Tag Archives: particle physics

Why Solving the Muon Puzzle Doesn’t Solve the Puzzle

4 Replies

You may have heard that the muon g-2 problem has been solved.

Muons are electrons’ heavier cousins. As spinning charged particles, they are magnetic, the strength of that magnetism characterized by a number denoted “g”. If you were to guess this number from classical physics alone, you’d conclude it should be 2, but quantum mechanics tweaks it. The leftover part, “g-2”, can be measured, and predicted, with extraordinary precision, which ought to make it an ideal test: if our current understanding of the particle physics, called the Standard Model, is subtly wrong, the difference might be noticeable there.

And for a while, it looked like such a difference was indeed noticeable. Extremely precise experiments over the last thirty years have consistently found a number slightly different from the extremely precise calculations, different enough that it seemed quite unlikely to be due to chance.

Now, the headlines are singing a different tune.

What changed?

That headline might make you think the change was an experimental result, a new measurement that changed the story. It wasn’t, though. There is a new, more precise measurement, but it agrees with the old measurements.

So the change has to be in the calculations, right? They did a new calculation, corrected a mistake or just pushed up their precision, and found that the Standard Model matches the experiment after all?

…sort of, but again, not really. The group of theoretical physicists associated with the experiment did release new, more accurate calculations. But it wasn’t the new calculations, by themselves, that made a difference. Instead, it was a shift in what kind of calculations they used…or even more specifically, what kind of calculations they trusted.

Parts of the calculation of g-2 can be done with Feynman diagrams, those photogenic squiggles you see on physicists’ blackboards. That part is very precise, and not especially controversial. However, Feynman diagrams only work well when forces between particles are comparatively weak. They’re great for electromagnetism, even better for the weak nuclear force. But for the strong nuclear force, the one that holds protons and neutrons together, you often need a different method.

For g-2, that used to be done via a “data-driven” method. Physicists measured different things, particles affected by the strong nuclear force in different ways, and used that to infer how the strong force would affect g-2. By getting a consistent picture from different experiments, they were reasonably confident that they had the right numbers.

Back in 2020, though, a challenger came to the scene, with another method. Called lattice QCD, this method involves building gigantic computer simulations of the effect of the strong force. People have been doing lattice QCD since the 1970’s, and the simulations have been getting better and better, until in 2020, a group managed to calculate the piece of the g-2 calculation that had until then been done by the data-driven method.

The lattice group found a very different result than what had been found previously. Instead of a wild disagreement with experiment, their calculation agreed. According to them, everything was fine, the muon g-2 was behaving exactly as the Standard Model predicted.

For some of us, that’s where the mystery ended. Clearly, something must be wrong with the data-driven method, not with the Standard Model. No more muon puzzle.

But the data-driven method wasn’t just a guess, it was being used for a reason. A significant group of physicists found the arguments behind it convincing. Now, there was a new puzzle: figuring out why the data-driven method and lattice QCD disagree.

Five years later, has that mystery been solved? Is that, finally, what the headlines are about?

Again, not really, no.

The theorists associated with the experiment have decided to trust lattice QCD, not the data-driven method. But they don’t know what went wrong, exactly.

Instead, they’ve highlighted cracks in the data-driven method. The way the data-driven method works, it brings together different experiments to try to get a shared picture. But that shared picture has started to fall apart. A new measurement by a different experiment doesn’t fit into the system: the data-driven method now “has tensions”, as physicists say. It’s no longer possible to combine all experiments into a shared picture they way they used to. Meanwhile, lattice QCD has gotten even better, reaching even higher precision. From the perspective of the theorists associated with the muon g-2 experiment, switching methods is now clearly the right call.

But does that mean they solved the puzzle?

If you were confident that lattice QCD is the right approach, then the puzzle was already solved in 2020. All that changed was the official collaboration finally acknowledging that.

And if you were confident that the data-driven method was the right approach, then the puzzle is even worse. Now, there are tensions within the method itself…but still no explanation of what went wrong! If you had good reasons to think the method should work, you still have those good reasons. Now you’re just…more puzzled.

I am reminded of another mystery, a few years back, when an old experiment announced a dramatically different measurement for the mass of the W boson. Then, I argued the big mystery was not how the W boson’s mass had changed (it hadn’t), but how they came to be so confident in a result so different from what others, also confidently, had found. In physics, our confidence is encoded in numbers, estimated and measured and tested and computed. If we’re not estimating that confidence correctly…then that’s the real mystery, the real puzzle. One much more important to solve.

Also, I had two more pieces out this week! In Quanta I have a short explainer about bosons and fermions, while at Ars Technica I have a piece about machine learning at the LHC. I may have a “bonus info” post on the latter at some point, I have to think about whether I have enough material for it.

Amplitudes 2025 This Week

1 Reply

Summer is conference season for academics, and this week held my old sub-field’s big yearly conference, called Amplitudes. This year, it was in Seoul at Seoul National University, the first time the conference has been in Asia.

(I wasn’t there, I don’t go to these anymore. But I’ve been skimming slides in my free time, to give you folks the updates you crave. Be forewarned that conference posts like these get technical fast, I’ll be back to my usual accessible self next week.)

