Author Archives: 4gravitons

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.

At Bohr-100: Current Themes in Theoretical Physics

During the pandemic, some conferences went online. Others went dormant.

Every summer before the pandemic, the Niels Bohr International Academy hosted a conference called Current Themes in High Energy Physics and Cosmology. Current Themes is a small, cozy conference, a gathering of close friends some of whom happen to have Nobel prizes. Holding it online would be almost missing the point.

Instead, we waited. Now, at least in Denmark, the pandemic is quiet enough to hold this kind of gathering. And it’s a special year: the 100th anniversary of Niels Bohr’s Nobel, the 101st of the Niels Bohr Institute. So it seemed like the time for a particularly special Current Themes.

For one, it lets us use remarkably simple signs

A particularly special Current Themes means some unusually special guests. Our guests are usually pretty special already (Gerard t’Hooft and David Gross are regulars, to just name the Nobelists), but this year we also had Alexander Polyakov. Polyakov’s talk had a magical air to it. In a quiet voice, broken by an impish grin when he surprised us with a joke, Polyakov began to lay out five unsolved problems he considered interesting. In the end, he only had time to present one, related to turbulence: when Gross asked him to name the remaining four, the second included a term most of us didn’t recognize (striction, known in a magnetic context and which he wanted to explore gravitationally), so the discussion hung while he defined that and we never did learn what the other three problems were.

At the big 100th anniversary celebration earlier in the spring, the Institute awarded a few years worth of its Niels Bohr Institute Medal of Honor. One of the recipients, Paul Steinhardt, couldn’t make it then, so he got his medal now. After the obligatory publicity photos were taken, Steinhardt entertained us all with a colloquium about his work on quasicrystals, including the many adventures involved in finding the first example “in the wild”. I can’t do the story justice in a short blog post, but if you won’t have the opportunity to watch him speak about it then I hear his book is good.

An anniversary conference should have some historical elements as well. For this one, these were ably provided by David Broadhurst, who gave an after-dinner speech cataloguing things he liked about Bohr. Some was based on public information, but the real draw were the anecdotes: his own reminiscences, and those of people he knew who knew Bohr well.

The other talks covered interesting ground: from deep approaches to quantum field theory, to new tools to understand black holes, to the implications of causality itself. One out of the ordinary talk was by Sabrina Pasterski, who advocated a new model of physics funding. I liked some elements (endowed organizations to further a subfield) and am more skeptical of others (mostly involving NFTs). Regardless it, and the rest of the conference more broadly, spurred a lot of good debate.

Shape the Science to the Statistics, Not the Statistics to the Science

In theatre, and more generally in writing, the advice is always to “show, don’t tell”. You could just tell your audience that Long John Silver is a ruthless pirate, but it works a lot better to show him marching a prisoner off the plank. Rather than just informing with words, you want to make things as concrete as possible, with actions.

There is a similar rule in pedagogy. Pedagogy courses teach you to be explicit about your goals, planning a course by writing down Intended Learning Outcomes. (They never seem amused when I ask about the Unintended Learning Outcomes.) At first, you’d want to write down outcomes like “students will understand calculus” or “students will know what a sine is”. These, however, are hard to judge, and thus hard to plan around. Instead, the advice is to write outcomes that correspond to actions you want the students to take, things you want them to be capable of doing: “students can perform integration by parts” “students can decide correctly whether to use a sine or cosine”. Again and again, the best way to get the students to know something is to get them to do something.

Jay Daigle recently finished a series of blog posts on how scientists use statistics to test hypotheses. I recommend it, it’s a great introduction to the concepts scientists use to reason about data, as well as a discussion of how they often misuse those concepts and what they can do better. I have a bit of a different perspective on one of the “takeaways” of the post, and I wanted to highlight that here.

The center of Daigle’s point is a tool, widely used in science, called Neyman-Pearson Hypothesis Testing. Neyman-Pearson is a tool for making decisions involving a threshold for significance: a number that scientists often call a p-value. If you follow the procedure, only acting when you find a p-value below 0.05, then you will only be wrong 5% of the time: specifically, that will be your rate of false positives, the percent of the time you conclude some action works when it really doesn’t.

A core problem, from Daigle’s perspective, is that scientists use Neyman-Pearson for the wrong purpose. Neyman-Pearson is a tool for making decisions, not a test that tells you whether or not a specific claim is true. It tells you “on average, if I approve drugs when their p-value is below 0.05, only 5% of them will fail”. That’s great if you can estimate how bad it is to deny a drug that should be approved, how bad it is to approve a drug that should be denied, and calculate out on average how often you can afford to be wrong. It doesn’t tell you anything about the specific drug, though. It doesn’t tell you “every drug with a p-value below 0.05 works”. It certainly doesn’t tell you “a drug with a p-value of 0.051 almost works” or “a drug with a p-value of 0.001 definitely works”. It just doesn’t give you that information.

In later posts, Daigle suggests better tools, which he argues map better to what scientists want to do, as well as general ways scientists can do better. Section 4. in particular focuses on the idea that one thing scientists need to do is ask better questions. He uses a specific example from cognitive psychology, a study that tests whether describing someone’s face makes you worse at recognizing it later. That’s a clear scientific question, one that can be tested statistically. That doesn’t mean it’s a good question, though. Daigle points out that questions like this have a problem: it isn’t clear what the result actually tells us.

