Tag Archives: DoingScience

Amplitudes 2022 Retrospective

I’m back from Amplitudes 2022 with more time to write, and (besides the several papers I’m working on) that means writing about the conference! Casual readers be warned, there’s no way around this being a technical post, I don’t have the space to explain everything!

I mostly said all I wanted about the way the conference was set up in last week’s post, but one thing I didn’t say much about was the conference dinner. Most conference dinners are the same aside from the occasional cool location or haggis speech. This one did have a cool location, and a cool performance by a blind pianist, but the thing I really wanted to comment on was the setup. Typically, the conference dinner at Amplitudes is a sit-down affair: people sit at tables in one big room, maybe getting up occasionally to pick up food, and eventually someone gives an after-dinner speech. This time the tables were standing tables, spread across several rooms. This was a bit tiring on a hot day, but it did have the advantage that it naturally mixed people around. Rather than mostly talking to “your table”, you’d wander, ending up at a new table every time you picked up new food or drinks. It was a good way to meet new people, a surprising number of which in my case apparently read this blog. It did make it harder to do an after-dinner speech, so instead Lance gave an after-conference speech, complete with the now-well-established running joke where Greta Thunberg tries to get us to fly less.

(In another semi-running joke, the organizers tried to figure out who had attended the most of the yearly Amplitudes conferences over the years. Weirdly, no-one has attended all twelve.)

In terms of the content, and things that stood out:

Nima is getting close to publishing his newest ‘hedron, the surfacehedron, and correspondingly was able to give a lot more technical detail about it. (For his first and most famous amplituhedron, see here.) He still didn’t have enough time to explain why he has to use category theory to do it, but at least he was concrete enough that it was reasonably clear where the category theory was showing up. (I wasn’t there for his eight-hour lecture at the school the week before, maybe the students who stuck around until 2am learned some category theory there.) Just from listening in on side discussions, I got the impression that some of the ideas here actually may have near-term applications to computing Feynman diagrams: this hasn’t been a feature of previous ‘hedra and it’s an encouraging development.

Alex Edison talked about progress towards this blog’s namesake problem, the question of whether N=8 supergravity diverges at seven loops. Currently they’re working at six loops on the N=4 super Yang-Mills side, not yet in a form it can be “double-copied” to supergravity. The tools they’re using are increasingly sophisticated, including various slick tricks from algebraic geometry. They are looking to the future: if they’re hoping their methods will reach seven loops, the same methods have to make six loops a breeze.

Xi Yin approached a puzzle with methods from String Field Theory, prompting the heretical-for-us title “on-shell bad, off-shell good”. A colleague reminded me of a local tradition for dealing with heretics.

While Nima was talking about a new ‘hedron, other talks focused on the original amplituhedron. Paul Heslop found that the amplituhedron is not literally a positive geometry, despite slogans to the contrary, but what it is is nonetheless an interesting generalization of the concept. Livia Ferro has made more progress on her group’s momentum amplituhedron: previously only valid at tree level, they now have a picture that can accomodate loops. I wasn’t sure this would be possible, there are a lot of things that work at tree level and not for loops, so I’m quite encouraged that this one made the leap successfully.

Sebastian Mizera, Andrew McLeod, and Hofie Hannesdottir all had talks that could be roughly summarized as “deep principles made surprisingly useful”. Each took topics that were explored in the 60’s and translated them into concrete techniques that could be applied to modern problems. There were surprisingly few talks on the completely concrete end, on direct applications to collider physics. I think Simone Zoia’s was the only one to actually feature collider data with error bars, which might explain why I singled him out to ask about those error bars later.

Likewise, Matthias Wilhelm’s talk was the only one on functions beyond polylogarithms, the elliptic functions I’ve also worked on recently. I wonder if the under-representation of some of these topics is due to the existence of independent conferences: in a year when in-person conferences are packed in after being postponed across the pandemic, when there are already dedicated conferences for elliptics and practical collider calculations, maybe people are just a bit too tired to go to Amplitudes as well.