There isn’t a huge amplitudes community in Korea, but it’s bigger than it was back when I got started in the field. Of the organizers, Kanghoon Lee of the Asia Pacific Center for Theoretical Physics and Sangmin Lee of Seoul National University have what I think of as “core amplitudes interests”, like recursion relations and the double-copy. The other Korean organizers are from adjacent areas, work that overlaps with amplitudes but doesn’t show up at the conference each year. There was also a sizeable group of organizers from Taiwan, where there has been a significant amplitudes presence for some time now. I do wonder if Korea was chosen as a compromise between a conference hosted in Taiwan or in mainland China, where there is also quite a substantial amplitudes community.

One thing that impresses me every year is how big, and how sophisticated, the gravitational-wave community in amplitudes has grown. Federico Buccioni’s talk began with a plot that illustrates this well (though that wasn’t his goal):

At the conference Amplitudes, dedicated to the topic of scattering amplitudes, there were almost as many talks with the phrase “black hole” in the title as there were with “scattering” or “amplitudes”! This is for a topic that did not even exist in the subfield when I got my PhD eleven years ago.

With that said, gravitational wave astronomy wasn’t quite as dominant at the conference as Buccioni’s bar chart suggests. There were a few talks each day on the topic: I counted seven in total, excluding any short talks on the subject in the gong show. Spinning black holes were a significant focus, central to Jung-Wook Kim’s, Andres Luna’s and Mao Zeng’s talks (the latter two showing some interesting links between the amplitudes story and classic ideas in classical mechanics) and relevant in several others, with Riccardo Gonzo, Miguel Correia, Ira Rothstein, and Enrico Herrmann’s talks showing not just a wide range of approaches, but an increasing depth of research in this area.

Herrmann’s talk in particular dealt with detector event shapes, a framework that lets physicists think more directly about what a specific particle detector or observer can see. He applied the idea not just to gravitational waves but to quantum gravity and collider physics as well. The latter is historically where this idea has been applied the most thoroughly, as highlighted in Hua Xing Zhu’s talk, where he used them to pick out particular phenomena of interest in QCD.

QCD is, of course, always of interest in the amplitudes field. Buccioni’s talk dealt with the theory’s behavior at high-energies, with a nice example of the “maximal transcendentality principle” where some quantities in QCD are identical to quantities in N=4 super Yang-Mills in the “most transcendental” pieces (loosely, those with the highest powers of pi). Andrea Guerreri’s talk also dealt with high-energy behavior in QCD, trying to address an experimental puzzle where QCD results appeared to violate a fundamental bound all sensible theories were expected to obey. By using S-matrix bootstrap techniques, they clarify the nature of the bound, finding that QCD still obeys it once correctly understood, and conjecture a weird theory that should be possible to frame right on the edge of the bound. The S-matrix bootstrap was also used by Alexandre Homrich, who talked about getting the framework to work for multi-particle scattering.

Heribertus Bayu Hartanto is another recent addition to Korea’s amplitudes community. He talked about a concrete calculation, two-loop five-particle scattering including top quarks, a tricky case that includes elliptic curves.

When amplitudes lead to integrals involving elliptic curves, many standard methods fail. Jake Bourjaily’s talk raised a question he has brought up again and again: what does it mean to do an integral for a new type of function? One possible answer is that it depends on what kind of numerics you can do, and since more general numerical methods can be cumbersome one often needs to understand the new type of function in more detail. In light of that, Stephen Jones’ talk was interesting in taking a common problem often cited with generic approaches (that they have trouble with the complex numbers introduced by Minkowski space) and finding a more natural way in a particular generic approach (sector decomposition) to take them into account. Giulio Salvatori talked about a much less conventional numerical method, linked to the latest trend in Nima-ology, surfaceology. One of the big selling points of the surface integral framework promoted by people like Salvatori and Nima Arkani-Hamed is that it’s supposed to give a clear integral to do for each scattering amplitude, one which should be amenable to a numerical treatment recently developed by Michael Borinsky. Salvatori can currently apply the method only to a toy model (up to ten loops!), but he has some ideas for how to generalize it, which will require handling divergences and numerators.

Other approaches to the “problem of integration” included Anna-Laura Sattelberger’s talk that presented a method to find differential equations for the kind of integrals that show up in amplitudes using the mathematical software Macaulay2, including presenting a package. Matthias Wilhelm talked about the work I did with him, using machine learning to find better methods for solving integrals with integration-by-parts, an area where two other groups have now also published. Pierpaolo Mastrolia talked about integration-by-parts’ up-and-coming contender, intersection theory, a method which appears to be delving into more mathematical tools in an effort to catch up with its competitor.

Sometimes, one is more specifically interested in the singularities of integrals than their numerics more generally. Felix Tellander talked about a geometric method to pin these down which largely went over my head, but he did have a very nice short description of the approach: “Describe the singularities of the integrand. Find a map representing integration. Map the singularities of the integrand onto the singularities of the integral.”

While QCD and gravity are the applications of choice, amplitudes methods germinate in N=4 super Yang-Mills. Ruth Britto’s talk opened the conference with an overview of progress along those lines before going into her own recent work with one-loop integrals and interesting implications of ideas from cluster algebras. Cluster algebras made appearances in several other talks, including Anastasia Volovich’s talk which discussed how ideas from that corner called flag cluster algebras may give insights into QCD amplitudes, though some symbol letters still seem to be hard to track down. Matteo Parisi covered another idea, cluster promotion maps, which he thinks may help pin down algebraic symbol letters.

