Category Archives: Life as a Physicist

What Do Theorists Do at Work?

Picture a scientist at work. You’re probably picturing an experiment, test tubes and beakers bubbling away. But not all scientists do experiments. Theoretical physicists work on the mathematical side of the field, making predictions and trying to understand how to make them better. So what does it look like when a theoretical physicist is working?

A theoretical physicist, at work in the equation mines

The first thing you might imagine is that we just sit and think. While that happens sometimes, we don’t actually do that very often. It’s better, and easier, to think by doing something.

Sometimes, this means working with pen and paper. This should be at least a little familiar to anyone who has done math homework. We’ll do short calculations and draw quick diagrams to test ideas, and do a more detailed, organized, “show your work” calculation if we’re trying to figure out something more complicated. Sometimes very short calculations are done on a blackboard instead, it can help us visualize what we’re doing.

Sometimes, we use computers instead. There are computer algebra packages, like Mathematica, Maple, or Sage, that let us do roughly what we would do on pen and paper, but with the speed and efficiency of a computer. Others program in more normal programming languages: C++, Python, even Fortran, making programs that can calculate whatever they are interested in.

Sometimes we read. With most of our field’s papers available for free on arXiv.org, we spend time reading up on what our colleagues have done, trying to understand their work and use it to improve ours.

Sometimes we talk. A paper can only communicate so much, and sometimes it’s better to just walk down the hall and ask a question. Conversations are also a good way to quickly rule out bad ideas, and narrow down to the promising ones. Some people find it easier to think clearly about something if they talk to a colleague about it, even (sometimes especially) if the colleague isn’t understanding much.

And sometimes, of course, we do all the other stuff. We write up our papers, making the diagrams nice and the formulas clean. We teach students. We go to meetings. We write grant applications.

It’s been said that a theoretical physicist can work anywhere. That’s kind of true. Some places are more comfortable, and everyone has different preferences: a busy office, a quiet room, a cafe. But with pen and paper, a computer, and people to talk to, we can do quite a lot.

The Road to Reality

I build tools, mathematical tools to be specific, and I want those tools to be useful. I want them to be used to study the real world. But when I build those tools, most of the time, I don’t test them on the real world. I use toy models, simpler cases, theories that don’t describe reality and weren’t intended to.

I do this, in part, because it lets me stay one step ahead. I can do more with those toy models, answer more complicated questions with greater precision, than I can for the real world. I can do more ambitious calculations, and still get an answer. And by doing those calculations, I can start to anticipate problems that will crop up for the real world too. Even if we can’t do a calculation yet for the real world, if it requires too much precision or too many particles, we can still study it in a toy model. Then when we’re ready to do those calculations in the real world, we know better what to expect. The toy model will have shown us some of the key challenges, and how to tackle them.

There’s a risk, working with simpler toy models. The risk is that their simplicity misleads you. When you solve a problem in a toy model, could you solve it only because the toy model is easy? Or would a similar solution work in the real world? What features of the toy model did you need, and which are extra?

The only way around this risk is to be careful. You have to keep track of how your toy model differs from the real world. You must keep in mind difficulties that come up on the road to reality: the twists and turns and potholes that real-world theories will give you. You can’t plan around all of them, that’s why you’re working with a toy model in the first place. But for a few key, important ones, you should keep your eye on the horizon. You should keep in mind that, eventually, the simplifications of the toy model will go away. And you should have ideas, perhaps not full plans but at least ideas, for how to handle some of those difficulties. If you put the work in, you stand a good chance of building something that’s useful, not just for toy models, but for explaining the real world.

Science, the Gift That Keeps on Giving

Merry Newtonmas, everyone!

You’ll find many scientists working over the holidays this year. Partly that’s because of the competitiveness of academia, with many scientists competing for a few positions, where even those who are “safe” have students who aren’t. But to put a more positive spin on it, it’s also because science is a gift that keeps on giving.

Scientists are driven by curiosity. We want to know more about the world, to find out everything we can. And the great thing about science is that, every time we answer a question, we have another one to ask.

Discover a new particle? You need to measure its properties, understand how it fits into your models and look for alternative explanations. Do a calculation, and in addition to checking it, you can see if the same method works on other cases, or if you can use the result to derive something else.

Down the line, the science that survives leads to further gifts. Good science spreads, with new fields emerging to investigate new phenomena. Eventually, science leads to technology, and our lives are enriched by the gifts of new knowledge.

Science is the gift that keeps on giving. It takes new forms, builds new ideas, it fills our lives and nourishes our minds. It’s a neverending puzzle.

So this Newtonmas, I hope you receive the greatest gift of all: the gift of science.

Life Cycle of an Academic Scientist

So you want to do science for a living. Some scientists work for companies, developing new products. Some work for governments. But if you want to do “pure science”, science just to learn about the world, then you’ll likely work at a university, as part of what we call academia.

The first step towards academia is graduate school. In the US, this means getting a PhD.

