Category Archives: Life as a Physicist

Answering Questions: Virtue or Compulsion?

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I was talking to a colleague about this blog. I mentioned worries I’ve had about email conversations with readers: worries about whether I’m communicating well, whether the readers are really understanding. For the colleague though, something else stood out:

“You sure are generous with your time.”

Am I?

I’d never really thought about it that way before. It’s not like I drop everything to respond to a comment, or a message. I leave myself a reminder, and get to it when I have time. To the extent that I have a time budget, I don’t spend it freely, I prioritize work before chatting with my readers, as nice as you folks are.

At the same time, though, I think my colleague was getting at a real difference there. It’s true that I don’t answer questions right away. But I do answer them eventually. I can’t imagine being asked a question, and just never answering it.

There are exceptions, of course. If you’re obviously just trolling, just insulting me or messing with me or asking the same question over and over, yeah I’ll skip your question. And if I don’t understand what you’re asking, there’s only so much effort I’m going to put in to try to decipher it. Even in those cases, though, I have a certain amount of regret. I have to take a deep breath and tell myself no, I can really skip this one.

On the one hand, this feels like a moral obligation, a kind of intellectual virtue. If knowledge, truth, information are good regardless of anything else, then answering questions is just straightforwardly good. People ought to know more, asking questions is how you learn, and that can’t work unless we’re willing to teach. Even if there’s something you need to keep secret, you can at least say something, if only to explain why you can’t answer. Just leaving a question hanging feels like something bad people do.

On the other hand, I think this might just be a compulsion, a weird quirk of my personality. It may even be more bad than good, an urge that makes me “waste my time”, or makes me too preoccupied with what others say, drafting responses in my head until I find release by writing them down. I think others are much more comfortable just letting a question lie, and moving on. It feels a bit like the urge to have the last word in a conversation, just more specific: if someone asks me to have the last word, I feel like I have to oblige!

I know this has to have its limits. The more famous bloggers get so many questions they can’t possibly respond to all of them. I’ve seen how people like Neil Gaiman describe responding to questions on tumblr, just opening a giant pile of unread messages, picking a few near the top, and then going back to their day. I can barely stand leaving unread messages in my email. If I got that famous, I don’t know how I’d deal with that. But I’d probably figure something out.

Am I too generous with you guys? Should people always answer questions? And does the fact that I ended this post with questions mean I’ll get more comments?

Of Snowmass and SAGEX

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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?

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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

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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

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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

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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

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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

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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.

Breaking Out of “Self-Promotion Voice”

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What do TED talks and grant applications have in common?

Put a scientist on a stage, and what happens? Some of us panic and mumble. Others are as smooth as a movie star. Most, though, fall back on a well-practiced mode: “self-promotion voice”.

A scientist doing self-promotion voice is easy to recognize. We focus on ourselves, of course (that’s in the name!), talking about all the great things we’ve done. If we have to mention someone else, we make sure to link it in some way: “my colleague”, “my mentor”, “which inspired me to”. All vulnerability is “canned” in one way or another: “challenges we overcame”, light touches on the most sympathetic of issues. Usually, we aren’t negative towards our colleagues either: apart from the occasional very distant enemy, everyone is working with great scientific virtue. If we talk about our past, we tell the same kinds of stories, mentioning our youthful curiosity and deep buzzwordy motivations. Any jokes or references are carefully pruned, made accessible to the lowest-common-denominator. This results in a standard vocabulary: see a metaphor, a quote, or a turn of phrase, and you’re bound to see it in talks again and again and again. Things get even more repetitive when you take into account how often we lean on the voice: a given speech or piece will be assembled from elementary pieces, snippets of practiced self-promotion that we pour in like packing peanuts after a minimal edit, filling all available time and word count.

“My passion for teaching manifests…”

Packing peanuts may not be glamorous, but they get the job done. A scientist who can’t do “the voice” is going to find life a lot harder, their negativity or clumsiness turning away support when they need it most. Except for the greatest of geniuses, we all have to learn a bit of self-promotion to stay employed.

