# This Week, at Scientific American

I’ve written an article for Scientific American! It went up online this week, the print versions go out on the 25th. The online version is titled “Loopy Particle Math”, the print one is “The Particle Code”, but they’re the same article.

For those who don’t subscribe to Scientific American, sorry about the paywall!

“The Particle Code” covers what will be familiar material to regulars on this blog. I introduce Feynman diagrams, and talk about the “amplitudeologists” who try to find ways around them. I focus on my corner of the amplitudes field, how the work of Goncharov, Spradlin, Vergu, and Volovich introduced us to “symbology”, a set of tricks for taking apart more complicated integrals (or “periods”) into simple logarithmic building blocks. I talk about how my collaborators and I use symbology, using these building blocks to compute amplitudes that would have been impossible with other techniques. Finally, I talk about the frontier of the field, the still-mysterious “elliptic polylogarithms” that are becoming increasingly well-understood.

(I don’t talk about the even more mysterious “Calabi-Yau polylogarithms“…another time for those!)

Working with Scientific American was a fun experience. I got to see how the professionals do things. They got me to clarify and explain, pointing out terms I needed to define and places I should pause to summarize. They took my rough gel-pen drawings and turned them into polished graphics. While I’m still a little miffed about them removing all the contractions, overall I learned a lot, and I think they did a great job of bringing the article to the printed page.

# 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.

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.

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.

# When You Shouldn’t Listen to a Distinguished but Elderly Scientist

Of science fiction author Arthur C. Clarke’s sayings, the most famous is “Clarke’s third law”, that “Any sufficiently advanced technology is indistinguishable from magic.” Almost as famous, though, is his first law:

“When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.”

Recently Michael Atiyah, an extremely distinguished but also rather elderly mathematician, claimed that something was possible: specifically, he claimed it was possible that he had proved the Riemann hypothesis, one of the longest-standing and most difficult puzzles in mathematics. I won’t go into the details here, but people are, well, skeptical.

This post isn’t really about Atiyah. I’m not close enough to that situation to comment. Instead, it’s about a more general problem.

See, the public seems to mostly agree with Clarke’s law. They trust distinguished, elderly scientists, at least when they’re saying something optimistic. Other scientists know better. We know that scientists are human, that humans age…and that sometimes scientific minds don’t age gracefully.

Some of the time, that means Alzheimer’s, or another form of dementia. Other times, it’s nothing so extreme, just a mind slowing down with age, opinions calcifying and logic getting just a bit more fuzzy.

And the thing is, watching from the sidelines, you aren’t going to know the details. Other scientists in the field will, but this kind of thing is almost never discussed with the wider public. Even here, though specific physicists come to mind as I write this, I’m not going to name them. It feels rude, to point out that kind of all-too-human weakness in someone who accomplished so much. But I think it’s important for the public to keep in mind that these people exist. When an elderly Nobelist claims to have solved a problem that baffles mainstream science, the news won’t tell you they’re mentally ill. All you can do is keep your eyes open, and watch for warning signs:

Be wary of scientists who isolate themselves. Scientists who still actively collaborate and mentor almost never have this kind of problem. There’s a nasty feedback loop when those contacts start to diminish. Being regularly challenged is crucial to test scientific ideas, but it’s also important for mental health, especially in the elderly. As a scientist thinks less clearly, they won’t be able to keep up with their collaborators as much, worsening the situation.

Similarly, beware those famous enough to surround themselves with yes-men. With Nobel prizewinners in particular, many of the worst cases involve someone treated with so much reverence that they forget to question their own ideas. This is especially risky when commenting on an unfamiliar field: often, the Nobelist’s contacts in the new field have a vested interest in holding on to their big-name support, and ignoring signs of mental illness.

Finally, as always, bigger claims require better evidence. If everything someone works on is supposed to revolutionize science as we know it, then likely none of it will. The signs that indicate crackpots apply here as well: heavily invoking historical scientists, emphasis on notation over content, a lack of engagement with the existing literature. Be especially wary if the argument seems easy, deep problems are rarely so simple to solve.