Talks on gravitational waves seem to have stabilized at roughly a day’s worth, which seems reasonable. While the subfield’s capabilities continue to be impressive, it’s also interesting how often new conceptual challenges appear. It seems like every time a challenge to their results or methods is resolved, a new one shows up. I don’t know whether the field will ever get to a stage of “business as usual”, or whether it will be novel qualitative questions “all the way up”.

I haven’t said much about the variety of talks bounding EFTs and investigating their structure, though this continues to be an important topic. And I haven’t mentioned Lance Dixon’s talk on antipodal duality, largely because I’m planning a post on it later: Quanta Magazine had a good article on it, but there are some aspects even Quanta struggled to cover, and I think I might have a good way to do it.

At Amplitudes 2022 in Prague

It’s that time of year again! I’m at the big yearly conference of my subfield, Amplitudes, this year in Prague.

The conference poster included a picture of Prague’s famous clock, which is admittedly cool. But I think this computer-generated anachronism from Matt Schwartz’s machine learning talk is much more fun.

Amplitudes has grown, and keeps growing. The last time we met in person, there were 175 of us. This year, many people are skipping: some avoiding travel due to COVID, others just exhausted from a summer filled with long-postponed conferences. Nonetheless, we have more people here than then: 222 registered participants!

The large number of people means a large number of talks. Almost all were quite short, 25+5 minutes. Some speakers took advantage of the short length to deliver very accessible talks. Others seemed to think of the time limit as an excuse to cut short the introduction and dive right into technical details. We had just a few 40+5 minute talks, each a review from an adjacent field.

It’s been fun seeing people in person again. I think half of my conversations started with “It’s been a long time!” It’s easy for motivation to wane when you don’t have regular contact with the wider field, getting enthusiastic about shared goals and brainstorming big questions.

I’ll probably give a longer retrospective later: the packed schedule means I don’t have much time to write! But I can say that I’ve largely enjoyed this, the organizers were organized and the presenters presented and things felt a bit more like they ought to in the world.

Covering the Angles

One way to think of science is of a lot of interesting little problems. Some scientists are driven by questions like “how does this weird cell work?” or “how accurately can I predict the chance these particles collide?” If the puzzles are fun enough and the questions are interesting enough, then that can be enough motivation on its own.

Another perspective thinks of science as pursuit of a few big problems. Physicists want to write down the laws of nature, to know where the universe came from, to reconcile gravity and quantum mechanics. Biologists want to understand how life works and manipulate it, psychologists want the same for the human mind. For some scientists, these big questions are at the heart of why they do science. Someone in my field once joked he can’t get up in the morning without telling himself “spacetime is doomed”.

Even if you care about the big questions, though, you can’t neglect the small ones. That’s because modern science is collaborative. A big change, like a new particle or a whole new theory of physics, requires confirmation. It’s not enough for one person to propose it. The ideas that last in science last because they crop up in many different places, with many different methods. They last because we check all the angles, compulsively, looking for any direction that might be screwed up.

In those checks, any and all science can be useful. We need the big conceptual leaps from people like Einstein and the careful and systematic measurements of Brahe. We need people who look for the wackiest ideas, not just because they might be true, but to rule them out when they’re false, to make us all the more confident we’re on the right path. We need people pushing tried-and-true theories to the next leap of precision, to show that nothing is hiding in the gaps and make it clearer when something is. We need many people pushing many different paths: all are necessary, and any one might be crucial.

Often, one of these paths gets the lion’s share of the glory: the press, the Nobel, the mention in the history books. But the other paths still matter: we wouldn’t be confident in the science if they didn’t exist. Most working scientists will be on those other paths, as a matter of course. But we still need them to get science done.

The Conference Dilemma: Freshness vs. Breadth

Back in 2017, I noticed something that should have struck me as a little odd. My sub-field has a big yearly conference, called Amplitudes, that brings in everyone who works on our kind of research. Amplitudes 2017 was fun, but not “fresh”: most people talked about work they had already published. A smaller conference I went to that year, called QCD Meets Gravity, was much “fresher”: a lot of discussion of work in progress and work “hot off the presses”.

At the time, I chalked the difference up to timing: it was a few months later, and people happened to have projects that matured around then. But I realized recently there’s another reason, one why you would expect bigger conferences to have less fresh content.

