Tag Archives: DoingScience

Gateway Hobbies

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

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

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

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

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

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

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

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

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

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

Keeping It Colloquial

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

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

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

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

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

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

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

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

Of Snowmass and SAGEX

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

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

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

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

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

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

How Expert Is That Expert?

The blog Astral Codex Ten had an interesting post a while back, about when to trust experts. Rather than thinking of some experts as “trustworthy” and some as “untrustworthy”, the post suggests an approach of “bounded distrust”. Even if an expert is biased or a news source sometimes lies, there are certain things you can still expect them to tell the truth about. If you are familiar enough with their work, you can get an idea of which kinds of claims you can trust and which you can’t, in a consistent and reliable way. Knowing how to do this is a skill, one you can learn to get better at.

In my corner of science, I can’t think of anyone who outright lies. Nonetheless, some claims are worth more trust than others. Sometimes experts have solid backing for what they say, direct experience that’s hard to contradict. Other times they’re speaking mostly from general impressions, and bias could easily creep in. Luckily, it’s not so hard to tell the difference. In this post, I’ll try to teach you how.

For an example, I’ll use something I saw at a conference last week. A speaker gave a talk describing the current state of cosmology: the new tools we have to map the early universe, and the challenges in using them to their full potential. After the talk, I remember her answering three questions. In each case, she seemed to know what she was talking about, but for different reasons. If she was contradicted by a different expert, I’d use these reasons to figure out which one to trust.

First, sometimes an expert gives what is an informed opinion, but just an informed opinion. As scientists, we are expected to know a fairly broad range of background behind our work, and be able to say something informed about it. We see overview talks and hear our colleagues’ takes, and get informed opinions about topics we otherwise don’t work on. This speaker fielded a question about quantum gravity, and her answer made it clear that the topic falls into this category for her. Her answer didn’t go into much detail, mentioning a few terms but no specific scientific results, and linked back in the end to a different question closer to her expertise. That’s generally how we speak on this kind of topic: vaguely enough to show what we know without overstepping.

The second question came from a different kind of knowledge, which I might call journal club knowledge. Many scientists have what are called “journal clubs”. We meet on a regular basis, read recent papers, and talk about them. The papers go beyond what we work on day-to-day, but not by that much, because the goal is to keep an eye open for future research topics. We read papers in close-by areas, watching for elements that could be useful, answers to questions we have or questions we know how to answer. The kind of “journal club knowledge” we have covers a fair amount of detail: these aren’t topics we are working on right now, but if we spent more time on it they could be. Here, the speaker answered a question about the Hubble tension, a discrepancy between two different ways of measuring the expansion of the universe. The way she answered focused on particular results: someone did X, there was a paper showing Y, this telescope is planning to measure Z. That kind of answer is a good way to tell that someone is answering from “journal club knowledge”. It’s clearly an area she could get involved in if she wanted to, one where she knows the important questions and which papers to read, with some of her own work close enough to the question to give an important advantage. But it was also clear that she hadn’t developed a full argument on one “side” or the other, and as such there are others I’d trust a bit more on that aspect of the question.

Finally, experts are the most trustworthy when we speak about our own work. In this speaker’s case, the questions about machine learning were where her expertise clearly shone through. Her answers there were detailed in a different way than her answers about the Hubble tension: not just papers, but personal experience. They were full of phrases like “I tried that, but it doesn’t work…” or “when we do this, we prefer to do it this way”. They also had the most technical terms of any of her answers, terms that clearly drew distinctions relevant to those who work in the field. In general, when an expert talks about what they do in their own work, and uses a lot of appropriate technical terms, you have especially good reason to trust them.

These cues can help a lot when evaluating experts. An expert who makes a generic claim, like “no evidence for X”, might not know as much as an expert who cites specific papers, and in turn they might not know as much as an expert who describes what they do in their own research. The cues aren’t perfect: one risk is that someone may be an expert on their own work, but that work may be irrelevant to the question you’re asking. But they help: rather than distrusting everyone, they help you towards “bounded distrust”, knowing which claims you can trust and which are riskier.

At the Bohr Centennial

One hundred years ago, Niels Bohr received his Nobel prize. One hundred and one years ago, the Niels Bohr Institute opened its doors (it would have been one hundred and two, but pandemics are inconvenient things).