The link between cluster algebras and symbol letters is an ongoing mystery where the field is seeing progress. Another symbol letter mystery is antipodal duality, where flipping an amplitude like a palindrome somehow gives another valid amplitude. Lance Dixon has made progress in understanding where this duality comes from, finding a toy model where it can be understood and proved.

Others pushed the boundaries of methods specific to N=4 super Yang-Mills, looking for novel structures. Song He’s talk pushes an older approach by Bourjaily and collaborators up to twelve loops, finding new patterns and connections to other theories and observables. Qinglin Yang bootstraps Wilson loops with a Lagrangian insertion, adding a side to the polygon used in previous efforts and finding that, much like when you add particles to amplitudes in a bootstrap, the method gets stricter and more powerful. Jaroslav Trnka talked about work he has been doing with “negative geometries”, an odd method descended from the amplituhedron that looks at amplitudes from a totally different perspective, probing a bit of their non-perturbative data. He’s finding more parts of that setup that can be accessed and re-summed, finding interestingly that multiple-zeta-values show up in quantities where we know they ultimately cancel out. Livia Ferro also talked about a descendant of the amplituhedron, this time for cosmology, getting differential equations for cosmological observables in a particular theory from a combinatorial approach.

Outside of everybody’s favorite theories, some speakers talked about more general approaches to understanding the differences between theories. Andreas Helset covered work on the geometry of the space of quantum fields in a theory, applying the method to a general framework for characterizing deviations from the standard model called the SMEFT. Jasper Roosmale Nepveu also talked about a general space of theories, thinking about how positivity (a trait linked to fundamental constraints like causality and unitarity) gets tangled up with loop effects, and the implications this has for renormalization.

Soft theorems, universal behavior of amplitudes when a particle has low energy, continue to be a trendy topic, with Silvia Nagy showing how the story continues to higher orders and Sangmin Choi investigating loop effects. Callum Jones talks about one of the more powerful results from the soft limit, Weinberg’s theorem showing the uniqueness of gravity. Weinberg’s proof was set up in Minkowski space, but we may ultimately live in curved, de Sitter space. Jones showed how the ideas Weinberg explored generalize in de Sitter, using some tools from the soft-theorem-inspired field of dS/CFT. Julio Parra-Martinez, meanwhile, tied soft theorems to another trendy topic, higher symmetries, a more general notion of the usual types of symmetries that physicists have explored in the past. Lucia Cordova reported work that was not particularly connected to soft theorems but was connected to these higher symmetries, showing how they interact with crossing symmetry and the S-matrix bootstrap.

Finally, a surprisingly large number of talks linked to Kevin Costello and Natalie Paquette’s work with self-dual gauge theories, where they found exact solutions from a fairly mathy angle. Paquette gave an update on her work on the topic, while Alfredo Guevara talked about applications to black holes, comparing the power of expanding around a self-dual gauge theory to that of working with supersymmetry. Atul Sharma looked at scattering in self-dual backgrounds in work that merges older twistor space ideas with the new approach, while Roland Bittelson talked about calculating around an instanton background.

Also, I had another piece up this week at FirstPrinciples, based on an interview with the (outgoing) president of the Sloan Foundation. I won’t have a “bonus info” post for this one, as most of what I learned went into the piece. But if you don’t know what the Sloan Foundation does, take a look! I hadn’t known they funded Jupyter notebooks and Hidden Figures, or that they introduced Kahneman and Tversky.

In Scientific American, With a Piece on Vacuum Decay

Leave a reply

I had a piece in Scientific American last week. It’s paywalled, but if you’re a subscriber there you can see it, or you can buy the print magazine.

(I also had two pieces out in other outlets this week. I’ll be saying more about them…in a couple weeks.)

The Scientific American piece is about an apocalyptic particle physics scenario called vacuum decay. It’s a topic I covered last year in Quanta Magazine, an unlikely event where the Higgs field which gives fundamental particles their mass changes value, suddenly making all other particles much more massive and changing physics as we know it. It’s a change that physicists think would start as a small bubble and spread at (almost) the speed of light, covering the universe.

What I wrote for Quanta was a short news piece covering a small adjustment to the calculation, one that made the chance of vacuum decay slightly more likely. (But still mind-bogglingly small, to be clear.)

Scientific American asked for a longer piece, and that gave me space to dig deeper. I was able to say more about how vacuum decay works, with a few metaphors that I think should make it a lot easier to understand. I also got to learn about some new developments, in particular, an interesting story about how tiny primordial black holes could make vacuum decay dramatically more likely.

One thing that was a bit too complicated to talk about were the puzzles involved in trying to calculate these chances. In the article, I mention a calculation of the chance of vacuum decay by a team including Matthew Schwartz. That calculation wasn’t the first to estimate the chance of vacuum decay, and it’s not the most recent update either. Instead, I picked it because Schwartz’s team approached the question in what struck me as a more reliable way, trying to cut through confusion by asking the most basic question you can in a quantum theory: given that now you observe X, what’s the chance that later you observe Y? Figuring out how to turn vacuum decay into that kind of question correctly is tricky (for example, you need to include the possibility that vacuum decay happens, then reverses, then happens again).