See, I’ve recently been on the other “side of the curtain”: I was an organizer for Amplitudes last year. And I noticed one big obstacle to having fresh content: the timeframe.

The bigger a conference is, the longer in advance you need to invite speakers. It’s a bigger task to organize everyone, to make sure travel and hotels and raw availability works, that everyone has time to prepare their talks and you have a nice full (but not too full) schedule. So when we started asking people, we didn’t know what the “freshest” work was going to be. We had recommendations from our scientific committee (a group of experts in the subfield whose job is to suggest speakers), but in practice the goal is more one of breadth than freshness: we needed to make sure that everybody in our community was represented.

A smaller conference can get around this. It can be organized a bit later, so the organizers have more information about new developments. It covers a smaller area, so the organizers have more information about new hot topics and unpublished results. And it typically invites most of the sub-community anyway, so you’re guaranteed to cover the hot new stuff just by raw completeness.

This doesn’t mean small conferences are “just better” or anything like that. Breadth is genuinely useful: a big conference covering a whole subfield is great for bringing a community together, getting everyone on a shared page and expanding their horizons. There’s a real tradeoff between those goals and getting a conference with the latest progress. It’s not a fixed tradeoff, we can improve both goals at once (I think at Amplitudes we as organizers could have been better at highlighting unpublished work), but we still have to make choices of what to emphasize.

Einstein-Years

Scott Aaronson recently published an interesting exchange on his blog Shtetl Optimized, between him and cognitive psychologist Steven Pinker. The conversation was about AI: Aaronson is optimistic (though not insanely so) Pinker is pessimistic (again, not insanely though). While fun reading, the whole thing would normally be a bit too off-topic for this blog, except that Aaronson’s argument ended up invoking something I do know a bit about: how we make progress in theoretical physics.

Aaronson was trying to respond to an argument of Pinker’s, that super-intelligence is too vague and broad to be something we could expect an AI to have. Aaronson asks us to imagine an AI that is nothing more or less than a simulation of Einstein’s brain. Such a thing isn’t possible today, and might not even be efficient, but it has the advantage of being something concrete we can all imagine. Aarsonson then suggests imagining that AI sped up a thousandfold, so that in one year it covers a thousand years of Einstein’s thought. Such an AI couldn’t solve every problem, of course. But in theoretical physics, surely such an AI could be safely described as super-intelligent: an amazing power that would change the shape of physics as we know it.

I’m not as sure of this as Aaronson is. We don’t have a machine that generates a thousand Einstein-years to test, but we do have one piece of evidence: the 76 Einstein-years the man actually lived.

Einstein is rightly famous as a genius in theoretical physics. His annus mirabilis resulted in five papers that revolutionized the field, and the next decade saw his theory of general relativity transform our understanding of space and time. Later, he explored what general relativity was capable of and framed challenges that deepened our understanding of quantum mechanics.

After that, though…not so much. For Einstein-decades, he tried to work towards a new unified theory of physics, and as far as I’m aware made no useful progress at all. I’ve never seen someone cite work from that period of Einstein’s life.

Aarsonson mentions simulating Einstein “at his peak”, and it would be tempting to assume that the unified theory came “after his peak”, when age had weakened his mind. But while that kind of thing can sometimes be an issue for older scientists, I think it’s overstated. I don’t think careers peak early because of “youthful brains”, and with the exception of genuine dementia I don’t think older physicists are that much worse-off cognitively than younger ones. The reason so many prominent older physicists go down unproductive rabbit-holes isn’t because they’re old. It’s because genius isn’t universal.

Einstein made the progress he did because he was the right person to make that progress. He had the right background, the right temperament, and the right interests to take others’ mathematics and take them seriously as physics. As he aged, he built on what he found, and that background in turn enabled him to do more great things. But eventually, the path he walked down simply wasn’t useful anymore. His story ended, driven to a theory that simply wasn’t going to work, because given his experience up to that point that was the work that interested him most.

I think genius in physics is in general like that. It can feel very broad because a good genius picks up new tricks along the way, and grows their capabilities. But throughout, you can see the links: the tools mastered at one age that turn out to be just right for a new pattern. For the greatest geniuses in my field, you can see the “signatures” in their work, hints at why they were just the right genius for one problem or another. Give one a thousand years, and I suspect the well would eventually run dry: the state of knowledge would no longer be suitable for even their breadth.

