Tag Archives: particle physics

(Not At) Amplitudes 2024 at the IAS

For over a decade, I studied scattering amplitudes, the formulas particle physicists use to find the probability that particles collide, or scatter, in different ways. I went to Amplitudes, the field’s big yearly conference, every year from 2015 to 2023.

This year is different. I’m on the way out of the field, looking for my next steps. Meanwhile, Amplitudes 2024 is going full speed ahead at the Institute for Advanced Study in Princeton.

With poster art that is, as the kids probably don’t say anymore, “on fleek”

The talks aren’t live-streamed this year, but they are posting slides, and they will be posting recordings. Since a few of my readers are interested in new amplitudes developments, I’ve been paging through the posted slides looking for interesting highlights. So far, I’ve only seen slides from the first few days: I will probably write about the later talks in a future post.

Each day of Amplitudes this year has two 45-minute “review talks”, one first thing in the morning and the other first thing after lunch. I put “review talks” in quotes because they vary a lot, between talks that try to introduce a topic for the rest of the conference to talks that mostly focus on the speaker’s own research. Lorenzo Tancredi’s talk was of the former type, an introduction to the many steps that go into making predictions for the LHC, with a focus on those topics where amplitudeologists have made progress. The talk opens with the type of motivation I’d been writing in grant and job applications over the last few years (we don’t know most of the properties of the Higgs yet! To measure them, we’ll need to calculate amplitudes with massive particles to high precision!), before moving into a review of the challenges and approaches in different steps of these calculations. While Tancredi apologizes in advance that the talk may be biased, I found it surprisingly complete: if you want to get an idea of the current state of the “LHC amplitudes pipeline”, his slides are a good place to start.

Tancredi’s talk serves as introduction for a variety of LHC-focused talks, some later that day and some later in the week. Federica Devoto discussed high-energy quarks while Chiara Signorile-Signorile and George Sterman showed advances in handling of low-energy particles. Xiaofeng Xu has a program that helps predict symbol letters, the building-blocks of scattering amplitudes that can be used to reconstruct or build up the whole thing, while Samuel Abreu talked about a tricky state-of-the-art case where Xu’s program misses part of the answer.

Later Monday morning veered away from the LHC to focus on more toy-model theories. Renata Kallosh’s talk in particular caught my attention. This blog is named after a long-standing question in amplitudes: will the four-graviton amplitude in N=8 supergravity diverge at seven loops in four dimensions? This seemingly arcane question is deep down a question about what is actually required for a successful theory of quantum gravity, and in particular whether some of the virtues of string theory can be captured by a simpler theory instead. Answering the question requires a prodigious calculation, and the more “loops” are involved the more difficult it is. Six years ago, the calculation got to five loops, and it hasn’t passed that mark since then. That five-loop calculation gave some reason for pessimism, a nice pattern at lower loops that stopped applying at five.

Kallosh thinks she has an idea of what to expect. She’s noticed a symmetry in supergravity, one that hadn’t previously been taken into account. She thinks that symmetry should keep N=8 supergravity from diverging on schedule…but only in exactly four dimensions. All of the lower-loop calculations in N=8 supergravity diverged in higher dimensions than four, and it seems like with this new symmetry she understands why. Her suggestion is to focus on other four-dimensional calculations. If seven loops is still too hard, then dialing back the amount of supersymmetry from N=8 to something lower should let her confirm her suspicions. Already a while back N=5 supergravity was found to diverge later than expected in four dimensions. She wants to know whether that pattern continues.

(Her backup slides also have a fun historical point: in dimensions greater than four, you can’t get elliptical planetary orbits. So four dimensions is special for our style of life.)

Other talks on Monday included a talk by Zahra Zahraee on progress towards “solving” the field’s favorite toy model, N=4 super Yang-Mills. Christian Copetti talked about the work I mentioned here, while Meta employee François Charlton’s “review talk” dealt with his work applying machine learning techniques to “translate” between questions in mathematics and their answers. In particular, he reported progress with my current boss Matthias Wilhelm and frequent collaborator and mentor Lance Dixon on using transformers to guess high-loop formulas in N=4 super Yang-Mills. They have an interesting proof of principle now, but it will probably still be a while until they can use the method to predict something beyond the state of the art.

In the meantime at least they have some hilarious AI-generated images

Tuesday’s review by Ian Moult was genuinely a review, but of a topic not otherwise covered at the conference, that of “detector observables”. The idea is that rather than talking about which individual particles are detected, one can ask questions that make more sense in terms of the experimental setup, like asking about the amounts of energy deposited in different detectors. This type of story has gone from an idle observation by theorists to a full research program, with theorists and experimentalists in active dialogue.

Natalia Toro brought up that, while we say each particle has a definite spin, that may not actually be the case. Particles with so-called “continuous spins” can masquerade as particles with a definite integer spin at lower energies. Toro and Schuster promoted this view of particles ten years ago, but now can make a bit more sense of it, including understanding how continuous-spin particles can interact.

The rest of Tuesday continued to be a bit of a grab-bag. Yael Shadmi talked about applying amplitudes techniques to Effective Field Theory calculations, while Franziska Porkert talked about a Feynman diagram involving two different elliptic curves. Interestingly (well, to me at least), the curves never appear “together”, you can represent the diagram as a sum of terms involving one curve and terms involving the other, much simpler than it could have been!

Tuesday afternoon’s review talk by Iain Stewart was one of those “guest from an adjacent field” talks, in this case from an approach called SCET, and at first glance didn’t seem to do much to reach out to the non-SCET people in the audience. Frequent past collaborator of mine Andrew McLeod showed off a new set of relations between singularities of amplitudes, found by digging in to the structure of the equations discovered by Landau that control this behavior. He and his collaborators are proposing a new way to keep track of these things involving “minimal cuts”, a clear pun on the “maximal cuts” that have been of great use to other parts of the community. Whether this has more or less staying power than “negative geometries” remains to be seen.

Closing Tuesday, Shruti Paranjape showed there was more to discover about the simplest amplitudes, called “tree amplitudes”. By asking why these amplitudes are sometimes equal to zero, she was able to draw a connection to the “double-copy” structure that links the theory of the strong force and the theory of gravity. Johannes Henn’s talk noticed an intriguing pattern. A while back, I had looked into under which circumstances amplitudes were positive. Henn found that “positive” is an understatement. In a certain region, the amplitudes we were looking at turn out to not just be positive, but also always decreasing, and also with second derivative always positive. In fact, the derivatives appear to alternate, always with one sign or the other as one takes more derivatives. Henn is calling this unusual property “completely monotonous”, and trying to figure out how widely it holds.

Wednesday had a more mathematical theme. Bernd Sturmfels began with a “review talk” that largely focused on his own work on the space of curves with marked points, including a surprising analogy between amplitudes and the likelihood functions one needs to minimize in machine learning. Lauren Williams was the other “actual mathematician” of the day, and covered her work on various topics related to the amplituhedron.

The remaining talks on Wednesday were not literally by mathematicians, but were “mathematically informed”. Carolina Figueiredo and Hayden Lee talked about work with Nima Arkani-Hamed on different projects. Figueiredo’s talk covered recent developments in the “curve integral formalism”, a recent step in Nima’s quest to geometrize everything in sight, this time in the context of more realistic theories. The talk, which like those Nima gives used tablet-written slides, described new insights one can gain from this picture, including new pictures of how more complicated amplitudes can be built up of simpler ones. If you want to understand the curve integral formalism further, I’d actually suggest instead looking at Mark Spradlin’s slides from later that day. The second part of Spradlin’s talk dealt with an area Figueiredo marked for future research, including fermions in the curve integral picture. I confess I’m still not entirely sure what the curve integral formalism is good for, but Spradlin’s talk gave me a better idea of what it’s doing. (The first part of his talk was on a different topic, exploring the space of string-like amplitudes to figure out which ones are actually consistent.)

Hayden Lee’s talk mentions the emergence of time, but the actual story is a bit more technical. Lee and collaborators are looking at cosmological correlators, observables like scattering amplitudes but for cosmology. Evaluating these is challenging with standard techniques, but can be approached with some novel diagram-based rules which let the results be described in terms of the measurable quantities at the end in a kind of “amplituhedron-esque” way.