Keep this in mind, and the next time a distinguished but elderly scientist states that something is possible, don’t trust them blindly. Ultimately, we’re still humans beings. We don’t last forever.

# The Physics Isn’t New, We Are

Last week, I mentioned the announcement from the IceCube, Fermi-LAT, and MAGIC collaborations of high-energy neutrinos and gamma rays detected from the same source, the blazar TXS 0506+056. Blazars are sources of gamma rays, thought to be enormous spinning black holes that act like particle colliders vastly more powerful than the LHC. This one, near Orion’s elbow, is “aimed” roughly at Earth, allowing us to detect the light and particles it emits. On September 22, a neutrino with energy around 300 TeV was detected by IceCube (a kilometer-wide block of Antarctic ice stuffed with detectors), coming from the direction of TXS 0506+056. Soon after, the satellite Fermi-LAT and ground-based telescope MAGIC were able to confirm that the blazar TXS 0506+056 was flaring at the time. The IceCube team then looked back, and found more neutrinos coming from the same source in earlier years. There are still lingering questions (Why didn’t they see this kind of behavior from other, closer blazars?) but it’s still a nice development in the emerging field of “multi-messenger” astronomy.

It also got me thinking about a conversation I had a while back, before one of Perimeter’s Public Lectures. An elderly fellow was worried about the LHC. He wondered if putting all of that energy in the same place, again and again, might do something unprecedented: weaken the fabric of space and time, perhaps, until it breaks? He acknowledged this didn’t make physical sense, but what if we’re wrong about the physics? Do we really want to take that risk?

At the time, I made the same point that gets made to counter fears of the LHC creating a black hole: that the energy of the LHC is less than the energy of cosmic rays, particles from space that collide with our atmosphere on a regular basis. If there was any danger, it would have already happened. Now, knowing about blazars, I can make a similar point: there are “galactic colliders” with energies so much higher than any machine we can build that there’s no chance we could screw things up on that kind of scale: if we could, they already would have.

This connects to a broader point, about how to frame particle physics. Each time we build an experiment, we’re replicating something that’s happened before. Our technology simply isn’t powerful enough to do something truly unprecedented in the universe: we’re not even close! Instead, the point of an experiment is to reproduce something where we can see it. It’s not the physics itself, but our involvement in it, our understanding of it, that’s genuinely new.

The IceCube experiment itself is a great example of this: throughout Antarctica, neutrinos collide with ice. The only difference is that in IceCube’s ice, we can see them do it. More broadly, I have to wonder how much this is behind the “unreasonable effectiveness of mathematics”: if mathematics is just the most precise way humans have to communicate with each other, then of course it will be effective in physics, since the goal of physics is to communicate the nature of the world to humans!

There may well come a day when we’re really able to do something truly unprecedented, that has never been done before in the history of the universe. Until then, we’re playing catch-up, taking laws the universe has tested extensively and making them legible, getting humanity that much closer to understanding physics that, somewhere out there, already exists.

# Why a New Particle Matters

A while back, when the MiniBoone experiment announced evidence for a sterile neutrino, I was excited. It’s still not clear whether they really found something, here’s an article laying out the current status. If they did, it would be a new particle beyond those predicted by the Standard Model, something like the neutrinos but which doesn’t interact with any of the fundamental forces except gravity.

At the time, someone asked me why this was so exciting. Does it solve the mystery of dark matter, or any other long-standing problems?

The sterile neutrino MiniBoone is suggesting isn’t, as far as I’m aware, a plausible candidate for dark matter. It doesn’t solve any long-standing problems (for example, it doesn’t explain why the other neutrinos are so much lighter than other particles). It would even introduce new problems of its own!

It still matters, though. One reason, which I’ve talked about before, is that each new type of particle implies a new law of nature, a basic truth about the universe that we didn’t know before. But there’s another reason why a new particle matters.

There’s a malaise in particle physics. For most of the twentieth century, theory and experiment were tightly linked. Unexpected experimental results would demand new theory, which would in turn suggest new experiments, driving knowledge forward. That mostly stopped with the Standard Model. There are a few lingering anomalies, like the phenomena we attribute to dark matter, that show the Standard Model can’t be the full story. But as long as every other experiment fits the Standard Model, we have no useful hints about where to go next. We’re just speculating, and too much of that warps the field.