…of course, none of that really matters for Aaronson’s point.

A century of Einstein-years wouldn’t have found the Standard Model or String Theory, but a century of physicist-years absolutely did. If instead of a simulation of Einstein, your AI was a simulation of a population of scientists, generating new geniuses as the years go by, then the argument works again. Sure, such an AI would be much more expensive, much more difficult to build, but the first one might have been as well. The point of the argument is simply to show such a thing is possible.

The core of Aaronson’s point rests on two key traits of technology. Technology is replicable: once we know how to build something, we can build more of it. Technology is scalable: if we know how to build something, we can try to build a bigger one with more resources. Evolution can tap into both of these, but not reliably: just because it’s possible to build a mind a thousand times better at some task doesn’t mean it will.

That is why the possibility of AI leads to the possibility of super-intelligence. If we can make a computer that can do something, we can make it do that something faster. That something doesn’t have to be “general”, you can have programs that excel at one task or another. For each such task, with more resources you can scale things up: so anything a machine can do now, a later machine can probably do better. Your starting-point doesn’t necessarily even have to be efficient, or a good algorithm: bad algorithms will take longer to scale, but could eventually get there too.

The only question at that point is “how fast?” I don’t have the impression that’s settled. The achievements that got Pinker and Aarsonson talking, GPT-3 and DALL-E and so forth, impressed people by their speed, by how soon they got to capabilities we didn’t expect them to have. That doesn’t mean that something we might really call super-intelligence is close: that has to do with the details, with what your target is and how fast you can actually scale. And it certainly doesn’t mean that another approach might not be faster! (As a total outsider, I can’t help but wonder if current ML is in some sense trying to fit a cubic with straight lines.)

It does mean, though, that super-intelligence isn’t inconceivable, or incoherent. It’s just the recognition that technology is a master of brute force, and brute force eventually triumphs. If you want to think about what happens in that “eventually”, that’s a very important thing to keep in mind.

Proxies for Proxies

Why pay scientists?

Maybe you care about science itself. You think that exploring the world should be one of our central goals as human beings, that it “makes our country worth defending”.

Maybe you care about technology. You support science because, down the line, you think it will give us new capabilities that improve people’s lives. Maybe you expect this to happen directly, or maybe indirectly as “spinoff” inventions like the internet.

Maybe you just think science is cool. You want the stories that science tells: they entertain you, they give you a place in the world, they help distract from the mundane day to day grind.

Maybe you just think that the world ought to have scientists in it. You can think of it as a kind of bargain, maintaining expertise so that society can tackle difficult problems. Or you can be more cynical, paying early-career scientists on the assumption that most will leave academia and cheapen labor costs for tech companies.

Maybe you want to pay the scientists to teach, to be professors at universities. You notice that they don’t seem to be happy if you don’t let them research, so you throw a little research funding at them, as a treat.

Maybe you just want to grow your empire: your department, your university, the job numbers in your district.

In most jobs, you’re supposed to do what people pay you to do. As a scientist, the people who pay you have all of these motivations and more. You can’t simply choose to do what people pay you to do.

So you come up with a proxy. You sum up all of these ideas, into a vague picture of what all those people want. You have some idea of scientific quality: not just a matter of doing science correctly and carefully, but doing interesting science. It’s not something you ever articulate. It’s likely even contradictory, after all, the goals it approximates often are. Nonetheless, it’s your guide, and not just your guide: it’s the guide of those who hire you, those who choose if you get promoted or whether you get more funding. All of these people have some vague idea in their head of what makes good science, their own proxy for the desires of the vast mass of voters and decision-makers and funders.

But of course, the standard is still vague. Should good science be deep? Which topics are deeper than others? Should it be practical? Practical for whom? Should it be surprising? What do you expect to happen, and what would surprise you? Should it get the community excited? Which community?