Aidan Herderschee and Mariana Carrillo González had talks on Wednesday on ways of dealing with curved space. Herderschee talked about how various amplitudes techniques need to be changed to deal with amplitudes in anti-de-Sitter space, with difference equations replacing differential equations and sum-by-parts relations replacing integration-by-parts relations. Carrillo González looked at curved space through the lens of a special kind of toy model theory called a self-dual theory, which allowed her to do cosmology-related calculations using a double-copy technique.

Finally, Stephen Sharpe had the second review talk on Wednesday. This was another “outside guest” talk, a discussion from someone who does Lattice QCD about how they have been using their methods to calculate scattering amplitudes. They seem to count the number of particles a bit differently than we do, I’m curious whether this came up in the question session.

At Quanta This Week, and Some Bonus Material

When I moved back to Denmark, I mentioned that I was planning to do more science journalism work. The first fruit of that plan is up this week: I have a piece at Quanta Magazine about a perennially trendy topic in physics, the S-matrix.

It’s been great working with Quanta again. They’ve been thorough, attentive to the science, and patient with my still-uncertain life situation. I’m quite likely to have more pieces there in future, and I’ve got ideas cooking with other outlets as well, so stay tuned!

My piece with Quanta is relatively short, the kind of thing they used to label a “blog” rather than say a “feature”. Since the S-matrix is a pretty broad topic, there were a few things I couldn’t cover there, so I thought it would be nice to discuss them here. You can think of this as a kind of “bonus material” section for the piece. So before reading on, read my piece at Quanta first!

Welcome back!

At Quanta I wrote a kind of cartoon of the S-matrix, asking you to think about it as a matrix of probabilities, with rows for input particles and columns for output particles. There are a couple different simplifications I snuck in there, the pop physicist’s “lies to children“. One, I already flag in the piece: the entries aren’t really probabilities, they’re complex numbers, probability amplitudes.

There’s another simplification that I didn’t have space to flag. The rows and columns aren’t just lists of particles, they’re lists of particles in particular states.

What do I mean by states? A state is a complete description of a particle. A particle’s state includes its energy and momentum, including the direction it’s traveling in. It includes its spin, and the direction of its spin: for example, clockwise or counterclockwise? It also includes any charges, from the familiar electric charge to the color of a quark.

This makes the matrix even bigger than you might have thought. I was already describing an infinite matrix, one where you can have as many columns and rows as you can imagine numbers of colliding particles. But the number of rows and columns isn’t just infinite, but uncountable, as many rows and columns as there are different numbers you can use for energy and momentum.

For some of you, an uncountably infinite matrix doesn’t sound much like a matrix. But for mathematicians familiar with vector spaces, this is totally reasonable. Even if your matrix is infinite, or even uncountably infinite, it can still be useful to think about it as a matrix.

Another subtlety, which I’m sure physicists will be howling at me about: the Higgs boson is not supposed to be in the S-matrix!

In the article, I alluded to the idea that the S-matrix lets you “hide” particles that only exist momentarily inside of a particle collision. The Higgs is precisely that sort of particle, an unstable particle. And normally, the S-matrix is supposed to only describe interactions between stable particles, particles that can survive all the way to infinity.

In my defense, if you want a nice table of probabilities to put in an article, you need an unstable particle: interactions between stable particles depend on their energy and momentum, sometimes in complicated ways, while a single unstable particle will decay into a reliable set of options.

More technically, there are also contexts in which it’s totally fine to think about an S-matrix between unstable particles, even if it’s not usually how we use the idea.

My piece also didn’t have a lot of room to discuss new developments. I thought at minimum I’d say a bit more about the work of the young people I mentioned. You can think of this as an appetizer: there are a lot of people working on different aspects of this subject these days.

Part of the initial inspiration for the piece was when an editor at Quanta noticed a recent paper by Christian Copetti, Lucía Cordova, and Shota Komatsu. The paper shows an interesting case, where one of the “logical” conditions imposed in the original S-matrix bootstrap doesn’t actually apply. It ended up being too technical for the Quanta piece, but I thought I could say a bit about it, and related questions, here.

Some of the conditions imposed by the original bootstrappers seem unavoidable. Quantum mechanics makes no sense if doesn’t compute probabilities, and probabilities can’t be negative, or larger than one, so we’d better have an S-matrix that obeys those rules. Causality is another big one: we probably shouldn’t have an S-matrix that lets us send messages back in time and change the past.

Other conditions came from a mixture of intuition and observation. Crossing is a big one here. Crossing tells you that you can take an S-matrix entry with in-coming particles, and relate it to a different S-matrix entry with out-going anti-particles, using techniques from the calculus of complex numbers.

Crossing may seem quite obscure, but after some experience with S-matrices it feels obvious and intuitive. That’s why for an expert, results like the paper by Copetti, Cordova, and Komatsu seem so surprising. What they found was that a particularly exotic type of symmetry, called a non-invertible symmetry, was incompatible with crossing symmetry. They could find consistent S-matrices for theories with these strange non-invertible symmetries, but only if they threw out one of the basic assumptions of the bootstrap.

This was weird, but upon reflection not too weird. In theories with non-invertible symmetries, the behaviors of different particles are correlated together. One can’t treat far away particles as separate, the way one usually does with the S-matrix. So trying to “cross” a particle from one side of a process to another changes more than it usually would, and you need a more sophisticated approach to keep track of it. When I talked to Cordova and Komatsu, they related this to another concept called soft theorems, aspects of which have been getting a lot of attention and funding of late.

In the meantime, others have been trying to figure out where the crossing rules come from in the first place.

There were attempts in the 1970’s to understand crossing in terms of other fundamental principles. They slowed in part because, as the original S-matrix bootstrap was overtaken by QCD, there was less motivation to do this type of work anymore. But they also ran into a weird puzzle. When they tried to use the rules of crossing more broadly, only some of the things they found looked like S-matrices. Others looked like stranger, meaningless calculations.

A recent paper by Simon Caron-Huot, Mathieu Giroux, Holmfridur Hannesdottir, and Sebastian Mizera revisited these meaningless calculations, and showed that they aren’t so meaningless after all. In particular, some of them match well to the kinds of calculations people wanted to do to predict gravitational waves from colliding black holes.

Imagine a pair of black holes passing close to each other, then scattering away in different directions. Unlike particles in a collider, we have no hope of catching the black holes themselves. They’re big classical objects, and they will continue far away from us. We do catch gravitational waves, emitted from the interaction of the black holes.

This different setup turns out to give the problem a very different character. It ends up meaning that instead of the S-matrix, you want a subtly different mathematical object, one related to the original S-matrix by crossing relations. Using crossing, Caron-Huot, Giroux, Hannesdottir and Mizera found many different quantities one could observe in different situations, linked by the same rules that the original S-matrix bootstrappers used to relate S-matrix entries.

The work of these two groups is just some of the work done in the new S-matrix program, but it’s typical of where the focus is going. People are trying to understand the general rules found in the past. They want to know where they came from, and as a consequence, when they can go wrong. They have a lot to learn from the older papers, and a lot of new insights come from diligent reading. But they also have a lot of new insights to discover, based on the new tools and perspectives of the modern day. For the most part, they don’t expect to find a new unified theory of physics from bootstrapping alone. But by learning how S-matrices work in general, they expect to find valuable knowledge no matter how the future goes.

Getting It Right vs Getting It Done

With all the hype around machine learning, I occasionally get asked if it could be used to make predictions for particle colliders, like the LHC.

Physicists do use machine learning these days, to be clear. There are tricks and heuristics, ways to quickly classify different particle collisions and speed up computation. But if you’re imagining something that replaces particle physics calculations entirely, or even replace the LHC itself, then you’re misunderstanding what particle physics calculations are for.

Why do physicists try to predict the results of particle collisions? Why not just observe what happens?

Physicists make predictions not in order to know what will happen in advance, but to compare those predictions to experimental results. If the predictions match the experiments, that supports existing theories like the Standard Model. If they don’t, then a new theory might be needed.