This year, also partly delayed by a pandemic, the Niels Bohr Institute is celebrating.

Using the fanciest hall the university has.

We’ve had a three-day conference, packed with Nobel prizewinners, people who don’t feel out of place among Nobel prizewinners, and for one morning’s ceremony the crown prince of Denmark. There were last-minute cancellations but also last-minute additions, including a moving speech by two Ukrainian PhD students. I don’t talk politics on this blog, so I won’t say much more about it (and you shouldn’t in the comments either, there are better venues), but I will say that was the only time I’ve seen a standing ovation at a scientific conference.

The other talks ran from reminiscences (Glashow struggled to get to the stage, but his talk was witty, even quoting Peter Woit apparently to try to rile David Gross in the front row (next to the Ukranian PhD students who must have found it very awkward)) to classic colloquium style talks (really interesting crisply described puzzles from astrochemistry to biophysics) to a few more “conference-ey” talks (t’Hooft, unfortunately). It’s been fun, but also exhausting, and as such that’s all I’m writing this week.

The Unpublishable Dirty Tricks of Theoretical Physics

As the saying goes, it is better not to see laws or sausages being made. You’d prefer to see the clean package on the outside than the mess behind the scenes.

The same is true of science. A good paper tells a nice, clean story: a logical argument from beginning to end, with no extra baggage to slow it down. That story isn’t a lie: for any decent paper in theoretical physics, the conclusions will follow from the premises. Most of the time, though, it isn’t how the physicist actually did it.

The way we actually make discoveries is messy. It involves looking for inspiration in all the wrong places: pieces of old computer code and old problems, trying to reproduce this or that calculation with this or that method. In the end, once we find something interesting enough, we can reconstruct a clearer, cleaner, story, something actually fit to publish. We hide the original mess partly for career reasons (easier to get hired if you tell a clean, heroic story), partly to be understood (a paper that embraced the mess of discovery would be a mess to read), and partly just due to that deep human instinct to not let others see us that way.

The trouble is, some of that “mess” is useful, even essential. And because it’s never published or put into textbooks, the only way to learn it is word of mouth.

A lot of these messy tricks involve numerics. Many theoretical physics papers derive things analytically, writing out equations in symbols. It’s easy to make a mistake in that kind of calculation, either writing something wrong on paper or as a bug in computer code. To correct mistakes, many things are checked numerically: we plug in numbers to make sure everything still works. Sometimes this means using an approximation, trying to make sure two things cancel to some large enough number of decimal places. Sometimes instead it’s exact: we plug in prime numbers, and can much more easily see if two things are equal, or if something is rational or contains a square root. Sometimes numerics aren’t just used to check something, but to find a solution: exploring many options in an easier numerical calculation, finding one that works, and doing it again analytically.

“Ansatze” are also common: our fancy word for an educated guess. These we sometimes admit, when they’re at the core of a new scientific idea. But the more minor examples go un-mentioned. If a paper shows a nice clean formula and proves it’s correct, but doesn’t explain how the authors got it…probably, they used an ansatz. This trick can go hand-in-hand with numerics as well: make a guess, check it matches the right numbers, then try to see why it’s true.

The messy tricks can also involve the code itself. In my field we often use “computer algebra” systems, programs to do our calculations for us. These systems are programming languages in their own right, and we need to write computer code for them. That code gets passed around informally, but almost never standardized. Mathematical concepts that come up again and again can be implemented very differently by different people, some much more efficiently than others.

I don’t think it’s unreasonable that we leave “the mess” out of our papers. They would certainly be hard to understand otherwise! But it’s a shame we don’t publish our dirty tricks somewhere, even in special “dirty tricks” papers. Students often start out assuming everything is done the clean way, and start doubting themselves when they notice it’s much too slow to make progress. Learning the tricks is a big part of learning to be a physicist. We should find a better way to teach them.

Science, Gifts Enough for Lifetimes

Merry Newtonmas, Everyone!

In past years, I’ve compared science to a gift: the ideal gift for the puzzle-fan, one that keeps giving new puzzles. I think people might not appreciate the scale of that gift, though.

