is small enough that we all pretty much know each other. If a paper is hard to read, we’ll probably get a chance to ask the author what they meant. Biology, in contrast, is huge. An important result could come from anywhere, and anyone. Having a standardized format makes it a lot easier to scan through an unfamiliar paper and find what you need, especially when there might be hundreds of relevant papers.

The problem with a standardized system, as always, is the existence of exceptions. A more “physics-like” biology paper is more readable with “physics-like” conventions, even if the rest of the field needs to stay “biology-like”. Because of that, I have a lot of sympathy for Goldstein’s argument, but I can’t help but feel that he should be asking for more. If creating new mathematical models and refining them with observation is at the heart of what Goldstein is doing, then maybe he shouldn’t have to use Results/Discussion/Methods in the first place. Maybe he should be allowed to write biology papers that look more like physics papers.

Adversarial Collaborations for Physics

Sometimes physics debates get ugly. For the scientists reading this, imagine your worst opponents. Think of the people who always misinterpret your work while using shoddy arguments to prop up their own, where every question at a talk becomes a screaming match until you just stop going to the same conferences at all.

Now, imagine writing a paper with those people.

Adversarial collaborations, subject of a recent a contest on the blog Slate Star Codex, are a proposed method for resolving scientific debates. Two scientists on opposite sides of an argument commit to writing a paper together, describing the overall state of knowledge on the topic. For the paper to get published, both sides have to sign off on it: they both have to agree that everything in the paper is true. This prevents either side from cheating, or from coming back later with made-up objections: if a point in the paper is wrong, one side or the other is bound to catch it.

This won’t work for the most vicious debates, when one (or both) sides isn’t interested in common ground. But for some ongoing debates in physics, I think this approach could actually help.

One advantage of adversarial collaborations is in preventing accusations of bias. The debate between dark matter and MOND-like proposals is filled with these kinds of accusations: claims that one group or another is ignoring important data, being dishonest about the parameters they need to fit, or applying standards of proof they would never require of their own pet theory. Adversarial collaboration prevents these kinds of accusations: whatever comes out of an adversarial collaboration, both sides would make sure the other side didn’t bias it.

Another advantage of adversarial collaborations is that they make it much harder for one side to move the goalposts, or to accuse the other side of moving the goalposts. From the sidelines, one thing that frustrates me watching string theorists debate whether the theory can describe de Sitter space is that they rarely articulate what it would take to decisively show that a particular model gives rise to de Sitter. Any conclusion of an adversarial collaboration between de Sitter skeptics and optimists would at least guarantee that both parties agreed on the criteria. Similarly, I get the impression that many debates about interpretations of quantum mechanics are bogged down by one side claiming they’ve closed off a loophole with a new experiment, only for the other to claim it wasn’t the loophole they were actually using, something that could be avoided if both sides were involved in the experiment from the beginning.

It’s possible, even likely, that no-one will try adversarial collaboration for these debates. Even if they did, it’s quite possible the collaborations wouldn’t be able to agree on anything! Still, I have to hope that someone takes the plunge and tries writing a paper with their enemies. At minimum, it’ll be an interesting read!