Those predictions certainly don’t need to be made by humans: most of the calculations are done by computers anyway. And they don’t need to be perfectly accurate: in particle physics, every calculation is an approximation. But the approximations used in particle physics are controlled approximations. Physicists keep track of what assumptions they make, and how they might go wrong. That’s not something you can typically do in machine learning, where you might train a neural network with millions of parameters. The whole point is to be able to check experiments against a known theory, and we can’t do that if we don’t know whether our calculation actually respects the theory.

That difference, between caring about the result and caring about how you got there, is a useful guide. If you want to predict how a protein folds in order to understand what it does in a cell, then you will find AlphaFold useful. If you want to confirm your theory of how protein folding happens, it will be less useful.

Some industries just want the final result, and can benefit from machine learning. If you want to know what your customers will buy, or which suppliers are cheating you, or whether your warehouse is moldy, then machine learning can be really helpful.

Other industries are trying, like particle physicists, to confirm that a theory is true. If you’re running a clinical trial, you want to be crystal clear about how the trial data turn into statistics. You, and the regulators, care about how you got there, not just about what answer you got. The same can be true for banks: if laws tell you you aren’t allowed to discriminate against certain kinds of customers for loans, you need to use a method where you know what traits you’re actually discriminating against.

So will physicists use machine learning? Yes, and more of it over time. But will they use it to replace normal calculations, or replace the LHC? No, that would be missing the point.

The Hidden Higgs

Peter Higgs, the theoretical physicist whose name graces the Higgs boson, died this week.

Peter Higgs, after the Higgs boson discovery was confirmed

This post isn’t an obituary: you can find plenty of those online, and I don’t have anything special to say that others haven’t. Reading the obituaries, you’ll notice they summarize Higgs’s contribution in different ways. Higgs was one of the people who proposed what today is known as the Higgs mechanism, the principle by which most (perhaps all) elementary particles gain their mass. He wasn’t the only one: Robert Brout and François Englert proposed essentially the same idea in a paper that was published two months earlier, in August 1964. Two other teams came up with the idea slightly later than that: Gerald Guralnik, Carl Richard Hagen, and Tom Kibble were published one month after Higgs, while Alexander Migdal and Alexander Polyakov found the idea independently in 1965 but couldn’t get it published till 1966.

Higgs did, however, do something that Brout and Englert didn’t. His paper doesn’t just propose a mechanism, involving a field which gives particles mass. It also proposes a particle one could discover as a result. Read the more detailed obituaries, and you’ll discover that this particle was not in the original paper: Higgs’s paper was rejected at first, and he added the discussion of the particle to make it more interesting.

At this point, I bet some of you are wondering what the big deal was. You’ve heard me say that particles are ripples in quantum fields. So shouldn’t we expect every field to have a particle?

Tell that to the other three Higgs bosons.

Electromagnetism has one type of charge, with two signs: plus, and minus. There are electrons, with negative charge, and their anti-particles, positrons, with positive charge.

Quarks have three types of charge, called colors: red, green, and blue. Each of these also has two “signs”: red and anti-red, green and anti-green, and blue and anti-blue. So for each type of quark (like an up quark), there are six different versions: red, green, and blue, and anti-quarks with anti-red, anti-green, and anti-blue.

Diagram of the colors of quarks

When we talk about quarks, we say that the force under which they are charged, the strong nuclear force, is an “SU(3)” force. The “S” and “U” there are shorthand for mathematical properties that are a bit too complicated to explain here, but the “(3)” is quite simple: it means there are three colors.

The Higgs boson’s primary role is to make the weak nuclear force weak, by making the particles that carry it from place to place massive. (That way, it takes too much energy for them to go anywhere, a feeling I think we can all relate to.) The weak nuclear force is an “SU(2)” force. So there should be two “colors” of particles that interact with the weak nuclear force…which includes Higgs bosons. For each, there should also be an anti-color, just like the quarks had anti-red, anti-green, and anti-blue. So we need two “colors” of Higgs bosons, and two “anti-colors”, for a total of four!

But the Higgs boson discovered at the LHC was a neutral particle. It didn’t have any electric charge, or any color. There was only one, not four. So what happened to the other three Higgs bosons?

The real answer is subtle, one of those physics things that’s tricky to concisely explain. But a partial answer is that they’re indistinguishable from the W and Z bosons.

Normally, the fundamental forces have transverse waves, with two polarizations. Light can wiggle along its path back and forth, or up and down, but it can’t wiggle forward and backward. A fundamental force with massive particles is different, because they can have longitudinal waves: they have an extra direction in which they can wiggle. There are two W bosons (plus and minus) and one Z boson, and they all get one more polarization when they become massive due to the Higgs.

That’s three new ways the W and Z bosons can wiggle. That’s the same number as the number of Higgs bosons that went away, and that’s no coincidence. We physicist like to say that the W and Z bosons “ate” the extra Higgs, which is evocative but may sound mysterious. Instead, you can think of it as the two wiggles being secretly the same, mixing together in a way that makes them impossible to tell apart.

The “count”, of how many wiggles exist, stays the same. You start with four Higgs wiggles, and two wiggles each for the precursors of the W+, W-, and Z bosons, giving ten. You end up with one Higgs wiggle, and three wiggles each for the W+, W-, and Z bosons, which still adds up to ten. But which fields match with which wiggles, and thus which particles we can detect, changes. It takes some thought to look at the whole system and figure out, for each field, what kind of particle you might find.

Higgs did that work. And now, we call it the Higgs boson.

Generalizing a Black Box Theory

In physics and in machine learning, we have different ways of thinking about models.

A model in physics, like the Standard Model, is a tool to make predictions. Using statistics and a whole lot of data (from particle physics experiments), we fix the model’s free parameters (like the mass of the Higgs boson). The model then lets us predict what we’ll see next: when we turn on the Large Hadron Collider, what will the data look like? In physics, when a model works well, we think that model is true, that it describes the real way the world works. The Standard Model isn’t the ultimate truth: we expect that a better model exists that makes better predictions. But it is still true, in an in-between kind of way. There really are Higgs bosons, even if they’re a result of some more mysterious process underneath, just like there really are atoms, even if they’re made out of protons, neutrons, and electrons.

A model in machine learning, like the Large Language Model that fuels ChatGPT, is also a tool to make predictions. Using statistics and a whole lot of data (from text on the internet, or images, or databases of proteins, or games of chess…) we fix the model’s free parameters (called weights, numbers for the strengths of connections between metaphorical neurons). The model then lets us predict what we’ll see next: when a text begins “Q: How do I report a stolen card? A:”, how does it end?

So far, that sounds a lot like physics. But in machine learning, we don’t generally think these models are true, at least not in the same way. The thing producing language isn’t really a neural network like a Large Language Model. It’s the sum of many human brains, many internet users, spread over many different circumstances. Each brain might be sort of like a neural network, but they’re not like the neural networks sitting on OpenAI’s servers. A Large Language Model isn’t true in some in-between kind of way, like atoms or Higgs bosons. It just isn’t true. It’s a black box, a machine that makes predictions, and nothing more.

But here’s the rub: what do we mean by true?

I want to be a pragmatist here. I don’t want to get stuck in a philosophical rabbit-hole, arguing with metaphysicists about what “really exists”. A true theory should be one that makes good predictions, that lets each of us know, based on our actions, what we should expect to see. That’s why science leads to technology, why governments and companies pay people to do it: because the truth lets us know what will happen, and make better choices. So if Large Language Models and the Standard Model both make good predictions, why is only one of them true?

Recently, I saw Dan Elton of More is Different make the point that there is a practical reason to prefer the “true” explanations: they generalize. A Large Language Model might predict what words come next in a text. But it doesn’t predict what happens when you crack someone’s brain open and see how the neurons connect to each other, even if that person is the one who made the text. A good explanation, a true model, can be used elsewhere. The Standard Model tells you what data from the Large Hadron Collider will look like, but it also tells you what data from the muon g-2 experiment will look like. It also, in principle, tells you things far away from particle physics: what stars look like, what atoms look like, what the inside of a nuclear reactor looks like. A black box can’t do that, even if it makes great predictions.