The calculations of black holes speeding things up didn’t work things out in quite as much detail. I like to think I’ve made a small contribution by motivating them to look at Schwartz’s work, which might spawn a more rigorous calculation in future. When I talked to Schwartz, he wasn’t even sure whether the picture of a bubble forming in one place and spreading at light speed is correct: he’d calculated the chance of the initial decay, but hadn’t found a similarly rigorous way to think about the aftermath. So even more than the uncertainty I talk about in the piece, the questions about new physics and probability, there is even some doubt about whether the whole picture really works the way we’ve been imagining it.

That makes for a murky topic! But it’s also a flashy one, a compelling story for science fiction and the public imagination, and yeah, another motivation to get high-precision measurements of the Higgs and top quark from future colliders! (If maybe not quite the way this guy said it.)

Experiments Should Be Surprising, but Not Too Surprising

1 Reply

People are talking about colliders again.

This year, the European particle physics community is updating its shared plan for the future, the European Strategy for Particle Physics. A raft of proposals at the end of March stirred up a tail of public debate, focused on asking what sort of new particle collider should be built, and discussing potential reasons why.

That discussion, in turn, has got me thinking about experiments, and how they’re justified.

The purpose of experiments, and of science in general, is to learn something new. The more sure we are of something, the less reason there is to test it. Scientists don’t check whether the Sun rises every day. Like everyone else, they assume it will rise, and use that knowledge to learn other things.

You want your experiment to surprise you. But to design an experiment to surprise you, you run into a contradiction.

Suppose that every morning, you check whether the Sun rises. If it doesn’t, you will really be surprised! You’ll have made the discovery of the century! That’s a really exciting payoff, grant agencies should be lining up to pay for…

Well, is that actually likely to happen, though?

The same reasons it would be surprising if the Sun stopped rising are reasons why we shouldn’t expect the Sun to stop rising. A sunrise-checking observatory has incredibly high potential scientific reward…but an absurdly low chance of giving that reward.

Ok, so you can re-frame your experiment. You’re not hoping the Sun won’t rise, you’re observing the sunrise. You expect it to rise, almost guaranteed, so your experiment has an almost guaranteed payoff.

But what a small payoff! You saw exactly what you expected, there’s no science in that!

By either criterion, the “does the Sun rise” observatory is a stupid experiment. Real experiments operate in between the two extremes. They also mix motivations. Together, that leads to some interesting tensions.

What was the purpose of the Large Hadron Collider?

There were a few things physicists were pretty sure of, when they planned the LHC. Previous colliders had measured W bosons and Z bosons, and their properties made it clear that something was missing. If you could collide protons with enough energy, physicists were pretty sure you’d see the missing piece. Physicists had a reasonably plausible story for that missing piece, in the form of the Higgs boson. So physicists could be pretty sure they’d see something, and reasonably sure it would be the Higgs boson.

If physicists expected the Higgs boson, what was the point of the experiment?

First, physicists expected to see the Higgs boson, but they didn’t expect it to have the mass that it did. In fact, they didn’t know anything about the particle’s mass, besides that it should be low enough that the collider could produce it, and high enough that it hadn’t been detected before. The specific number? That was a surprise, and an almost-inevitable one. A rare creature, an almost-guaranteed scientific payoff.

I say almost, because there was a second point. The Higgs boson didn’t have to be there. In fact, it didn’t have to exist at all. There was a much bigger potential payoff, of noticing something very strange, something much more complicated than the straightforward theory most physicists had expected.

(Many people also argued for another almost-guaranteed payoff, and that got a lot more press. People talked about finding the origin of dark matter by discovering supersymmetric particles, which they argued was almost guaranteed due to a principle called naturalness. This is very important for understanding the history…but it’s an argument that many people feel has failed, and that isn’t showing up much anymore. So for this post, I’ll leave it to the side.)

This mix, of a guaranteed small surprise and the potential for a very large surprise, was a big part of what made the LHC make sense. The mix has changed a bit for people considering a new collider, and it’s making for a rougher conversation.

Like the LHC, most of the new collider proposals have a guaranteed payoff. The LHC could measure the mass of the Higgs, these new colliders will measure its “couplings”: how strongly it influences other particles and forces.

Unlike the LHC, though, this guarantee is not a guaranteed surprise. Before building the LHC, we did not know the mass of the Higgs, and we could not predict it. On the other hand, now we absolutely can predict the couplings of the Higgs. We have quite precise numbers, our expectation for what they should be based on a theory that so far has proven quite successful.

We aren’t certain, of course, just like physicists weren’t certain before. The Higgs boson might have many surprising properties, things that contradict our current best theory and usher in something new. These surprises could genuinely tell us something about some of the big questions, from the nature of dark matter to the universe’s balance of matter and antimatter to the stability of the laws of physics.

But of course, they also might not. We no longer have that rare creature, a guaranteed mild surprise, to hedge in case the big surprises fail. We have guaranteed observations, and experimenters will happily tell you about them…but no guaranteed surprises.

That’s a strange position to be in. And I’m not sure physicists have figured out what to do about it.

Antimatter Isn’t Magic

4 Replies

You’ve heard of antimatter, right?