We don’t have to stop there, though. Self-promotion voice works, but it’s boring and stilted, and it all looks basically the same. If we can do something a bit more authentic then we stand out from the crowd.

I’ve been learning this more and more lately. My blog posts have always run the gamut: some are pure formula, but the ones I’m most proud of have a voice all their own. Over the years, I’ve been pushing my applications in that direction. Each grant and job application has a bit of the standard self-promotion voice pruned away, and a bit of another voice (my own voice?) sneaking in. This year, as I send out applications, I’ve been tweaking things. I almost hope the best jobs come late in the year, my applications will be better then!

Why Can’t I Pay Academics to Do Things for Me?

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A couple weeks back someone linked to this blog with a problem. A non-academic, he had done some mathematical work but didn’t feel it was ready to publish. He reached out to a nearby math department and asked what they would charge to help him clean up the work. If the price was reasonable, he’d do it, if not at least he’d know what it would cost.

Neither happened. He got no response, and got more and more frustrated.

For many of you, that result isn’t a big surprise. My academic readers are probably cringing at the thought of getting an email like that. But the guy’s instinct here isn’t too off-base. Certainly, in many industries that kind of email would get a response with an actual quote. Academia happens to be different, in a way that makes the general rule not really apply.

There’s a community called Effective Altruists that evaluate charities. They have a saying, “Money is the Unit of Caring”. The point of the saying isn’t that people with more money care more, or anything like that. Rather, it’s a reminder that, whatever a charity wants to accomplish, more money makes it easier. A lawyer could work an hour in a soup kitchen, but if they donated the proceeds of an hour’s work the soup kitchen could hire four workers instead. Food banks would rather receive money than food, because the money lets them buy whatever they need in bulk. As the Simpsons meme says, “money can be exchanged for goods and services”.

If you pay a charity, or a business, it helps them achieve what they want to do. If you pay an academic, it gets a bit more complicated.

The problem is that for academics, time matters a lot more than our bank accounts. If we want to settle down with a stable job, we need to spend our time doing things that look good on job applications: writing papers, teaching students, and so on. The rest of the time gets spent resting so we have the energy to do all of that.

(What about tenured professors? They don’t have to fight for their own jobs…but by that point, they’ve gotten to know their students and other young people in their sub-field. They want them to get jobs too!)

Money can certainly help with those goals, but not personal money: grant money. With grant money we can hire students and postdocs to do some of that work for us, or pay our own salary so we’re easier for a university to hire. We can buy equipment for those who need that sort of thing, and get to do more interesting science. Rather than “Money is the Unit of Caring”, for academics, “Grant Money is the Unit of Caring”.

Personal money, in contrast, just matters for our rest time. And unless we have expensive tastes, we usually get paid enough for that.

(The exception is for extremely underpaid academics, like PhD students and adjuncts. For some of them money can make a big difference to their quality of life. I had quite a few friends during my PhD who had side gigs, like tutoring, to live a bit more comfortably.)

This is not to say that it’s impossible to pay academics to do side jobs. People do. But when it works, it’s usually due to one of these reasons:

  1. It’s fun. Side work trades against rest time, but if it helps us rest up then it’s not really a tradeoff. Even if it’s a little more boring that what we’d rather do, if it’s not so bad the money can make up the difference.
  2. It looks good on a CV. This covers most of the things academics are sometimes paid to do, like writing articles for magazines. If we can spin something as useful to our teaching or research, or as good for the greater health of the field (or just for our “personal brand”), then we can justify doing it.
  3. It’s a door out of academia. I’ve seen the occasional academic take time off to work for a company. Usually that’s a matter of seeing what it’s like, and deciding whether it looks like a better life. It’s not really “about the money”, even in those cases.

So what if you need an academic’s help with something? You need to convince them it’s worth their time. Money could do it, but only if they’re living precariously, like some PhD students. Otherwise, you need to show that what you’re asking helps the academic do what they’re trying to do: that it is likely to move the field forward, or that it fulfills some responsibility tied to their personal brand. Without that, you’re not likely to hear back.