It’s a good point. But thinking about it, I realized things are a little murkier.

You can’t generalize a Large Language Model to tell you how human neurons are connected. But you can generalize it in other ways, and people do. There’s a huge industry in trying to figure out what GPT and its relatives “know”. How much math can they do? How much do they know about geography? Can they predict the future?

These generalizations don’t work the way that they do in physics, or the rest of science, though. When we generalize the Standard Model, we aren’t taking a machine that makes particle physics predictions and trying to see what those particle physics predictions can tell us. We’re taking something “inside” the machine, the fields and particles, and generalizing that, seeing how the things around us could be made of those fields and those particles. In contrast, when people generalize GPT, they typically don’t look inside the “black box”. They use the Large Language Model to make predictions, and see what those predictions “know about”.

On the other hand, we do sometimes generalize scientific models that way too.

If you’re simulating the climate, or a baby star, or a colony of bacteria, you typically aren’t using your simulation like a prediction machine. You don’t plug in exactly what is going on in reality, then ask what happens next. Instead, you run many simulations with different conditions, and look for patterns. You see how a cloud of sulfur might cool down the Earth, or how baby stars often form in groups, leading them to grow up into systems of orbiting black holes. Your simulation is kind of like a black box, one that you try out in different ways until you uncover some explainable principle, something your simulation “knows” that you can generalize.

And isn’t nature that kind of black box, too? When we do an experiment, aren’t we just doing what the Large Language Models are doing, prompting the black box in different ways to get an idea of what it knows? Are scientists who do experiments that picky about finding out what’s “really going on”, or do they just want a model that works?

We want our models to be general, and to be usable. Building a black box can’t be the whole story, because a black box, by itself, isn’t general. But it can certainly be part of the story. Going from the black box of nature to the black box of a machine lets you run tests you couldn’t previously do, lets you investigate faster and ask stranger questions. With a simulation, you can blow up stars. With a Large Language Model, you can ask, for a million social media comments, whether the average internet user would call them positive or negative. And if you make sure to generalize, and try to make better decisions, then it won’t be just the machine learning. You’ll be learning too.

What Are Particles? The Gentle Introduction

On this blog, I write about particle physics for the general public. I try to make things as simple as possible, but I do have to assume some things. In particular, I usually assume you know what particles are!

This time, I won’t do that. I know some people out there don’t know what a particle is, or what particle physicists do. If you’re a person like that, this post is for you! I’m going to give a gentle introduction to what particle physics is all about.

Let’s start with atoms.

Every object and substance around you, everything you can touch or lift or walk on, the water you drink and the air you breathe, all of these are made up of atoms. Some are simple: an iron bar is made of Iron atoms, aluminum foil is mostly Aluminum atoms. Some are made of combinations of atoms into molecules, like water’s famous H2O: each molecule has two Hydrogen atoms and one Oxygen atom. Some are made of more complicated mixtures: air is mostly pairs of Nitrogen atoms, with a healthy amount of pairs of Oxygen, some Carbon Dioxide (CO2), and many other things, while the concrete sidewalks you walk on have Calcium, Silicon, Aluminum, Iron, and Oxygen, all combined in various ways.

There is a dizzying array of different types of atoms, called chemical elements. Most occur in nature, but some are man-made, created by cutting-edge nuclear physics. They can all be organized in the periodic table of elements, which you’ve probably seen on a classroom wall.

The periodic table

The periodic table is called the periodic table because it repeats, periodically. Each element is different, but their properties resemble each other. Oxygen is a gas, Sulfur a yellow powder, Polonium an extremely radioactive metal…but just as you can find H2O, you can make H2S, and even H2Po. The elements get heavier as you go down the table, and more metal-like, but their chemical properties, the kinds of molecules you can make with them, repeat.

Around 1900, physicists started figuring out why the elements repeat. What they discovered is that each atom is made of smaller building-blocks, called sub-atomic particles. (“Sub-atomic” because they’re smaller than atoms!) Each atom has electrons on the outside, and on the inside has a nucleus made of protons and neutrons. Atoms of different elements have different numbers of protons and electrons, which explains their different properties.

Different atoms with different numbers of protons, neutrons, and electrons

Around the same time, other physicists studied electricity, magnetism, and light. These things aren’t made up of atoms, but it was discovered that they are all aspects of the same force, the electromagnetic force. And starting with Einstein, physicists figured out that this force has particles too. A beam of light is made up of another type of sub-atomic particle, called a photon.

For a little while then, it seemed that the universe was beautifully simple. All of matter was made of electrons, protons, and neutrons, while light was made of photons.

(There’s also gravity, of course. That’s more complicated, in this post I’ll leave it out.)

Soon, though, nuclear physicists started noticing stranger things. In the 1930’s, as they tried to understand the physics behind radioactivity and mapped out rays from outer space, they found particles that didn’t fit the recipe. Over the next forty years, theoretical physicists puzzled over their equations, while experimental physicists built machines to slam protons and electrons together, all trying to figure out how they work.

Finally, in the 1970’s, physicists had a theory they thought they could trust. They called this theory the Standard Model. It organized their discoveries, and gave them equations that could predict what future experiments would see.

In the Standard Model, there are two new forces, the weak nuclear force and the strong nuclear force. Just like photons for the electromagnetic force, each of these new forces has a particle. The general word for these particles is bosons, named after Satyendra Nath Bose, a collaborator of Einstein who figured out the right equations for this type of particle. The weak force has bosons called W and Z, while the strong force has bosons called gluons. A final type of boson, called the Higgs boson after a theorist who suggested it, rounds out the picture.

The Standard Model also has new types of matter particles. Neutrinos interact with the weak nuclear force, and are so light and hard to catch that they pass through nearly everything. Quarks are inside protons and neutrons: a proton contains one one down quark and two up quarks, while a neutron contains two down quarks and one up quark. The quarks explained all of the other strange particles found in nuclear physics.

Finally, the Standard Model, like the periodic table, repeats. There are three generations of particles. The first, with electrons, up quarks, down quarks, and one type of neutrino, show up in ordinary matter. The other generations are heavier, and not usually found in nature except in extreme conditions. The second generation has muons (similar to electrons), strange quarks, charm quarks, and a new type of neutrino called a muon-neutrino. The third generation has tauons, bottom quarks, top quarks, and tau-neutrinos.

(You can call these last quarks “truth quarks” and “beauty quarks” instead, if you like.)

Physicists had the equations, but the equations still had some unknowns. They didn’t know how heavy the new particles were, for example. Finding those unknowns took more experiments, over the next forty years. Finally, in 2012, the last unknown was found when a massive machine called the Large Hadron Collider was used to measure the Higgs boson.

The Standard Model

We think that these particles are all elementary particles. Unlike protons and neutrons, which are both made of up quarks and down quarks, we think that the particles of the Standard Model are not made up of anything else, that they really are elementary building-blocks of the universe.

We have the equations, and we’ve found all the unknowns, but there is still more to discover. We haven’t seen everything the Standard Model can do: to see some properties of the particles and check they match, we’d need a new machine, one even bigger than the Large Hadron Collider. We also know that the Standard Model is incomplete. There is at least one new particle, called dark matter, that can’t be any of the known particles. Mysteries involving the neutrinos imply another type of unknown particle. We’re also missing deeper things. There are patterns in the table, like the generations, that we can’t explain.

We don’t know if any one experiment will work, or if any one theory will prove true. So particle physicists keep working, trying to find new tricks and make new discoveries.

Book Review: The Case Against Reality

Nima Arkani-Hamed shows up surprisingly rarely in popular science books. A major figure in my former field, Nima is extremely quotable (frequent examples include “spacetime is doomed” and “the universe is not a crappy metal”), but those quotes don’t seem to quite have reached the popular physics mainstream. He’s been interviewed in books by physicists, and has a major role in one popular physics book that I’m aware of. From this scattering of mentions, I was quite surprised to hear of another book where he makes an appearance: not a popular physics book at all, but a popular psychology book: Donald Hoffman’s The Case Against Reality. Naturally, this meant I had to read it.