For each type of particle, there is a rare kind of evil twin with the opposite charge, called an anti-particle. When an anti-proton meets a proton, they annihilate each other in a giant blast of energy.

I see a lot of questions online about antimatter. One recurring theme is people asking a very general question: how does antimatter work?

If you’ve just heard the pop physics explanation, antimatter probably sounds like magic. What about antimatter lets it destroy normal matter? Does it need to touch? How long does it take? And what about neutral particles like neutrons?

You find surprisingly few good explanations of this online, but I can explain why. Physicists like me don’t expect antimatter to be confusing in this way, because to us, antimatter isn’t doing anything all that special. When a particle and an antiparticle annihilate, they’re doing the same thing that any other pair of particles do when they do…basically anything else.

Instead of matter and antimatter, let’s talk about one of the oldest pieces of evidence for quantum mechanics, the photoelectric effect. Scientists shone light at a metal, and found that if the wavelength of the light was short enough, electrons would spring free, causing an electric current. If the wavelength was too long, the metal wouldn’t emit any electrons, no matter how much light they shone. Einstein won his Nobel prize for the explanation: the light hitting the metal comes in particle-sized pieces, called photons, whose energy is determined by the wavelength of the light. If the individual photons don’t have enough energy to get an electron to leave the metal, then no electron will move, no matter how many photons you use.

What happens to the photons after they hit the metal?

They go away. We say they are absorbed, an electron absorbs a photon and speeds up, increasing its kinetic energy so it can escape.

But we could just as easily say the photon is annihilated, if we wanted to.

In the photoelectric effect, you start with one electron and one photon, they come together, and you end up with one electron and no photon. In proton-antiproton annihilation, you start with a proton and an antiproton, they come together, and you end up with no protons or antiprotons, but instead “energy”…which in practice, usually means two photons.

That’s all that happens, deep down at the root of things. The laws of physics are rules about inputs and outputs. Start with these particles, they come together, you end up with these other particles. Sometimes one of the particles stays the same. Sometimes particles seem to transform, and different kinds of particles show up. Sometimes some of the particles are photons, and you think of them as “just energy”, and easy to absorb. But particles are particles, and nothing is “just energy”. Each thing, absorption, decay, annihilation, each one is just another type of what we call interactions.

What makes annihilation of matter and antimatter seem unique comes down to charges. Interactions have to obey the laws of physics: they conserve energy, they conserve momentum, and they conserve charge.

So why can an antiproton and a proton annihilate to pure photons, while two protons can’t? A proton and an antiproton have opposite charge, a photon has zero charge. You could combine two protons to make something else, but it would have to have the same charge as two protons.

What about neutrons? A neutron has no electric charge, so you might think it wouldn’t need antimatter. But a neutron has another type of charge, called baryon number. In order to annihilate one, you’d need an anti-neutron, which would still have zero electric charge but would have the opposite baryon number. (By the way, physicists have been making anti-neutrons since 1956.)

On the other hand, photons actually have no charge. So do Higgs bosons. So one Higgs boson can become two photons, without annihilating with anything else. Each of these particles can be called its own antiparticle: a photon is also an antiphoton, a Higgs is also an anti-Higgs.

Because particle-antiparticle annihilation follows the same rules as other interactions between particles, it also takes place via the same forces. When a proton and an antiproton annihilate each other, they typically do this via the electromagnetic force. This is why you end up with light, which is an electromagnetic wave. Like everything in the quantum world, this annihilation isn’t certain. Is has a chance to happen, proportional to the strength of the interaction force involved.

What about neutrinos? They also appear to have a kind of charge, called lepton number. That might not really be a conserved charge, and neutrinos might be their own antiparticles, like photons. However, they are much less likely to be annihilated than protons and antiprotons, because they don’t have electric charge, and thus their interaction doesn’t depend on the electromagnetic force, but on the much weaker weak nuclear force. A weaker force means a less likely interaction.

Antimatter might seem like the stuff of science fiction. But it’s not really harder to understand than anything else in particle physics.

(I know, that’s a low bar!)

It’s just interactions. Particles go in, particles go out. If it follows the rules, it can happen, if it doesn’t, it can’t. Antimatter is no different.

Lambda-CDM Is Not Like the Standard Model

3 Replies

A statistician will tell you that all models are wrong, but some are useful.

Particle physicists have an enormously successful model called the Standard Model, which describes the world in terms of seventeen quantum fields, giving rise to particles from the familiar electron to the challenging-to-measure Higgs boson. The model has nineteen parameters, numbers that aren’t predicted by the model itself but must be found by doing experiments and finding the best statistical fit. With those numbers as input, the model is extremely accurate, aside from the occasional weird discrepancy.

Cosmologists have their own very successful standard model that they use to model the universe as a whole. Called ΛCDM, it describes the universe in terms of three things: dark energy, denoted with a capital lambda (Λ), cold dark matter (CDM), and ordinary matter, all interacting with each other via gravity. The model has six parameters, which must be found by observing the universe and finding the best statistical fit. When those numbers are input, the model is extremely accurate, though there have recently been some high-profile discrepancies.

These sound pretty similar. You model the world as a list of things, fix your parameters based on nature, and make predictions. Wikipedia has a nice graphic depicting the quantum fields of the Standard Model, and you could imagine a similar graphic for ΛCDM.