(Master’s degrees, at least in the US, have a different purpose. Most are “terminal Master’s”, designed to be your last degree. With a terminal Master’s, you can be a technician in a lab, but you won’t get farther down this path. In the US you don’t need a Master’s before you apply for a PhD program, and having one is usually a waste of time: PhD programs will make you re-take most of the same classes.)

Once you have a PhD, it’s time to get a job! Often, your first job after graduate school is a postdoc. Postdocs are short-term jobs, usually one to three years long. Some people are lucky enough to go to the next stage quickly, others have more postdoc jobs first. These jobs will take you all over the world, everywhere people with your specialty work. Sometimes these jobs involve teaching, but more often you just do scientific research.

In the US system, If everything goes well, eventually you get a tenure-track job. These jobs involve both teaching and research. You get to train PhD students, hire postdocs, and in general start acting like a proper professor. This stage lasts around seven years, while the university evaluates you. If they decide you’re not worth it then typically you’ll have to leave to apply for another job in another university. If they like you though, you get tenure.

Tenure is the first time as an academic scientist that you aren’t on a short-term contract. Your job is more permanent than most, you have extra protection from being fired that most people don’t. While you can’t just let everything slide, you have freedom to make more of your own decisions.

A tenured job can last until retirement, when you become an emeritus professor. Emeritus professors are retired but still do some of the work they did as professors. They’re paid out of their pension instead of a university salary, but they still sometimes teach or do research, and they usually still have an office. The university can hire someone new, and the cycle continues.

This isn’t the only path scientists take. Some work in a national lab instead. These don’t usually involve teaching duties, and the path to a permanent job is a bit different. Some get teaching jobs instead of research professorships. These teaching jobs are usually not permanent, instead universities are hiring more and more adjunct faculty who have to string together temporary contracts to make a precarious living.

I’ve mostly focused on the US system here. Europe is a bit different: Master’s degrees are a real part of the system, tenure-track doesn’t really exist, and adjunct faculty don’t always either. Some particular countries, like Germany, have their own quite complicated systems, other countries fall in between.

Academia Has Changed Less Than You’d Think

I recently read a biography of James Franck. Many of you won’t recognize the name, but physicists might remember the Franck-Hertz experiment from a lab class. Franck and Hertz performed a decisive test of Bohr’s model of the atom, ushering in the quantum age and receiving the 1925 Nobel Prize. After fleeing Germany when Hitler took power, Franck worked on the Manhattan project and co-authored the Franck Report urging the US not to use nuclear bombs on Japan. He settled at the University of Chicago, which named an institute after him.*

You can find all that on his Wikipedia page. The page also mentions his marriage later in life to Hertha Sponer. Her Wikipedia page talks about her work in spectroscopy, about how she was among the first women to receive a PhD in Germany and the first on the physics faculty at Duke University, and that she remained a professor there until 1966, when she was 70.

Neither Wikipedia page talks about two-body problems, or teaching loads, or pensions.

That’s why I was surprised when the biography covered Franck’s later life. Until Franck died, he and Sponer would travel back and forth, he visiting her at Duke and she visiting him in Chicago. According to the biography, this wasn’t exactly by choice: they both would have preferred to live together in the same city. Somehow though, despite his Nobel Prize and her scientific accomplishments, they never could. The biography talks about how the university kept her teaching class after class, so she struggled to find time for research. It talks about what happened as the couple got older, as their health made it harder and harder to travel back and forth, and they realized that without access to their German pensions they would not be able to support themselves in retirement. The biography gives the impression that Sponer taught till 70 not out of dedication but because she had no alternative.

When we think about the heroes of the past, we imagine them battling foes with historic weight: sexism, antisemitism, Nazi-ism. We don’t hear about their more everyday battles, with academic two-body problems and stingy universities. From this, we can get the impression that the dysfunctions of modern academia are new. But while the problems have grown, we aren’t the first academics with underpaid, overworked teaching faculty, nor the first to struggle to live where we want and love who we want. These are struggles academics have faced for a long, long time.

*Full disclosure: Franck was also my great-great-grandfather, hence I may find his story more interesting than most.

When to Trust the Contrarians

One of my colleagues at the NBI had an unusual experience: one of his papers took a full year to get through peer review. This happens often in math, where reviewers will diligently check proofs for errors, but it’s quite rare in physics: usually the path from writing to publication is much shorter. Then again, the delays shouldn’t have been too surprising for him, given what he was arguing.

My colleague Mohamed Rameez, along with Jacques Colin, Roya Mohayaee, and Subir Sarkar, wants to argue against one of the most famous astronomical discoveries of the last few decades: that the expansion of our universe is accelerating, and thus that an unknown “dark energy” fills the universe. They argue that one of the key pieces of evidence used to prove acceleration is mistaken: that a large region of the universe around us is in fact “flowing” in one direction, and that tricked astronomers into thinking its expansion was accelerating. You might remember a paper making a related argument back in 2016. I didn’t like the media reaction to that paper, and my post triggered a response by the authors, one of whom (Sarkar) is on this paper as well.

I’m not an astronomer or an astrophysicist. I’m not qualified to comment on their argument, and I won’t. I’d still like to know whether they’re right, though. And that means figuring out which experts to trust.