Then, I saw the first quote on the back cover…or specifically, who was quoted.

Seeing that, I settled in for a frustrating read.

A few pages later, I realized that this, despite his endorsement, is not a Deepak Chopra kind of book. Hoffman is careful in some valuable ways. Specifically, he has a philosopher’s care, bringing up objections and potential holes in his arguments. As a result, the book wasn’t frustrating in the way I expected.

It was even more frustrating, actually. But in an entirely different way.

When a science professor writes a popular book, the result is often a kind of ungainly Frankenstein. The arguments we want to make tend to be better-suited to shorter pieces, like academic papers, editorials, and blog posts. To make these into a book, we have to pad them out. We stir together all the vaguely related work we’ve done, plus all the best-known examples from other peoples’ work, trying (often not all that hard) to make the whole sound like a cohesive story. Read enough examples, and you start to see the joints between the parts.

Hoffman is ostensibly trying to tell a single story. His argument is that the reality we observe, of objects in space and time, is not the true reality. It is a convenient reality, one that has led to our survival, but evolution has not (and as he argues, cannot) let us perceive the truth. Instead, he argues that the true reality is consciousness: a world made up of conscious beings interacting with each other, with space, time, and all the rest emerging as properties of those interactions.

That certainly sounds like it could be one, cohesive argument. In practice, though, it is three, and they don’t fit together as well as he’d hope.

Hoffman is trained as a psychologist. As such, one of the arguments is psychological: that research shows that we mis-perceive the world in service of evolutionary fitness.

Hoffman is a cognitive scientist, and while many cognitive scientists are trained as psychologists, others are trained as philosophers. As such, one of his arguments is philosophical: that the contents of consciousness can never be explained by relations between material objects, and that evolution, and even science, systematically lead us astray.

Finally, Hoffman has evidently been listening to and reading the work of some physicists, like Nima and Carlo Rovelli. As such, one of his arguments is physical: that physicists believe that space and time are illusions and that consciousness may be fundamental, and that the conclusions of the book lead to his own model of the basic physical constituents of the world.

The book alternates between these three arguments, so rather than in chapter order, I thought it would be better to discuss each argument in its own section.

The Psychological Argument

Sometimes, when two academics get into a debate, they disagree about what’s true. Two scientists might argue about whether an experiment was genuine, whether the statistics back up a conclusion, or whether a speculative theory is actually consistent. These are valuable debates, and worth reading about if you want to learn something about the nature of reality.

Sometimes, though, two debating academics agree on what’s true, and just disagree on what’s important. These debates are, at best, relevant to other academics and funders. They are not generally worth reading for anybody else, and are often extremely petty and dumb.

Hoffman’s psychological argument, regrettably, is of the latter kind. He would like to claim it’s the former, and to do so he marshals a host of quotes from respected scientists that claim that human perception is veridical: that what we perceive is real, courtesy of an evolutionary process that would have killed us off if it wasn’t. From that perspective, every psychological example Hoffman gives is a piece of counter-evidence, a situation where evolution doesn’t just fail to show us the true nature of reality, but actively hides reality from us.

The problem is that, if you actually read the people Hoffman quotes, they’re clearly not making the extreme point he claims. These people are psychologists, and all they are arguing is that perception is veridical in a particular, limited way. They argue that we humans are good at estimating distances or positions of objects, or that we can see a wide range of colors. They aren’t making some sort of philosophical point about those distances or positions or colors being how the world “really is”, nor are they claiming that evolution never makes humans mis-perceive.

Instead, they, and thus Hoffman, are arguing about importance. When studying humans, is it more useful to think of us as perceiving the world as it is? Or is it more useful to think of evolution as tricking us? Which happens more often?

The answers to each of those questions have to be “it depends”. Neither answer can be right all the time. At most then, this kind of argument can convince one academic to switch from researching in one way to researching in another, by saying that right now one approach is a better strategy. It can’t tell us anything more.

If the argument Hoffman is trying to get across here doesn’t matter, are there other reasons to read this part?

Popular psychology books tend to re-use a few common examples. There are some good ones, so if you haven’t read such a book you probably should read a couple, just to hear about them. For example, Hoffman tells the story of the split-brain patients, which is definitely worth being aware of.

(Those of you who’ve heard that story may be wondering how the heck Hoffman squares it with his idea of consciousness as fundamental. He actually does have a (weird) way to handle this, so read on.)

The other examples come from Hoffman’s research, and other research in his sub-field. There are stories about what optical illusions tell us about our perception, about how evolution primes us to see different things as attractive, and about how advertisers can work with attention.

These stories would at least be a source of a few more cool facts, but I’m a bit wary. The elephant in the room here is the replication crisis. Paper after paper in psychology has turned out to be a statistical mirage, accidental successes that fail to replicate in later experiments. This can happen without any deceit on the part of the psychologist, it’s just a feature of how statistics are typically done in the field.

Some psychologists make a big deal about the replication crisis: they talk about the statistical methods they use, and what they do to make sure they’re getting a real result. Hoffman talks a bit about tricks to rule out other explanations, but mostly doesn’t focus on this kind of thing.. This doesn’t mean he’s doing anything wrong: it might just be it’s off-topic. But it makes it a bit harder to trust him, compared to other psychologists who do make a big deal about it.

The Philosophical Argument

Hoffman structures his book around two philosophical arguments, one that appears near the beginning and another that, as he presents it, is the core thesis of the book. He calls both of these arguments theorems, a naming choice sure to irritate mathematicians and philosophers alike, but the mathematical content in either is for the most part not the point: in each case, the philosophical setup is where the arguments get most of their strength.

The first of these arguments, called The Scrambling Theorem, is set up largely as background material: not his core argument, but just an entry into the overall point he’s making. I found it helpful as a way to get at his reasoning style, the sorts of things he cares about philosophically and the ones he doesn’t.

The Scrambling Theorem is meant to weigh in on the debate over a thought experiment called the Inverted Spectrum, which in turn weighs on the philosophical concept of qualia. The Inverted Spectrum asks us to imagine someone who sees the spectrum of light inverted compared to how we see it, so that green becomes red and red becomes green, without anything different about their body or brain. Such a person would learn to refer to colors the same ways that we do, still referring to red blood even though they see what we see when we see green grass. Philosophers argue that, because we can imagine this, the “qualia” we see in color, like red or green, are distinct from their practical role: they are images in the mind’s eye that can be compared across minds, but do not correspond to anything we have yet characterized scientifically in the physical world.

As a response, other philosophers argued that you can’t actually invert the spectrum. Colors aren’t really a wheel, we can distinguish, for example, more colors between red and blue than between green and yellow. Just flipping colors around would have detectable differences that would have to have physical implications, you can’t just swap qualia and nothing else.

The Scrambling Theorem is in response to this argument. Hoffman argues that, while you can’t invert the spectrum, you can scramble it. By swapping not only the colors, but the relations between them, you can arrange any arbitrary set of colors however else you’d like. You can declare that green not only corresponds to blood and not grass, but that it has more colors between it and yellow, perhaps by stealing them from the other side of the color wheel. If you’re already allowed to swap colors and their associations around, surely you can do this too, and change order and distances between them.

Believe it or not, I think Hoffman’s argument is correct, at least in its original purpose. You can’t respond to the Inverted Spectrum just by saying that colors are distributed differently on different sides of the color wheel. If you want to argue against the Inverted Spectrum, you need a better argument.

Hoffman’s work happens to suggest that better argument. Because he frames this argument in the language of mathematics, as a “theorem”, Hoffman’s argument is much more general than the summary I gave above. He is arguing that not merely can you scramble colors, but anything you like. If you want to swap electrons and photons, you can: just make your photons interact with everything the way electrons did, and vice versa. As long as you agree that the things you are swapping exist, according to Hoffman, you are free to exchange them and their properties any way you’d like.

This is because, to Hoffman, things that “actually exist” cannot be defined just in terms of their relations. An electron is not merely a thing that repels other electrons and is attracted to protons and so on, it is a thing that “actually exists” out there in the world. (Or, as he will argue, it isn’t really. But that’s because in the end he doesn’t think electrons exist.)