A graphic like that would be misleading, though.

ΛCDM doesn’t just propose a list of fields and let them interact freely. Instead, it tries to model the universe as a whole, which means it carries assumptions about how matter and energy are distributed, and how space-time is shaped. Some of this is controlled by its parameters, and by tweaking them one can model a universe that varies in different ways. But other assumptions are baked in. If the universe had a very different shape, caused by a very different distribution of matter and energy, then we would need a very different model to represent it. We couldn’t use ΛCDM.

The Standard Model isn’t like that. If you collide two protons together, you need a model of how quarks are distributed inside protons. But that model isn’t the Standard Model, it’s a separate model used for that particular type of experiment. The Standard Model is supposed to be the big picture, the stuff that exists and affects every experiment you can do.

That means the Standard Model is supported in a way that ΛCDM isn’t. The Standard Model describes many different experiments, and is supported by almost all of them. When an experiment disagrees, it has specific implications for part of the model only. For example, neutrinos have mass, which was not predicted in the Standard Model, but it proved easy for people to modify the model to fit. We know the Standard Model is not the full picture, but we also know that any deviations from it must be very small. Large deviations would contradict other experiments, or more basic principles like probabilities needing to be smaller than one.

In contrast, ΛCDM is really just supported by one experiment. We have one universe to observe. We can gather a lot of data, measuring it from its early history to the recent past. But we can’t run it over and over again under different conditions, and our many measurements are all measuring different aspects of the same thing. That’s why unlike in the Standard Model, we can’t separate out assumptions about the shape of the universe from assumptions about what it contains. Dark energy and dark matter are on the same footing as distribution of fluctuations and homogeneity and all those shape-related words, part of one model that gets fit together as a whole.

And so while both the Standard Model and ΛCDM are successful, that success means something different. It’s hard to imagine that we find new evidence and discover that electrons don’t exist, or quarks don’t exist. But we may well find out that dark energy doesn’t exist, or that the universe has a radically different shape. The statistical success of ΛCDM is impressive, and it means any alternative has a high bar to clear. But it doesn’t have to mean rethinking everything the way an alternative to the Standard Model would.

Hot Things Are Less Useful

Leave a reply

Did you know that particle colliders have to cool down their particle beams before they collide?

You might have learned in school that temperature is secretly energy. With a number called Boltzmann’s constant, you can convert a temperature of a gas in Kelvin to the average energy of a molecule in the gas. If that’s what you remember about temperature, it might seem weird that someone would cool down the particles in a particle collider. The whole point of a particle collider is to accelerate particles, giving them lots of energy, before colliding them together. Since those particles have a lot of energy, they must be very hot, right?

Well, no. Here’s the thing: temperature is not just the average energy. It’s the average random energy. It’s energy that might be used to make a particle move forward or backwards, up or down, a random different motion for each particle. It doesn’t include motion that’s the same for each particle, like the movement of a particle beam.

Cooling down a particle beam then, doesn’t mean slowing it down. Rather, it means making it more consistent, getting the different particles moving in the same direction rather than randomly spreading apart. You want the particles to go somewhere specific, speeding up and slamming into the other beam. You don’t want them to move randomly, running into the walls and destroying your collider. So you can have something with high energy that is comparatively cool.

In general, the best way I’ve found to think about temperature and heat is in terms of usefulness and uselessness. Cool things are useful, they do what you expect and not much more. Hot things are less useful, they use energy to do random things you don’t want. Sometimes, by chance, this random energy will still do something useful, and if you have a cold thing to pair with the hot thing, you can take advantage of this in a consistent way. But hot things by themselves are less useful, and that’s why particle colliders try to cool down their beams.

Cool Asteroid News

Leave a reply

Did you hear about the asteroid?

Which one?

You might have heard that an asteroid named 2024 YR4 is going to come unusually close to the Earth in 2032. When it first made the news, astronomers estimated a non-negligible chance of it hitting us: about three percent. That’s small enough that they didn’t expect it to happen, but large enough to plan around it: people invest in startups with a smaller chance of succeeding. Still, people were fairly calm about this one, and there are a couple of good reasons:

  • First, this isn’t a “kill the dinosaurs” asteroid, it’s much smaller. This is a “Tunguska Event” asteroid. Still pretty bad if it happens near a populated area, but not the end of life as we know it.
  • We know about it far in advance, and space agencies have successfully deflected an asteroid before, for a test. If it did pose a risk, it’s quite likely they’d be able to change its path so it misses the Earth instead.
  • It’s tempting to think of that 3% chance as like a roll of a hundred-sided die: the asteroid is on a random path, roll 1 to 3 and it will hit the Earth, roll higher and it won’t, and nothing we do will change that. In reality, though, that 3% was a measure of our ignorance. As astronomers measure the asteroid more thoroughly, they’ll know more and more about its path, and each time they figure something out, they’ll update the number.

And indeed, the number has been updated. In just the last few weeks, the estimated probability of impact has dropped from 3% to a few thousandths of a percent, as more precise observations clarified the asteroid’s path. There’s still a non-negligible chance it will hit the moon (about two percent at the moment), but it’s far too small to do more than make a big flashy crater.