As a practicing scientist, you have to build your own proxy for these proxies. The same work that could get you hired in one place might meet blank stares at another, and you can’t build your life around those unpredictable quirks. So you make your own vague idea of what you’re supposed to do, an alchemy of what excites you and what makes an impact and what your friends are doing. You build a stand-in in your head, on the expectation that no-one else will have quite the same stand-in, then go out and convince the other stand-ins to give money to your version. You stand on a shifting pile of unwritten rules, subtler even than some artists, because at the end of the day there’s never a real client to be seen. Just another proxy.

Types of Undergrad Projects

I saw a discussion on twitter recently, about PhD programs in the US. Apparently universities are putting more and more weight whether prospective students published a paper during their Bachelor’s degree. For some, it’s even an informal requirement. Some of those in the discussion were skeptical that the students were really contributing to these papers much, and thought that most of the work must have been done by the papers’ other authors. If so, this would mean universities are relying more and more on a metric that depends on whether students can charm their professors enough to be “included” in this way, rather than their own abilities.

I won’t say all that much about the admissions situation in the US. (Except to say that if you find yourself making up new criteria to carefully sift out a few from a group of already qualified-enough candidates, maybe you should consider not doing that.) What I did want to say a bit about is what undergraduates can typically actually do, when it comes to research in my field.

First, I should clarify that I’m talking about students in the US system here. Undergraduate degrees in Europe follow a different path. Students typically take three years to get a Bachelor’s degree, often with a project at the end, followed by a two-year Master’s degree capped with a Master’s thesis. A European Master’s thesis doesn’t have to result in a paper, but is often at least on that level, while a European Bachelor project typically isn’t. US Bachelor’s degrees are four years, so one might expect a Bachelor’s thesis to be in between a European Bachelor’s project and Master’s thesis. In practice, it’s a bit different: courses for Master’s students in Europe will generally cover material taught to PhD students in the US, so a typical US Bachelor’s student won’t have had some courses that have a big role in research in my field, like Quantum Field Theory. On the other hand, the US system is generally much more flexible, with students choosing more of their courses and having more opportunities to advance ahead of the default path. So while US Bachelor’s students don’t typically take Quantum Field Theory, the more advanced students can and do.

Because of that, how advanced a given US Bachelor’s student is varies. A small number are almost already PhD students, and do research to match. Most aren’t, though. Despite that, it’s still possible for such a student to complete a real research project in theoretical physics, one that results in a real paper. What does that look like?

Sometimes, it’s because the student is working with a toy model. The problems we care about in theoretical physics can be big and messy, involving a lot of details that only an experienced researcher will know. If we’re lucky, we can make a simpler version of the problem, one that’s easier to work with. Toy models like this are often self-contained, the kind of thing a student can learn without all of the background we expect. The models may be simpler than the real world, but they can still be interesting, suggesting new behavior that hadn’t been considered before. As such, with a good choice of toy model an undergraduate can write something that’s worthy of a real physics paper.

Other times, the student is doing something concrete in a bigger collaboration. This isn’t quite the same as the “real scientists” doing all the work, because the student has a real task to do, just one that is limited in scope. Maybe there is particular computer code they need to get working, or a particular numerical calculation they need to do. The calculation may be comparatively straightforward, but in combination with other results it can still merit a paper. My first project as a PhD student was a little like that, tackling one part of a larger calculation. Once again, the task can be quite self-contained, the kind of thing you can teach a student over a summer project.

Undergraduate projects in the US won’t always result in a paper, and I don’t think anyone should expect, or demand, that they do. But a nontrivial number do, and not because the student is “cheating”. With luck, a good toy model or a well-defined sub-problem can lead a Bachelor’s student to make a real contribution to physics, and get a paper in the bargain.

Gateway Hobbies

When biologists tell stories of their childhoods, they’re full of trails of ants and fireflies in jars. Lots of writers start young, telling stories on the playground and making skits with their friends. And the mere existence of “chemistry sets” tells you exactly how many chemists get started. Many fields have these “gateway hobbies”, like gateway drugs for careers, ways that children and teenagers get hooked and gain experience.

Physics is a little different, though. While kids can play with magnets and electricity, there aren’t a whole lot of other “physics hobbies”, especially for esoteric corners like particle physics. Instead, the “gateway hobbies” of physics are more varied, drawing from many different fields.