Critics of the physics mainstream pick up on this, but I’m not optimistic about what I’ve seen of their solutions. Peter Woit has suggested that physics should emulate the culture of mathematics, caring more about rigor and being more careful to confirm things before speaking. The title of Sabine Hossenfelder’s “Lost in Math” might suggest the opposite, but I get the impression she’s arguing for something similar: that particle physicists have been using sloppy arguments and should clean up their act, taking foundational problems seriously and talking to philosophers to help clarify their ideas.

Rigor and clarity are worthwhile, but the problems they’ll solve aren’t the ones causing the malaise. If there are problems we can expect to solve just by thinking better, they’re problems that we found by thinking in the first place: quantum gravity theories that stop making sense at very high energies, paradoxical thought experiments with black holes. There, rigor and clarity can matter: to some extent they’re already there, but I can appreciate the argument that it’s not yet nearly enough.

What rigor and clarity won’t do is make physics feel (and function) like it did in the twentieth century. For that, we need new evidence: experiments that disobey the Standard Model, and do it in a clear enough way that we can’t just chalk it up to predictable errors. We need a new particle, or something like it. Without that, our theories are most likely underdetermined by the data, and anything we propose is going to be subjective. Our subjective judgements may get better, we may get rid of the worst-justified biases, but at the end of the day we still won’t have enough information to actually make durable progress.

That’s not a popular message, in part, because it’s not something we can control. There’s a degree of helplessness in realizing that if nature doesn’t throw us a bone then we’ll probably just keep going in circles forever. It’s not the kind of thing that lends itself to a pithy blog post.

If there’s something we can do, it’s to keep our eyes as open as possible, to make sure we don’t miss nature’s next hint. It’s why people are getting excited about low-energy experiments, about precision calculations, about LIGO. Even this seemingly clickbaity proposal that dark matter killed the dinosaurs is motivated by the same sort of logic: if the only evidence for dark matter we have is gravitational, what can gravitational evidence tell us about what it’s made of? In each case, we’re trying to widen our net, to see new phenomena we might have missed.

I suspect that’s why this reviewer was disappointed that Hossenfelder’s book lacked a vision for the future. It’s not that the book lacked any proposals whatsoever. But it lacked this kind of proposal, of a new place to look, where new evidence, and maybe a new particle, might be found. Without that we can still improve things, we can still make progress on deep fundamental mathematical questions, we can kill off the stupidest of the stupid arguments. But the malaise won’t lift, we won’t get back to the health of twentieth century physics. For that, we need to see something new.

# Quelques Houches

For the last two weeks I’ve been at Les Houches, a village in the French Alps, for the Summer School on Structures in Local Quantum Field Theory.

To assist, we have a view of some very large structures in local quantum field theory

Les Houches has a long history of prestigious summer schools in theoretical physics, going back to the activity of Cécile DeWitt-Morette after the second world war. This was more of a workshop than a “school”, though: each speaker gave one talk, and they weren’t really geared for students.

The workshop was organized by Dirk Kreimer and Spencer Bloch, who both have a long track record of work on scattering amplitudes with a high level of mathematical sophistication. The group they invited was an even mix of physicists interested in mathematics and mathematicians interested in physics. The result was a series of talks that managed to both be thoroughly technical and ask extremely deep questions, including “is quantum electrodynamics really an asymptotic series?”, “are there simple graph invariants that uniquely identify Feynman integrals?”, and several talks about something called the Spine of Outer Space, which still sounds a bit like a bad sci-fi novel. Along the way there were several talks showcasing the growing understanding of elliptic polylogarithms, giving me an opportunity to quiz Johannes Broedel about his recent work.

While some of the more mathematical talks went over my head, they spurred a lot of productive dialogues between physicists and mathematicians. Several talks had last-minute slides, added as a result of collaborations that happened right there at the workshop. There was even an entire extra talk, by David Broadhurst, based on work he did just a few days before.