It’s kind of fun to think that there are people out there who systematically track these things, with a plan to deal with them. It feels like something out of a sci-fi novel.

But I find the other asteroid more fun.

In 2020, a probe sent by NASA visited an asteroid named Bennu, taking samples which it carefully packaged and brought back to Earth. Now, scientists have analyzed the samples, revealing several moderately complex chemicals that have an important role in life on Earth, like amino acids and the bases that make up RNA and DNA. Interestingly, while on Earth these molecules all have the same “handedness“, the molecules on Bennu are divided about 50/50. Something similar was seen on samples retrieved from another asteroid, so this reinforces the idea that amino acids and nucleotide bases in space do not have a preferred handedness.

I first got into physics for the big deep puzzles, the ones that figure into our collective creation story. Where did the universe come from? Why are its laws the way they are? Over the ten years since I got my PhD, it’s felt like the answers to these questions have gotten further and further away, with new results serving mostly to rule out possible explanations with greater and greater precision.

Biochemistry has its own deep puzzles figuring into our collective creation story, and the biggest one is abiogenesis: how life formed from non-life. What excites me about these observations from Bennu is that it represents real ongoing progress on that puzzle. By glimpsing a soup of ambidextrous molecules, Bennu tells us something about how our own molecules’ handedness could have developed, and rules out ways that it couldn’t have. In physics, if we could see an era of the universe when there were equal amounts of matter and antimatter, we’d be ecstatic: it would confirm that the imbalance between matter and antimatter is a real mystery, and show us where we need to look for the answer. I love that researchers on the origins of life have reason right now to be similarly excited.

Some FAQ for Microsoft’s Majorana 1 Chip

2 Replies

Recently, Microsoft announced a fancy new quantum computing chip called Majorana 1. I’ve noticed quite a bit of confusion about what they actually announced, and while there’s a great FAQ page about it on the quantum computing blog Shtetl Optimized, the post there aims at a higher level, assuming you already know the basics. You can think of this post as a complement to that one, that tries to cover some basic things Shtetl Optimized took for granted.

Q: In the announcement, Microsoft said:

“It leverages the world’s first topoconductor, a breakthrough type of material which can observe and control Majorana particles to produce more reliable and scalable qubits, which are the building blocks for quantum computers.”

That sounds wild! Are they really using particles in a computer?

A: All computers use particles. Electrons are particles!

Q: You know what I mean!

A: You’re asking if these are “particle physics” particles, like the weird types they try to observe at the LHC?

No, they’re not.

Particle physicists use a mathematical framework called quantum field theory, where particles are ripples in things called quantum fields that describe properties of the universe. But they aren’t the only people to use that framework. Instead of studying properties of the universe you can study properties of materials, weird alloys and layers of metal and crystal that do weird and useful things. The properties of these materials can be approximately described with the same math, with quantum fields. Just as the properties of the universe ripple to produce particles, these properties of materials ripple to produce what are called quasiparticles. Ultimately, these quasiparticles come down to movements of ordinary matter, usually electrons in the original material. They’re just described with a kind of math that makes them look like their own particles.

Q: So, what are these Majorana particles supposed to be?

A: In quantum field theory, most particles come with an antimatter partner. Electrons, for example, have partners called positrons, with a positive electric charge instead of a negative one. These antimatter partners have to exist due to the math of quantum field theory, but there is a way out: some particles are their own antimatter partner, letting one particle cover both roles. This happens for some “particle physics particles”, but all the examples we’ve found are a type of particle called a “boson”, particles related to forces. In 1937, the physicist Ettore Majorana figured out the math you would need to make a particle like this that was a fermion instead, the other main type of particle that includes electrons and protons. So far, we haven’t found one of these Majorana fermions in nature, though some people think the elusive neutrino particles could be an example. Others, though, have tried instead to find a material described by Majorana’s theory. This should in principle be easier, you can build a lot of different materials after all. But it’s proven quite hard for people to do. Back in 2018, Microsoft claimed they’d managed this, but had to retract the claim. This time, they seem more confident, though the scientific community is still not convinced.

Q: And what’s this topoconductor they’re talking about?

A: Topoconductor is short for topological superconductor. Superconductors are materials that conduct electricity much better than ordinary metals.

Q: And, topological means? Something about donuts, right?

A: If you’ve heard anything about topology, you’ve heard that it’s a type of mathematics where donuts are equivalent to coffee cups. You might have seen an animation of a coffee cup being squished and mushed around until the ring of the handle becomes the ring of a donut.

This isn’t actually the important part of topology. The important part is that, in topology, a ball is not equivalent to a donut.

Topology is the study of which things can change smoothly into one another. If you want to change a donut into a ball, you have to slice through the donut’s ring or break the surface inside. You can’t smoothly change one to another. Topologists study shapes of different kinds of things, figuring out which ones can be changed into each other smoothly and which can’t.

Q: What does any of that have to do with quantum computers?

A: The shapes topologists study aren’t always as simple as donuts and coffee cups. They can also study the shape of quantum fields, figuring out which types of quantum fields can change smoothly into each other and which can’t.

The idea of topological quantum computation is to use those rules about what can change into each other to encode information. You can imagine a ball encoding zero, and a donut encoding one. A coffee cup would then also encode one, because it can change smoothly into a donut, while a box would encode zero because you can squash the corners to make it a ball. This helps, because it means that you don’t screw up your information by making smooth changes. If you accidentally drop your box that encodes zero and squish a corner, it will still encode zero.