Pick anything we know in physics, and you’ll find at least one person who disagrees. I don’t mean a crackpot, though they exist too. I mean an actual expert who is convinced the rest of the field is wrong. A contrarian, if you will.

I used to be very unsympathetic to these people. I was convinced that the big results of a field are rarely wrong, because of how much is built off of them. I thought that even if a field was using dodgy methods or sloppy reasoning, the big results are used in so many different situations that if they were wrong they would have to be noticed. I’d argue that if you want to overturn one of these big claims you have to disprove not just the result itself, but every other success the field has ever made.

I still believe that, somewhat. But there are a lot of contrarians here at the Niels Bohr Institute. And I’ve started to appreciate what drives them.

The thing is, no scientific result is ever as clean as it ought to be. Everything we do is jury-rigged. We’re almost never experts in everything we’re trying to do, so we often don’t know the best method. Instead, we approximate and guess, we find rough shortcuts and don’t check if they make sense. This can take us far sometimes, sure…but it can also backfire spectacularly.

The contrarians I’ve known got their inspiration from one of those backfires. They saw a result, a respected mainstream result, and they found a glaring screw-up. Maybe it was an approximation that didn’t make any sense, or a statistical measure that was totally inappropriate. Whatever it was, it got them to dig deeper, and suddenly they saw screw-ups all over the place. When they pointed out these problems, at best the people they accused didn’t understand. At worst they got offended. Instead of cooperation, the contrarians are told they can’t possibly know what they’re talking about, and ignored. Eventually, they conclude the entire sub-field is broken.

Are they right?

Not always. They can’t be, for every claim you can find a contrarian, believing them all would be a contradiction.

But sometimes?

Often, they’re right about the screw-ups. They’re right that there’s a cleaner, more proper way to do that calculation, a statistical measure more suited to the problem. And often, doing things right raises subtleties, means that the big important result everyone believed looks a bit less impressive.

Still, that’s not the same as ruling out the result entirely. And despite all the screw-ups, the main result is still often correct. Often, it’s justified not by the original, screwed-up argument, but by newer evidence from a different direction. Often, the sub-field has grown to a point that the original screwed-up argument doesn’t really matter anymore.

Often, but again, not always.

I still don’t know whether to trust the contrarians. I still lean towards expecting fields to sort themselves out, to thinking that error alone can’t sustain long-term research. But I’m keeping a more open mind now. I’m waiting to see how far the contrarians go.

Knowing When to Hold/Fold ‘Em in Science

The things one learns from Wikipedia. For example, today I learned that the country song “The Gambler” was selected for preservation by the US Library of Congress as being “culturally, historically, or artistically significant.”

You’ve got to know when to hold ’em, know when to fold ’em,

Know when to walk away, know when to run.

Knowing when to “hold ’em” or “fold ’em” is important in life in general, but it’s particularly important in science.

And not just on poker night

As scientists, we’re often trying to do something no-one else has done before. That’s exciting, but it’s risky too: sometimes whatever we’re trying simply doesn’t work. In those situations, it’s important to recognize when we aren’t making progress, and change tactics. The trick is, we can’t give up too early either: science is genuinely hard, and sometimes when we feel stuck we’re actually close to the finish line. Knowing which is which, when to “hold” and when to “fold”, is an essential skill, and a hard one to learn.

Sometimes, we can figure this out mathematically. Computational complexity theory classifies calculations by how difficult they are, including how long they take. If you can estimate how much time you should take to do a calculation, you can decide whether you’ll finish it in a reasonable amount of time. If you just want a rough guess, you can do a simpler version of the calculation, and see how long that takes, then estimate how much longer the full one will. If you figure out you’re doomed, then it’s time to switch to a more efficient algorithm, or a different question entirely.

Sometimes, we don’t just have to consider time, but money as well. If you’re doing an experiment, you have to estimate how much the equipment will cost, and how much it will cost to run it. Experimenters get pretty good at estimating these things, but they still screw up sometimes and run over budget. Occasionally this is fine: LIGO didn’t detect anything in its first eight-year run, but they upgraded the machines and tried again, and won a Nobel prize. Other times it’s a disaster, and money keeps being funneled into a project that never works. Telling the difference is crucial, and it’s something we as a community are still not so good at.

Sometimes we just have to follow our instincts. This is dangerous, because we have a bias (the “sunk cost fallacy”) to stick with something if we’ve already spent a lot of time or money on it. To counteract that, it’s good to cultivate a bias in the opposite direction, which you might call “scientific impatience”. Getting frustrated with slow progress may not seem productive, but it keeps you motivated to search for a better way. Experienced scientists get used to how long certain types of project take. Too little progress, and they look for another option. This can fail, killing a project that was going to succeed, but it can also prevent over-investment in a failing idea. Only a mix of instincts keeps the field moving.

In the end, science is a gamble. Like the song, we have to know when to hold ’em and fold ’em, when to walk away, and when to run an idea as far as it will go. Sometimes it works, and sometimes it doesn’t. That’s science.