Here’s another example of the same problem. In grad school, I knew a lot of social psychologists. One was researching a phenomenon called extended contact. Extended contact is meant to be a foil to another phenomenon called direct contact, both having to do with our views of other groups. In direct contact, making a friend from another group makes you view that whole group better. In extended contact, making a friend who has a friend from another group makes you view the other group better.

The social psychologist was looking into a concrete-sounding question: which of these phenomena, direct or extended contact, is stronger?

At first, that seems like it has the same problem as Daigle’s example. Suppose one of these effects is larger: what does that mean? Why do we care?

Well, one answer is that these aren’t just phenomena: they’re interventions. If you know one phenomenon is stronger than another, you can use that to persuade people to be more accepting of other groups. The psychologist’s advisor even had a procedure to make people feel like they made a new friend. Armed with that, it’s definitely useful to know whether extended contact or direct contact is better: whichever one is stronger is the one you want to use!

You do need some “theory” behind this, of course. You need to believe that, if a phenomenon is stronger in your psychology lab, it will be stronger wherever you try to apply it in the real world. It probably won’t be stronger every single time, so you need some notion of how much stronger it needs to be. That in turn means you need to estimate costs: what it costs if you pick the weaker one instead, how much money you’re wasting or harm you’re doing.

You’ll notice this is sounding a lot like the requirements I described earlier, for Neyman-Pearson. That’s not accident: as you try to make your science more and more clearly defined, it will get closer and closer to a procedure to make a decision, and that’s exactly what Neyman-Pearson is good for.

So in the end I’m quite a bit more supportive of Neyman-Pearson than Daigle is. That doesn’t mean it isn’t being used wrong: most scientists are using it wrong. Instead of calculating a p-value each time they make a decision, they do it at the end of a paper, misinterpreting it as evidence that one thing or another is “true”. But I think that what these scientists need to do is not chance their statistics, but change their science. If they focused their science on making concrete decisions, they would actually be justified in using Neyman-Pearson…and their science would get a lot better in the process.

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 Folks With the Best Pictures

Sometimes I envy astronomers. Particle physicists can write books full of words and pages of colorful graphs and charts, and the public won’t retain any of it. Astronomers can mesmerize the world with a single picture.

NASA just released the first images from its James Webb Space Telescope. They’re impressive, and not merely visually: in twelve hours, they probe deeper than the Hubble Space Telescope managed in weeks on the same patch of sky, as well as gathering data that can show what kinds of molecules are present in the galaxies.

(If you’re curious how the James Webb images compare to Hubble ones, here’s a nice site comparing them.)

Images like this enter the popular imagination. The Hubble telescope’s deep field has appeared on essentially every artistic product one could imagine. As of writing this, searching for “Hubble” on Etsy gives almost 5,000 results. “JWST”, the acronym for the James Webb Space Telescope, already gives over 1,000, including several on the front page that already contain just-released images. Despite the Large Hadron Collider having operated for over a decade, searching “LHC” also leads to just around 1,000 results…and a few on the front page are actually pictures of the JWST!

It would be great as particle physicists to have that kind of impact…but I think we shouldn’t stress ourselves too much about it. Ultimately astronomers will always have this core advantage. Space is amazing, visually stunning and mind-bogglingly vast. It has always had a special place for human cultures, and I’m happy for astronomers to inherit that place.

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.

Carving Out the Possible

If you imagine a particle physicist, you probably picture someone spending their whole day dreaming up new particles. They figure out how to test those particles in some big particle collider, and for a lucky few their particle gets discovered and they get a Nobel prize.

Occasionally, a wiseguy asks if we can’t just cut out the middleman. Instead of dreaming up particles to test, why don’t we just write down every possible particle and test for all of them? It would save the Nobel committee a lot of money at least!

It turns out, you can sort of do this, through something called Effective Field Theory. An Effective Field Theory is a type of particle physics theory that isn’t quite true: instead, it’s “effectively” true, meaning true as long as you don’t push it too far. If you test it at low energies and don’t “zoom in” too much then it’s fine. Crank up your collider energy high enough, though, and you expect the theory to “break down”, revealing new particles. An Effective Field Theory lets you “hide” unknown particles inside new interactions between the particles we already know.

To help you picture how this works, imagine that the pink and blue lines here represent familiar particles like electrons and quarks, while the dotted line is a new particle somebody dreamed up. (The picture is called a Feynman diagram, if you don’t know what that is check out this post.)

In an Effective Field Theory, we “zoom out”, until the diagram looks like this:

Now we’ve “hidden” the new particle. Instead, we have a new type of interaction between the particles we already know.

So instead of writing down every possible new particle we can imagine, we only have to write down every possible interaction between the particles we already know.

That’s not as hard as it sounds. In part, that’s because not every interaction actually makes sense. Some of the things you could write down break some important rules. They might screw up cause and effect, letting something happen before its cause instead of after. They might screw up probability, giving you a formula for the chance something happens that gives a number greater than 100%.

Using these rules you can play a kind of game. You start out with a space representing all of the interactions you can imagine. You begin chipping at it, carving away parts that don’t obey the rules, and you see what shape is left over. You end up with plots that look a bit like carving a ham.

People in my subfield are getting good at this kind of game. It isn’t quite our standard fare: usually, we come up with tricks to make calculations with specific theories easier. Instead, many groups are starting to look at these general, effective theories. We’ve made friends with groups in related fields, building new collaborations. There still isn’t one clear best way to do this carving, so each group manages to find a way to chip a little farther. Out of the block of every theory we could imagine, we’re carving out a space of theories that make sense, theories that could conceivably be right. Theories that are worth testing.