This matters in quantum computing because it is very easy to screw up quantum information. Quantum computers are very delicate, and making them work reliably has been immensely challenging, requiring people to build much bigger quantum computers so they can do each calculation with many redundant backups. The hope is that topological superconductors would make this easier, by encoding information in a way that is hard to accidentally change.

Q: Cool. So does that mean Microsoft has the best quantum computer now?

A: The machine Microsoft just announced has only a single qubit, the quantum equivalent of just a single bit of computer memory. At this point, it can’t do any calculations. It can just be read, giving one or zero. The hope is that the power of the new method will let Microsoft catch up with companies that have computers with hundred of qubits, and help them arrive faster at the millions of qubits that will be needed to do anything useful.

Q: Ah, ok. But it sounds like they accomplished some crazy Majorana stuff at least, right?

A: Umm…

Read the Shtetl-Optimized FAQ if you want more details. The short answer is that this is still controversial. So far, the evidence they’ve made public isn’t enough to show that they found these Majorana quasiparticles, or that they made a topological superconductor. They say they have more recent evidence that they haven’t published yet. We’ll see.

Bonus Material for “How Hans Bethe Stumbled Upon Perfect Quantum Theories”

Leave a reply

I had an article last week in Quanta Magazine. It’s a piece about something called the Bethe ansatz, a method in mathematical physics that was discovered by Hans Bethe in the 1930’s, but which only really started being understood and appreciated around the 1960’s. Since then it’s become a key tool, used in theoretical investigations in areas from condensed matter to quantum gravity. In this post, I thought I’d say a bit about the story behind the piece and give some bonus material that didn’t fit.

When I first decided to do the piece I reached out to Jules Lamers. We were briefly office-mates when I worked in France, where he was giving a short course on the Bethe ansatz and the methods that sprung from it. It turned out he had also been thinking about writing a piece on the subject, and we considered co-writing for a bit, but that didn’t work for Quanta. He helped me a huge amount with understanding the history of the subject and tracking down the right sources. If you’re a physicist who wants to learn about these things, I recommend his lecture notes. And if you’re a non-physicist who wants to know more, I hope he gets a chance to write a longer popular-audience piece on the topic!

If you clicked through to Jules’s lecture notes, you’d see the word “Bethe ansatz” doesn’t appear in the title. Instead, you’d see the phrase “quantum integrability”. In classical physics, an “integrable” system is one where you can calculate what will happen by doing an integral, essentially letting you “solve” any problem completely. Systems you can describe with the Bethe ansatz are solvable in a more complicated quantum sense, so they get called “quantum integrable”. There’s a whole research field that studies these quantum integrable systems.

My piece ended up rushing through the history of the field. After talking about Bethe’s original discovery, I jumped ahead to ice. The Bethe ansatz was first used to think about ice in the 1960’s, but the developments I mentioned leading up to it, where experimenters noticed extra variability and theorists explained it with the positions of hydrogen atoms, happened earlier, in the 1930’s. (Thanks to the commenter who pointed out that this was confusing!) Baxter gets a starring role in this section and had an important role in tying things together, but other people (Lieb and Sutherland) were involved earlier, showing that the Bethe ansatz indeed could be used with thin sheets of ice. This era had a bunch of other big names that I didn’t have space to talk about: C. N. Yang makes an appearance, and while Faddeev comes up later, I didn’t mention that he had a starring role in the 1970’s in understanding the connection to classical integrability and proposing a mathematical structure to understand what links all these different integrable theories together.

I vaguely gestured at black holes and quantum gravity, but didn’t have space for more than that. The connection there is to a topic you might have heard of before if you’ve read about string theory, called AdS/CFT, a connection between two kinds of world that are secretly the same: a toy model of gravity called Anti-de Sitter space (AdS) and a theory without gravity that looks the same at any scale (called a Conformal Field Theory, or CFT). It turns out that in the most prominent example of this, the theory without gravity is integrable! In fact, it’s a theory I spent a lot of time working with back in my research days, called N=4 super Yang-Mills. This theory is kind of like QCD, and in some sense it has integrability for similar reasons to those that Feynman hoped for and Korchemsky and Faddeev found. But it actually goes much farther, outside of the high-energy approximation where Korchemsky and Faddeev’s result works, and in principle seems to include everything you might want to know about the theory. Nowadays, people are using it to investigate the toy model of quantum gravity, hoping to get insights about quantum gravity in general.

One thing I didn’t get a chance to mention at all is the connection to quantum computing. People are trying to build a quantum computer with carefully-cooled atoms. It’s important to test whether the quantum computer functions well enough, or if the quantum states aren’t as perfect as they need to be. One way people have been testing this is with the Bethe ansatz: because it lets you calculate the behavior of special systems perfectly, you can set up your quantum computer to model a Bethe ansatz, and then check how close to the prediction your results are. You know that the theoretical result is complete, so any failure has to be due to an imperfection in your experiment.

I gave a quick teaser to a very active field, one that has fascinated a lot of prominent physicists and been applied in a wide variety of areas. I hope I’ve inspired you to learn more!