First, of course, even if a child can’t “do physics”, they can always read about it. Kids will memorize the names of quarks, read about black holes, or watch documentaries about string theory. I’m not counting this as a “physics hobby” because it isn’t really: physics isn’t a collection of isolated facts, but of equations: frameworks you can use to make predictions. Reading about the Big Bang is a good way to get motivated and excited, it’s a great thing to do…but it doesn’t prepare you for the “science part” of the science.

A few efforts at physics popularization get a bit more hands-on. Many come in the form of video games. You can get experience with relativity through Velocity Raptor, quantum mechanics through Quantum Chess, or orbital mechanics through Kerbal Space Program. All of these get just another bit closer to “doing physics” rather than merely reading about it.

One can always gain experience in other fields, and that can be surprisingly relevant. Playing around with a chemistry set gives first-hand experience of the kinds of things that motivated quantum mechanics, and some things that still motivate condensed matter research. Circuits are physics, more directly, even if they’re also engineering: and for some physicists, designing electronic sensors is a huge part of what they do.

Astronomy has a special place, both in the history of physics and the pantheon of hobbies. There’s a huge amateur astronomy community, one that both makes real discoveries and reaches out to kids of all ages. Many physicists got their start looking at the heavens, using it like Newton’s contemporaries as a first glimpse into the mechanisms of nature.

More and more research in physics involves at least some programming, and programming is another activity kids have access to in spades, from Logo to robotics competitions. Learning how to program isn’t just an important skill: it’s also a way for young people to experience a world bound by clear laws and logic, another motivation to study physics.

Of course, if you’re interested in rules and logic, why not go all the way? Plenty of physicists grew up doing math competitions. I have fond memories of Oregon’s Pentagames, and the more “serious” activities go all the way up to the famously challenging Putnam Competition.

Finally, there are physics competitions too, at least in the form of the International Physics Olympiad, where high school students compete in physics prowess.

Not every physicist did these sorts of things, of course: some got hooked later. Others did more than one. A friend of mine who’s always been “Mr. Science” got almost the whole package, with a youth spent exploring the wild west of the early internet, working at a planetarium, and discovering just how easy it is to get legal access to dangerous and radioactive chemicals. There are many paths in to physics, so even if kids can’t “do physics” the same way they “do chemistry”, there’s still plenty to do!

Keeping It Colloquial

In the corners of academia where I hang out, a colloquium is a special kind of talk. Most talks we give are part of weekly seminars for specific groups. For example, the theoretical particle physicists here have a seminar. Each week we invite a speaker, who gives a talk on their recent work. Since they expect an audience of theoretical particle physicists, they can go into more detail.

A colloquium isn’t like that. Colloquia are talks for the whole department: theorists and experimentalists, particle physicists and biophysicists. They’re more prestigious, for big famous professors (or sometimes, for professors interviewing for jobs…). The different audience, and different context, means that the talk plays by different rules.

Recently, I saw a conference full of “colloquium-style” talks, trying to play by these rules. Some succeeded, some didn’t…and I think I now have a better idea of how those rules work.

First, in a colloquium, you’re not just speaking for yourself. You’re an ambassador for your field. For some of the audience, this might be the first time they’ve heard a talk by someone who does your kind of research. You want to give them a good impression, not just about you, but about the whole topic. So while you definitely want to mention your own work, you want to tell a full story, one that gives more than a glimpse of what others are doing as well.

Second, you want to connect to something the audience already knows. With an audience of physicists, you can assume a certain baseline, but not much more than that. You need to make the beginning accessible and start with something familiar. For the conference I mentioned, a talk that did this well was the talk on exoplanets, which started with the familiar planets of the solar system, classifying them in order to show what you might expect exoplanets to look like. In contrast, t’Hooft’s talk did this poorly. His work is exploiting a loophole in a quantum-mechanical argument called Bell’s theorem, which most physicists have heard of. Instead of mentioning Bell’s theorem, he referred vaguely to “criteria from philosophers”, and only even mentioned that near the end of the talk, instead starting with properties of quantum mechanics his audience was much less familiar with.