(I’m tempted to argue against this with a mathematical object like group elements. Surely the identity element of a group is defined by its relations? But I think he would argue identity elements of groups don’t actually exist.)

In the end, Hoffman is coming from a particular philosophical perspective, one common in modern philosophers of metaphysics, the study of the nature of reality. From this perspective, certain things exist, and are themselves by necessity. We cannot ask what if a thing were not itself. For example, in this perspective it is nonsense to ask what if Superman was not Clark Kent, because the two names refer to the same actually existing person.

(If, you know, Superman actually existed.)

Despite the name of the book, Hoffman is not actually making a case against reality in general. He very much seems to believe in this type of reality, in the idea that there are certain things out there that are real, independent of any purely mathematical definition of their properties. He thinks they are different things than you think they are, but he definitely thinks there are some such things, and that it’s important and scientifically useful to find them.

Hoffman’s second argument is, as he presents it, the core of the book. It’s the argument that’s supposed to show that the world is almost certainly not how we perceive it, even through scientific instruments and the scientific method. Once again, he calls it a theorem: the Fitness Beats Truth theorem.

The Fitness Beats Truth argument begins with a question: why should we believe what we see? Why do we expect that the things we perceive should be true?

In Hoffman’s mind, the only answer is evolution. If we perceived the world inaccurately, we would die out, replaced by creatures that perceived the world better than we did. You might think we also have evidence from biology, chemistry, and physics: we can examine our eyes, test them against cameras, see how they work and what they can and can’t do. But to Hoffman, all of this evidence may be mistaken, because to learn biology, chemistry, and physics we must first trust that we perceive the world correctly to begin with. Evolution, though, doesn’t rely on any of that. Even if we aren’t really bundles of cells replicating through DNA and RNA, we should still expect something like evolution, some process by which things differ, are selected, and reproduce their traits differently in the next generation. Such things are common enough, and general enough, that one can (handwavily) expect them through pure reason alone.

But, says Hoffman’s psychology experience, evolution tricks us! We do mis-perceive, and systematically, in ways that favor our fitness over reality. And so Hoffman asks, how often should we expect this to happen?

The Fitness Beats Truth argument thinks of fitness as randomly distributed: some parts of reality historically made us more fit, some less. This distribution could match reality exactly, so that for any two things that are actually different, they will make us fit in different ways. But it doesn’t have to. There might easily be things that are really very different from each other, but which are close enough from a fitness perspective that to us they seem exactly the same.

The “theorem” part of the argument is an attempt to quantify this. Hoffman imagines a pixelated world, and asks how likely it is that a random distribution of fitness matches a random distribution of pixels. This gets extremely unlikely for a world of any reasonable size, for pretty obvious reasons. Thus, Hoffman concludes: in a world with evolution, we should almost always expect it to hide something from us. The world, if it has any complexity at all, has an almost negligible probability of being as we perceive it.

On one level, this is all kind of obvious. Evolution does trick us sometimes, just as it tricks other animals. But Hoffman is trying to push this quite far, to say that ultimately our whole picture of reality, not just our eyes and ears and nose but everything we see with microscopes and telescopes and calorimeters and scintillators, all of that might be utterly dramatically wrong. Indeed, we should expect it to be.

In this house, we tend to dismiss the Cartesian Demon. If you have an argument that makes you doubt literally everything, then it seems very unlikely you’ll get anything useful from it. Unlike Descartes’s Demon, Hoffman thinks we won’t be tricked forever. The tricks evolution plays on us mattered in our ancestral environment, but over time we move to stranger and stranger situations. Eventually, our fitness will depend on something new, and we’ll need to learn something new about reality.

This means that ultimately, despite the skeptical cast, Hoffman’s argument fits with the way science already works. We are, very much, trying to put ourselves in new situations and test whether our evolved expectations still serve us well or whether we need to perceive things anew. That is precisely what we in science are always doing, every day. And as we’ll see in the next section, whatever new things we have to learn have no particular reason to be what Hoffman thinks they should be.

But while it doesn’t really matter, I do still want to make one counter-argument to Fitness Beats Truth. Hoffman considers a random distribution of fitness, and asks what the chance is that it matches truth. But fitness isn’t independent of truth, and we know that not just from our perception, but from deeper truths of physics and mathematics. Fitness is correlated with truth, fitness often matches truth, for one key reason: complex things are harder than simple things.

Imagine a creature evolving an eye. They have a reason, based on fitness, to need to know where their prey is moving. If evolution was a magic wand, and chemistry trivial, it would let them see their prey, and nothing else. But evolution is not magic, and chemistry is not trivial. The easiest thing for this creature to see is patches of light and darkness. There are many molecules that detect light, because light is a basic part of the physical world. To detect just prey, you need something much more complicated, molecules and cells and neurons. Fitness imposes a cost, and it means that the first eyes that evolve are spots, detecting just light and darkness.

Hoffman asks us not to assume that we know how eyes work, that we know how chemistry works, because we got that knowledge from our perceptions. But the nature of complexity and simplicity, entropy and thermodynamics and information, these are things we can approach through pure thought, as much as evolution. And those principles tell us that it will always be easier for an organism to perceive the world as it truly is than not, because the world is most likely simple and it is most likely the simplest path to perceive it directly. When benefits get high enough, when fitness gets strong enough, we can of course perceive the wrong thing. But if there is only a small fitness benefit to perceiving something incorrectly, then simplicity will win out. And by asking simpler and simpler questions, we can make real durable scientific progress towards truth.

The Physical Argument

So if I’m not impressed by the psychology or the philosophy, what about the part that motivated me to read the book in the first place, the physics?

Because this is, in a weird and perhaps crackpot way, a physics book. Hoffman has a specific idea, more specific than just that the world we perceive is an evolutionary illusion, more specific than that consciousness cannot be explained by the relations between physical particles. He has a proposal, based on these ideas, one that he thinks might lead to a revolutionary new theory of physics. And he tries to argue that physicists, in their own way, have been inching closer and closer to his proposal’s core ideas.

Hoffman’s idea is that the world is made, not of particles or fields or anything like that, but of conscious agents. You and I are, in this picture, certainly conscious agents, but so are the sources of everything we perceive. When we reach out and feel a table, when we look up and see the Sun, those are the actions of some conscious agent intruding on our perceptions. Unlike panpsychists, who believe that everything in the world is conscious, Hoffman doesn’t believe that the Sun itself is conscious, or is made of conscious things. Rather, he thinks that the Sun is an evolutionary illusion that rearranges our perceptions in a convenient way. The perceptions still come from some conscious thing or set of conscious things, but unlike in panpsychism they don’t live in the center of our solar system, or in any other place (space and time also being evolutionary illusions in this picture). Instead, they could come from something radically different that we haven’t imagined yet.

Earlier, I mentioned split brain patients. For anyone who thinks of conscious beings as fundamental, split brain patients are a challenge. These are people who, as a treatment for epilepsy, had the bridge between the two halves of their brain severed. The result is eerily as if their consciousness was split in two. While they only express one train of thought, that train of thought seems to only correspond to the thoughts of one side of their brain, controlling only half their body. The other side, controlling the other half of their body, appears to have different thoughts, different perceptions, and even different opinions, which are made manifest when instead of speaking they use that side of their body to gesture and communicate. While some argue that these cases are over-interpreted and don’t really show what they’re claimed to, Hoffman doesn’t. He accepts that these split-brain patients genuinely have their consciousness split in two.

Hoffman thinks this isn’t a problem because for him, conscious agents can be made up of other conscious agents. Each of us is conscious, but we are also supposed to be made up of simpler conscious agents. Our perceptions and decisions are not inexplicable, but can be explained in terms of the interactions of the simpler conscious entities that make us up, each one communicating with the others.

Hoffman speculates that everything is ultimately composed of the simplest possible conscious agents. For him, a conscious agent must do two things: perceive, and act. So the simplest possible agent perceives and acts in the simplest possible way. They perceive a single bit of information: 0 or 1, true or false, yes or no. And they take one action, communicating a different bit of information to another conscious agent: again, 0 or 1, true or false, yes or no.