Bigger than all the creative commons Wikipedia images

Maybe you’ve heard the old joke that studying for a PhD means learning more and more about less and less until you know absolutely everything about nothing at all. This joke is overstating things: even when you’ve specialized down to nothing at all, you still won’t know everything.

If you read the history of science, it might feel like there are only a few important things going on at a time. You notice the simultaneous discoveries, like calculus from Newton and Liebniz and natural selection from Darwin and Wallace. You can get the impression that everyone was working on a few things, the things that would make it into the textbooks. In fact, though, there was always a lot to research, always many interesting things going on at once. As a scientist, you can’t escape this. Even if you focus on your own little area, on a few topics you care about, even in a small field, there will always be more going on than you can keep up with.

This is especially clear around the holiday season. As everyone tries to get results out before leaving on vacation, there is a tidal wave of new content. I have five papers open on my laptop right now (after closing four or so), and some recorded talks I keep meaning to watch. Two of the papers are the kind of simultaneous discovery I mentioned: two different groups noticing that what might seem like an obvious fact – that in classical physics, unlike in quantum, one can have zero uncertainty – has unexpected implications for our kind of calculations. (A third group got there too, but hasn’t published yet.) It’s a link I would never have expected, and with three groups coming at it independently you’d think it would be the only thing to pay attention to: but even in the same sub-sub-sub-field, there are other things going on that are just as cool! It’s wild, and it’s not some special quirk of my area: that’s science, for all us scientists. No matter how much you expect it to give you, you’ll get more, lifetimes and lifetimes worth. That’s a Newtonmas gift to satisfy anyone.

The Irons in the Fire Metric

I remember, a while back, visiting a friend in his office. He had just became a professor, and was still setting things up. I noticed a list on the chalkboard, taking up almost one whole side. Taking a closer look, I realized that list was a list of projects. To my young postdoc eyes, the list was amazing: how could one person be working on so many things?

There’s an idiom in English, “too many irons in the fire”. You can imagine a blacksmith forging many things at once, each piece of iron taking focus from the others. Too many, and a piece might break, or otherwise fail.

Perhaps the irons in the fire are fire irons

In theoretical physics, a typical PhD publishes three papers before they graduate. That usually means one project at a time, maybe two. For someone used to one or two irons in the fire, so many at a time seems an impossible feat.

Scientists grow over their careers, though, and in more than one way. What seems impossible can eventually be business as usual.

First, as your skill grows, you become more efficient. A lot of scientific work is a kind of debugging: making mistakes, and figuring out how to fix them. The more experience you have, the more you know what kinds of mistakes you might make, and the better you will be at avoiding them. (Never perfect, of course: scientists always have to debug something.)

Second, your collaborations grow. The more people you work with, the more you can share these projects, each person contributing their own piece. With time, you start supervising as well: Masters students, PhD students, postdocs. Each one adds to the number of irons you can manage in your fire. While for bad supervisors this just means having their name on lots of projects, the good supervisors will be genuinely contributing to each one. That’s yet another kind of growth: as you get further along, you get a better idea of what works and what doesn’t, so even in a quick meeting you can solve meaningful problems.

Third, you grow your system. The ideas you explore early on blossom into full-fledged methods, tricks which you can pull out again and again when you need them. The tricks combine, forming new, bigger tricks, and eventually a long list of projects becomes second nature, a natural thing your system is able to do.

As you grow as a scientist, you become more than just one researcher, one debugger at a laptop or pipetter at a lab bench. You become a research program, one that manifests across many people and laptops and labs. As your expertise grows, you become a kind of living exchange of ideas, concepts flowing through you when needed, building your own scientific world.

Serial Killers and Grad School Horror Stories

It’s time for my yearly Halloween post. My regular readers know what to expect: a horror trope and a physics topic, linked by a tortured analogy. And this year, the pun is definitely intended.

Horror movies have a fascination with serial killers. Over the years, they’ve explored every possible concept: from gritty realism to the supernatural, crude weapons to sophisticated traps, motivations straightforward to mysterious, and even killers who are puppets.