Moving on, then, you want to present a mystery. So far, everything in the talk has made sense, and your audience feels like they understand. Now, you show them something that doesn’t fit, something their familiar model can’t accommodate. This activates your audience’s scientist instincts: they’re curious now, they want to know the answer. A good example from the conference was a talk on chemistry in space. The speaker emphasized that we can see evidence of complex molecules in space, but that space dust is so absurdly dilute that it seems impossible such molecules could form: two atoms could go a billion years without meeting each other.

You can’t just leave your audience mystified, though. You next have to solve the mystery. Ideally, your solution will be something smart, but simple: something your audience can intuitively understand. This has two benefits. First, it makes you look smart: you described a mysterious problem, and then you show how to solve it! Second, it makes the audience feel smart: they felt the problem was hard, but now they understand how to solve it too. The audience will have good feelings about you as a result, and good feelings about the topic: in some sense, you’ve tied a piece of their self-esteem to knowing the solution to your problem. This was well-done by the speaker discussing space chemistry, who explained that the solution was chemistry on surfaces: if two atoms are on the surface of a dust grain or meteorite, they’re much more likely to react. It was also well-done by a speaker discussing models of diseases like diabetes: he explained the challenge of controlling processes with cells, when cells replicate exponentially, and showed one way they could be controlled, when the immune system kills off any cells that replicate much faster than their neighbors. (He also played the guitar to immune system-themed songs…also a good colloquium strategy for those who can pull it off!)

Finally, a picture is worth a thousand wordsas long as it’s a clear one. For an audience that won’t follow most of your equations, it’s crucial to show them something visual: graphics, puns, pictures of equipment or graphs. Crucially, though, your graphics should be something the audience can understand. If you put up a graph with a lot of incomprehensible detail: parameters you haven’t explained, or just set up in a way your audience doesn’t get, then your audience gets stuck. Much like an unfamiliar word, a mysterious graph will have members of the audience scratching their heads, trying to figure out what it means. They’ll be so busy trying, they’ll miss what you say next, and you’ll lose them! So yes, put in graphs, put in pictures: but make sure that the ones you use, you have time to explain.

Of Snowmass and SAGEX

arXiv-watchers might have noticed an avalanche of papers with the word Snowmass in the title. (I contributed to one of them.)

Snowmass is a place, an area in Colorado known for its skiing. It’s also an event in that place, the Snowmass Community Planning Exercise for the American Physical Society’s Division of Particles and Fields. In plain terms, it’s what happens when particle physicists from across the US get together in a ski resort to plan their future.

Usually someone like me wouldn’t be involved in that. (And not because it’s a ski resort.) In the past, these meetings focused on plans for new colliders and detectors. They got contributions from experimentalists, and a few theorists heavily focused on their work, but not the more “formal” theorists beyond.

This Snowmass is different. It’s different because of Corona, which changed it from a big meeting in a resort to a spread-out series of meetings and online activities. It’s also different because they invited theorists to contribute, and not just those interested in particle colliders. The theorists involved study everything from black holes and quantum gravity to supersymmetry and the mathematics of quantum field theory. Groups focused on each topic submit “white papers” summarizing the state of their area. These white papers in turn get organized and summarized into a few subfields, which in turn contribute to the planning exercise. No-one I’ve talked to is entirely clear on how this works, how much the white papers will actually be taken into account or by whom. But it seems like a good chance to influence US funding agencies, like the Department of Energy, and see if we can get them to prioritize our type of research.

Europe has something similar to Snowmass, called the European Strategy for Particle Physics. It also has smaller-scale groups, with their own purposes, goals, and funding sources. One such group is called SAGEX: Scattering Amplitudes: from Geometry to EXperiment. SAGEX is an Innovative Training Network, an organization funded by the EU to train young researchers, in this case in scattering amplitudes. Its fifteen students are finishing their PhDs and ready to take the field by storm. Along the way, they spent a little time in industry internships (mostly at Maple and Mathematica), and quite a bit of time working on outreach.

They have now summed up that outreach work in an online exhibition. I’ve had fun exploring it over the last couple days. They’ve got a lot of good content there, from basic explanations of relativity and quantum mechanics, to detailed games involving Feynman diagrams and associahedra, to a section that uses solitons as a gentle introduction to integrability. If you’re in the target audience, you should check it out!