Hoffman thinks that this could be the key to a new theory of physics. Instead of thinking about the world as composed of particles and fields, think about it as composed of these simple conscious agents, each one perceiving and communicating one bit at a time.

Hoffman thinks this, in part, because he sees physics as already going in this direction. He’s heard that “spacetime is doomed”, he’s heard that quantum mechanics is contextual and has no local realism, he’s heard that quantum gravity researchers think the world might be a hologram and space-time has a finite number of bits. This all “rhymes” enough with his proposal that he’s confident physics has his back.

Hoffman is trained in psychology. He seems to know his philosophy, at least enough to engage with the literature there. But he is absolutely not a physicist, and it shows. Time and again it seems like he relies on “pop physics” accounts that superficially match his ideas without really understanding what the physicists are actually talking about.

He keeps up best when it comes to interpretations of quantum mechanics, a field where concepts from philosophy play a meaningful role. He covers the reasons why quantum mechanics keeps philosophers up at night: Bell’s Theorem, which shows that a theory that matches the predictions of quantum mechanics cannot both be “realist”, with measurements uncovering pre-existing facts about the world, and “local”, with things only influencing each other at less than the speed of light, the broader notion of contextuality, where measured results are dependent on which other measurements are made, and the various experiments showing that both of these properties hold in the real world.

These two facts, and their implications, have spawned a whole industry of interpretations of quantum mechanics, where physicists and philosophers decide which side of various dilemmas to take and how to describe the results. Hoffman quotes a few different “non-realist” interpretations: Carlo Rovelli’s Relational Quantum Mechanics, Quantum Bayesianism/QBism, Consistent Histories, and whatever Chris Fields is into. These are all different from one another, which Hoffman is aware of. He just wants to make the case that non-realist interpretations are reasonable, that the physicists collectively are saying “maybe reality doesn’t exist” just like he is.

The problem is that Hoffman’s proposal is not, in the quantum mechanics sense, non-realist. Yes, Hoffman thinks that the things we observe are just an “interface”, that reality is really a network of conscious agents. But in order to have a non-realist interpretation, you need to also have other conscious agents not be real. That’s easily seen from the old “Wigner’s friend” thought experiment, where you put one of your friends in a Schrodinger’s cat-style box. Just as Schrodinger’s cat can be both alive and dead, your friend can both have observed something and not have observed it, or observed something and observed something else. The state of your friend’s mind, just like everything else in a non-realist interpretation, doesn’t have a definite value until you measure it.

Hoffman’s setup doesn’t, and can’t, work that way. His whole philosophical project is to declare that certain things exist and others don’t: the sun doesn’t exist, conscious agents do. In a non-realist interpretation, the sun and other conscious agents can both be useful descriptions, but ultimately nothing “really exists”. Science isn’t a catalogue of what does or doesn’t “really exist”, it’s a tool to make predictions about your observations.

Hoffman gets even more confused when he gets to quantum gravity. He starts out with a common misconception: that the Planck length represents the “pixels” of reality, sort of like the pixels of your computer screen, which he uses to support his “interface” theory of consciousness. This isn’t really the right way to think about it the Planck length, though, and certainly isn’t what the people he’s quoting have in mind. The Planck length is a minimum scale in that space and time stop making sense as one approaches it, but that’s not necessarily because space and time are made up of discrete pixels. Rather, it’s because as you get closer to the Planck length, space and time stop being the most convenient way to describe things. For a relatively simple example of how this can work, see my post here.

From there, he reflects on holography: the discovery that certain theories in physics can be described equally well by what is happening on their boundary as by their interior, the way that a 2D page can hold all the information for an apparently 3D hologram. He talks about the Bekenstein bound, the conjecture that there is a maximum amount of information needed to describe a region of space, proportional not to the volume of the region but to its area. For Hoffman, this feels suspiciously like human vision: if we see just a 2D image of the world, could that image contain all the information needed to construct that world? Could the world really be just what we see?

In a word, no.

On the physics side, the Bekenstein bound is a conjecture, and one that doesn’t always hold. A more precise version that seems to hold more broadly, called the Bousso bound, works by demanding the surface have certain very specific geometric properties in space-time, properties not generally shared by the retinas of our eyes.

But it even fails in Hoffman’s own context, once we remember that there are other types of perception than vision. When we hear, we don’t detect a 2D map, but a 1D set of frequencies, put in “stereo” by our ears. When we feel pain, we can feel it in any part of our body, essentially a 3D picture since it goes inwards as well. Nothing about human perception uniquely singles out a 2D surface.

There is actually something in physics much closer to what Hoffman is imagining, but it trades on a principle Hoffman aspires to get rid of: locality. We’ve known since Einstein that you can’t change the world around you faster than the speed of light. Quantum mechanics doesn’t change that, despite what you may have heard. More than that, simultaneity is relative: two distant events might be at the same time in your reference frame, but for someone else one of them might be first, or the other one might be, there is no one universal answer.

Because of that, if you want to think about things happening one by one, cause following effect, actions causing consequences, then you can’t think of causes or actions as spread out in space. You have to think about what happens at a single point: the location of an imagined observer.

Once you have this concept, you can ask whether describing the world in terms of this single observer works just as well as describing it in terms of a wide open space. And indeed, it actually can do well, at least under certain conditions. But one again, this really isn’t how Hoffman is doing things: he has multiple observers all real at the same time, communicating with each other in a definite order.

In general, a lot of researchers in quantum gravity think spacetime is doomed. They think things are better described in terms of objects with other properties and interactions, with space and time as just convenient approximations for a more complicated reality. They get this both from observing properties of the theories we already have, and from thought experiments showing where those theories cause problems.

Nima, the most catchy of these quotable theorists, is approaching the problem from the direction of scattering amplitudes: the calculations we do to find the probability of observations in particle physics. Each scattering amplitude describes a single observation: what someone far away from a particle collision can measure, independent of any story of what might have “actually happened” to the particles in between. Nima’s goal is to describe these amplitudes purely in terms of those observations, to get rid of the “story” that shows up in the middle as much as possible.

The other theorists have different goals, but have this in common: they treat observables as their guide. They look at the properties that a single observer’s observations can have, and try to take a fresh view, independent of any assumptions about what happens in between.

This key perspective, this key insight, is what Hoffman is missing throughout this book. He has read what many physicists have to say, but he does not understand why they are saying it. His book is titled The Case Against Reality, but he merely trades one reality for another. He stops short of the more radical, more justified case against reality: that “reality”, that thing philosophers argue about and that makes us think we can rule out theories based on pure thought, is itself the wrong approach: that instead of trying to characterize an idealized real world, we are best served by focusing on what we can do.

One thing I didn’t do here is a full critique of Hoffman’s specific proposal, treating it as a proposed theory of physics. That would involve quite a bit more work, on top of what has turned out to be a very long book review. I would need to read not just his popular description, but the actual papers where he makes his case and lays out the relevant subtleties. Since I haven’t done that, I’ll end with a few questions: things that his proposal will need to answer if it aspires to be a useful idea for physics.

  • Are the networks of conscious agents he proposes Turing-complete? In other words, can they represent any calculation a computer can do? If so, they aren’t a useful idea for physics, because you could imagine a network of conscious agents to reproduce any theory you want. The idea wouldn’t narrow things down to get us closer to a useful truth. This was also one of the things that made me uncomfortable with the Wolfram Physics Project.
  • What are the conditions that allow a network of simple conscious agents to make up a bigger conscious agent? Do those conditions depend meaningfully on the network’s agents being conscious, or do they just have to pass messages? If the latter, then Hoffman is tacitly admitting you can make a conscious agent out of non-conscious agents, even if he insists this is philosophically impossible.
  • How do you square this network with relativity and quantum mechanics? Is there a set time, an order in which all the conscious agents communicate with each other? If so, how do you square that with the relativity of simultaneity? Are the agents themselves supposed to be able to be put in quantum states, or is quantum mechanics supposed to emerge from a theory of classical agents?
  • How does evolution fit in here? A bit part of Hoffman’s argument was supported by the universality of the evolutionary algorithm. In order for evolution to matter for your simplest agents, they need to be able to be created or destroyed. But then they have more than two actions: not just 0 and 1, but 0, 1, and cease to exist. So you could have an even simpler agent that has just two bits.