Yes I know Billy is not actually the killer in the Saw films

One common theme of all fictional serial killers is power. Serial killers are scary because they have almost all the power in a situation, turned to alien and unpredictable goals. The protagonists of a horror film are the underdogs, never knowing whether the killer will pull out some new ability or plan that makes everything they try irrelevant. Even if they get the opportunity to negotiate, the power imbalance means that they can’t count on getting what they need: anything the killer agrees will be twisted to serve their own ends.

Academics tell their own kind of horror stories. Earlier this month, the historian Brett Deveraux had a blog post about graduate school, describing what students go through to get a PhD. As he admits, parts of his story only apply to the humanities. STEM departments have more money, and pay their students a bit better. It’s not a lot better (I was making around $20,000 a year at Stony Brook), but it’s enough that I’ve never heard of a student taking out a loan to make ends meet. (At most, people took on tutoring jobs for a bit of extra cash.) We don’t need to learn new languages, and our degrees take a bit less time: six or seven years for an experimental physicist, and often five for a theoretical physicist. Finally, the work can be a lot less lonely, especially for those who work in a lab.

Still, there is a core in common, and that core once again is power. Universities have power, of course: and when you’re not a paying customer but an employee with your career on the line, that power can be quite scary. But the person with the most power over a PhD student is their advisor. Deveraux talks compellingly about the difference that power can make: how an advisor who is cruel, or indifferent, or just clueless, can make or break not just your career but your psychological well-being. The lucky students, like Deveraux and me, find supportive mentors who help us survive and move forward. The unlucky students leave with scars, even if those scars aren’t jigsaw-shaped.

Neither Deveraux or I have experience with PhD programs in Europe, which are quite different in structure from those in the US. But the power imbalance is still there, and still deadly, and so despite the different structure, I’ve seen students here break down, scarred in the same way.

Deveraux frames his post as advice for those who want to go to grad school, and his first piece of advice is “Have you tried wanting something else?” I try to echo that when I advise students. I don’t always succeed: there’s something exciting about a young person interested in the same topics we’re interested in, willing to try to make a life of it. But it is important to know what you’re getting into, and to know there’s a big world out there of other options. If, after all that, you decide to stick through it, just remember: power matters. If you give someone power over you, try to be as sure as you can that it won’t turn into a horror story.

In Uppsala for Elliptics 2021

I’m in Uppsala in Sweden this week, at an actual in-person conference.

With actual blackboards!

Elliptics started out as a series of small meetings of physicists trying to understand how to make sense of elliptic integrals in calculations of colliding particles. It grew into a full-fledged yearly conference series. I organized last year, which naturally was an online conference. This year though, the stage was set for Uppsala University to host in person.

I should say mostly in person. It’s a hybrid conference, with some speakers and attendees joining on Zoom. Some couldn’t make it because of travel restrictions, or just wanted to be cautious about COVID. But seemingly just as many had other reasons, like teaching schedules or just long distances, that kept them from coming in person. We’re all wondering if this will become a long-term trend, where the flexibility of hybrid conferences lets people attend no matter their constraints.

The hybrid format worked better than expected, but there were still a few kinks. The audio was particularly tricky, it seemed like each day the organizers needed a new microphone setup to take questions. It’s always a little harder to understand someone on Zoom, especially when you’re sitting in an auditorium rather than focused on your own screen. Still, technological experience should make this work better in future.

Content-wise, the conference began with a “mini-school” of pedagogical talks on particle physics, string theory, and mathematics. I found the mathematical talks by Erik Panzer particularly nice, it’s a topic I still feel quite weak on and he laid everything out in a very clear way. It seemed like a nice touch to include a “school” element in the conference, though I worry it ate too much into the time.

The rest of the content skewed more mathematical, and more string-theoretic, than these conferences have in the past. The mathematical content ranged from intriguing (including an interesting window into what it takes to get high-quality numerics) to intimidatingly obscure (large commutative diagrams, category theory on the first slide). String theory was arguably under-covered in prior years, but it felt over-covered this year. With the particle physics talks focusing on either general properties with perhaps some connections to elliptics, or to N=4 super Yang-Mills, it felt like we were missing the more “practical” talks from past conferences, where someone was computing something concrete in QCD and told us what they needed. Next year is in Mainz, so maybe those talks will reappear.