We also had a talk by Jaclyn Bell, a former student of one of the participants who was on a BBC reality show about training to be an astronaut. She’s heavily involved in outreach now, and honestly I’m a little envious of how good she is at it.

# Be Rational, Integrate Our Way!

I’ve got another paper up this week with Jacob Bourjaily, Andrew McLeod, and Matthias Wilhelm, about integrating Feynman diagrams.

If you’ve been following this blog for a while, you might be surprised: most of my work avoids Feynman diagrams at all costs. I’ve changed my mind, in part, because it turns out integrating Feynman diagrams can be a lot easier than I had thought.

At first, I thought Feynman integrals would be hard purely because they’re integrals. Those of you who’ve taken calculus might remember that, while taking derivatives was just a matter of following the rules, doing integrals required a lot more thought. Rather than one set of instructions, you had a set of tricks, meant to try to match your integral to the derivative of some known function. Sometimes the tricks worked, sometimes you just ended up completely lost.

As it turns out, that’s not quite the problem here. When I integrate a Feynman diagram, most of the time I’m expecting a particular kind of result, called a polylogarithm. If you know that’s the end goal, then you really can just follow the rules, using partial-fractioning to break your integral up into simpler integrations, linear pieces that you can match to the definition of polylogarithms. There are even programs that do this for you: Erik Panzer’s HyperInt is an especially convenient one.

Or it would be convenient, if Maple’s GUI wasn’t cursed…

Still, I wouldn’t have expected Feynman integrals to work particularly well, because they require too many integrations. You need to integrate a certain number of times to define a polylogarithm: for the ones we get out of Feynman diagrams, it’s two integrations for each loop the diagram has. The usual ways we calculate Feynman diagrams lead to a lot more integrations: the systematic method, using something called Symanzik polynomials, involves one integration per particle line in the diagram, which usually adds up to a lot more than two per loop.

When I arrived at the Niels Bohr Institute, I assumed everyone in my field knew about Symanzik polynomials. I was surprised when it turned out Jake Bourjaily hadn’t even heard of them. He was integrating Feynman diagrams by what seemed like a plodding, unsystematic method, taking the intro example from textbooks and just applying it over and over, gaining no benefit from all of the beautiful graph theory that goes into the Symanzik polynomials.

I was even more surprised when his method turned out to be the better one.

Avoid Symanzik polynomials, and you can manage with a lot fewer integrations. Suddenly we were pretty close to the “two integrations per loop” sweet spot, with only one or two “extra” integrations to do.

A few more advantages, and Feynman integrals were actually looking reasonable. The final insight came when we realized that just writing the problem in the right variables made a huge difference.

HyperInt, as I mentioned, tries to break a problem up into simpler integrals. Specifically, it’s trying to make things linear in the integration variable. In order to do this, sometimes it has to factor quadratic polynomials, like so:

Notice the square roots in this formula? Those can make your life a good deal trickier. Once you’ve got irrational functions in the game, HyperInt needs extra instructions for how to handle them, and integration is a lot more cumbersome.

The last insight, then, and the key point in our paper, is to avoid irrational functions. To do that, we use variables that rationalize the square roots.

We get these variables from one of the mainstays of our field, called momentum twistors. These variables are most useful in our favorite theory of N=4 super Yang-Mills, but they’re useful in other contexts too. By parametrizing them with a good “chart”, one with only the minimum number of variables we need to capture the integral, we can rationalize most of the square roots we encounter.

That “most” is going to surprise some people. We rationalized all of the expected square roots, letting us do integrals all the way to four loops in a few cases. But there were some unexpected square roots, and those we couldn’t rationalize.

These unexpected square roots don’t just make our life more complicated, if they stick around in a physically meaningful calculation they’ll upset a few other conjectures as well. People had expected that these integrals were made of certain kinds of “letters”, organized by a mathematical structure called a cluster algebra. That cluster algebra structure doesn’t have room for square roots, which suggests that it can’t be the full story here.

The integrals that we can do, though, with no surprise square roots? They’re much easier than anyone expected, much easier than with any other method. Rather than running around doing something fancy, we just integrated things the simple, rational way…and it worked!