Valentine’s Day Physics Poem 2024

It’s that time of year again! In one of this blog’s yearly traditions, I’m posting a poem mixing physics and romance. For those who’d like to see more, you can find past years’ poems here.

Modeling Together

Together, we set out to model the world, and learn something new.

The Physicist said,
“My model is simple, the model of fundamental things. Particles go in, particles go out. For each configuration, a probability. For each calculation, an approximation. I can see the path, clear as day. I just need to fix the parameters.”

The Engineer responded,
“I will trust you, because you are a Physicist. You dream of greater things, and have given me marvels. But my models are the models of everything else. Their parameters are countless as waves of the ocean, and all complex things are their purview. Their only path is to learn, and learn more, and see where learning takes you.”

The Physicist followed his model, and the Engineer followed along. With their money and sweat, cajoling and wheedling, they built a grand machine, all to the Physicist’s specifications. And according to the Physicist’s path, parameters begun to be fixed.

But something was missing.

The Engineer asked,
“What are we learning, following your path? We have spent and spent, but all I see is your machine. What marvels will it give us? What children will it feed?”

The Physicist considered, and said,
“You must wait for the marvels, and wait for the learning. New things take time. But my path is clear, my model is the only choice.”

The Engineer, with patience, responded,
“I will trust you, because you are a Physicist, and know the laws of your world. But my models are the models of everything else, and there is always another choice.”

Months went by, and they fed more to the machine. More energy, more time, more insight, more passion. Parameters tightened, and they hoped for marvels.

And they learned, one by one, that the marvels would not come. The machine would not spare them toil, would not fill the Engineer’s pockets or feed the starving, would not fill the world with art and mystery and value.

And the Engineer asked,
“Without these marvels, must we keep following your path? Should we not go out into the world, and learn another?”

And the Physicist thought, and answered,
“You must wait a little longer. For my model is the only model I have known, the only path I know to follow, and I am loathe to abandon it.”

And the Engineer, generously, responded,
“I will trust you, because you are a Physicist, down to the bone. But my models are the models of everything else, of chattering voices and adaptable answers. And you can always learn another path.”

More months went by. The machine gave less and less, and took more and more for the giving. Energy was dear, and time more so, and the waiting was its own kind of emptiness.

The Engineer, silently, looked to the Physicist.

The Physicist said,
“I will trust you. Because you are an Engineer, yes, and your models are the models of everything else. And because, through these months, you have trusted me. I am ready to learn, and learn more, and try something new. Let us try a new model, and see where it leads.”

The simplest model says that one and one is two, and two is greater. We are billions of parameters, and can miss the simple things. But time,
                                                           And learning,
Can fix parameters,
And us.

Neu-tree-no Detector

I’ve written before about physicists’ ideas for gigantic particle accelerators, proposals for machines far bigger than the Large Hadron Collider or even plans for a Future Circular Collider. The ideas ranged from wacky but not obviously impossible (a particle collider under the ocean) to pure science fiction (a beam of neutrinos that can blow up nukes across the globe).

But what if you don’t want to accelerate particles? What if, instead, you want to detect particles from the depths of space? Can you still propose ridiculously huge things?

Neutrinos are extremely hard to detect. Immune to the strongest forces of nature, they only interact via the weak nuclear force and gravity. The weakness of these forces means they can pass through huge amounts of material without disturbing a single atom. The Sudbury Neutrino Observatory used a tank of 1000 tonnes of water in order to stop enough neutrinos to study them. The IceCube experiment is bigger yet, and getting even bigger: their planned expansion will fill eight cubic kilometers of Antarctic ice with neutrino detectors, letting them measure around a million neutrinos every year.

But if you want to detect the highest-energy neutrinos, you may have to get even bigger than that. With so few of them to study, you need to cover a huge area with antennas to spot a decent number of them.

Or, maybe you can just use trees.

Pictured: a physics experiment?

That’s the proposal of Steven Prohira, a MacArthur Genius Grant winner who works as a professor at the University of Kansas. He suggests that, instead of setting up a giant array of antennas to detect high-energy neutrinos, trees could be used, with a coil of wire around the tree to measure electrical signals. Prohira even suggests that “A forest detector could also motivate the large-scale reforesting of land, to grow a neutrino detector for future generations”.

Despite sounding wacky, tree antennas have actually been used before. Militaries have looked into them as a way to set up antennas in remote locations, and later studies indicate they work surprisingly well. So the idea is not completely impossible, much like the “collider-under-the-sea”.

Like the “collider-under-the-sea”, though, some wackiness still remains. Prohira admits he hasn’t yet done all the work needed to test the idea’s feasibility, and comparing to mature experiments like IceCube makes it clear there is a lot more work to be done. Chatting with neutrino experts, one problem a few of them pointed out is that unlike devices sunk into Antarctic ice, trees are not uniformly spaced, and that might pose a problem if you want to measure neutrinos carefully.

What stands out to me, though, is that those questions are answerable. If the idea sounds promising, physicists can follow up. They can make more careful estimates, or do smaller-scale experiments. They won’t be stuck arguing over interpretations, or just building the full experiment and seeing if it works.

That’s the great benefit of a quantitative picture of the world. We can estimate some things very accurately, with theories that give very precise numbers for how neutrinos behave. Other things we can estimate less accurately, but still can work on: how tall trees are, how widely they are spaced, how much they vary. We have statistical tools and biological data. We can find numbers, and even better, we can know how uncertain we should be about those numbers. Because of that picture, we don’t need to argue fruitlessly about ideas like this. We can work out numbers, and check!

Generalize

What’s the difference between a model and an explanation?

Suppose you cared about dark matter. You observe that things out there in the universe don’t quite move the way you would expect. There is something, a consistent something, that changes the orbits of galaxies and the bending of light, the shape of the early universe and the spiderweb of super-clusters. How do you think about that “something”?

One option is to try to model the something. You want to use as few parameters as possible, so that your model isn’t just an accident, but will actually work to predict new data. You want to describe how it changes gravity, on all the scales you care about. Your model might be very simple, like the original MOND, and just describe a modification to Newtonian gravity, since you typically only need Newtonian gravity to model many of these phenomena. (Though MOND itself can’t account for all the things attributed to dark matter, so it had to be modified.) You might have something slightly more complicated, proposing some “matter” but not going into much detail about what it is, just enough for your model to work.

If you were doing engineering, a model like that is a fine thing to have. If you were building a spaceship and wanted to figure out what its destination would look like after a long journey, you’d need a model of dark matter like this, one that predicted how galaxies move and light bends, to do the job.

But a model like that isn’t an explanation. And the reason why is that explanations generalize.

In practice, you often just need Newtonian gravity to model how galaxies move. But if you want to model more dramatic things, the movement of the whole universe or the area around a black hole, then you need general relativity as well. So to generalize to those areas, you can’t just modify Newtonian gravity. You need an explanation, one that tells you not just how Newton’s equations change, but how Einstein’s equations change.

In practice, you can get by with a simple model of dark matter, one that doesn’t tell you very much, and just adds a new type of matter. But if you want to model quantum gravity, you need to know how this new matter interacts, not just at baseline with gravity, but with everything else. You need to know how the new matter is produced, whether it gets its mass from the Higgs boson or from something else, whether it falls into the same symmetry groups as the Standard Model or totally new ones, how it arises from tangled-up strings and multi-dimensional membranes. You need not just a model, but an explanation, one that tells you not just roughly what kind of particle you need, but how it changes our models of particle physics overall.

Physics, at its best, generalizes. Newton’s genius wasn’t that he modeled gravity on Earth, but that he unified it with gravity in the solar system. By realizing that gravity was universal, he proposed an explanation that led to much more progress than the models of predecessors like Kepler. Later, Einstein’s work on general relativity led to similar progress.

We can’t always generalize. Sometimes, we simply don’t know enough. But if we’re not engineering, then we don’t need a model, and generalizing should, at least in the long-run, be our guiding hope.