Quantum Diaries

Later this week another generation of academics will finalize their decisions about which graduate programs to attend next year—many congratulations to all of you soon-to-be grad students who will join us in the trenches at the frontier of human knowledge.

Unlike undergraduate life which has a well-known idealization in Animal House (or the TV series Greek), grad school doesn’t get much publicity other than the sardonic (and delightful) PhD comics. I wanted to take a moment to share some observations of what graduate school is actually like, with the usual caveat that this is just my personal perspective—each person has their own experience. (Grads and former grads: feel free to add to the discussion in the comments section.)  Without further ado, here are five observations about grad school.

[All illustrations are my own and brought back memories of my failed first-year aspirations of becoming a chalkboard Banksy.]

1. Grad school: more like Zelda than Mario

College is a lot like a Super Mario Bros. video game. You wake up, go to class, do the homework that’s assigned, and study the chapters you were told to, and rock the exam after practicing on past exams. Sure, sometimes you have to try a few times before you can make that jump right at the end of the level, but at each step it was clear what you had to do.

You may have to rethink your measures of success and re-evaluate the tools you need to get there.

Grad school is different. You can’t just wake up in the morning and do all the stuff that you know you have to do—because research is precisely about figuring out what to do when it is not clear at all what the next step is. In this respect grad school is more like playing a Legend of Zelda video game.

Unlike coursework, research is about open questions. Usually these questions are still open for a good reason: they’re hard! You won’t have an answer key in the back of the book or a TA’s office hours to show you the trick. There’s no road map; you need to carve out your own path and figure out what tools you need to develop to move forward. Sometimes there will be dead ends and you’ll have to back-track, but in the end this can be a rewarding experience. You don’t remember Mario Bros. for how hard it was to stomp on Bowser’s head, but you do remember all the time you spent trying to figure out the puzzle to break into that one dungeon so you could rescue Zelda.

2. Get paid to do what you love—just not very much

One of the perks of grad school that often surprises non-academics is that yes, you get paid to do science! (Usually this is associated with doing some teaching.) In a difficult economy and with undergraduate student loans soaring, this is a welcome respite from large tuition bills and reliance on parental support. On the other hand, don’t expect to be drowning in disposable income.

Just be careful not to fall into the trap of comparing your income to your college friends who got ‘real’ jobs. That being said, you’ll have health insurance, be able afford an apartment and food, and most importantly, you’ll have the freedom to work on what you want and how (and when) you want to.

3. Somewhere between being a kid and a grown up

Maybe it’s just me (and I really hope not), but part of being a grad student is living precipitously on the edge of growing up. My personal experience has been full of office pranks, jokes, and the “child-ish” silliness that sometimes comes with the “child-like” curiosity that is at the heart of being a scientist. At the same time, one has to balance one’s aforementioned budget, keep pushing the less-fun parts of projects, and be responsible for the direction and content of one’s research.

It’s worth mentioning that sometimes it can feel like the rest of the world is growing up way faster than you are. In addition to earning much more than you, your old high school friends will be getting married and starting families—the latter of which is something which can be difficult (though not impossible) as a young academic.

High school reunion can be a reminder that everyone else has "grown up" while you're still in school.

In a larger sense, grad students are fledgling scientists, apprentices to professors who train their academic offspring. And just like biological offspring, it’s often the case that the apple doesn’t fall far from the tree—after all, your grad school mentors are the ones who teach you how to think about your science, how to grapple with hard problems, and (very important) how to interact with other scientists.

4. Sometimes the next step is a step back

Whatever discipline you’re in, and no matter how smooth things seem to be going for the other students, graduate school is hard. (So are professional schools and real grown up life, for that matter.) Sure, most people are prepared to spend their PhD working on hard research questions. What people don’t usually expect is that often it’s actually everything else that makes a PhD hard: balancing your work with the rest of your life.

“Rest of your life?” It’s a cliche that grad students don’t have lives outside of their labs, and it’s completely wrong. The most successful students—both in undergrad and grad school—are often the ones who have something else that they’re passionate about and that is totally unrelated to their work. Maybe music or art, maybe a particular sport, or a social activity (blogging?)… something to dive into and keep you sane when work isn’t going well—and there will be times when work is not going well.

In many ways the defining moments in graduate school aren’t when research is going well, but rather those times when it feels like everything is crumbling beneath you. Those moments when you feel like you should chain yourself to your desk until everything works? Those are usually the times when the best thing you can do is to take a step back for a bit and relax.

It’s crucially important to recognize that things will not go as smoothly as you plan. Consider the following very-scientific graph of happiness over time.

Actually, the pointy curves come from something I've been working on (with different labels).

Naively, one might imagine that grad school is a period where you just keep learning more and more about something you enjoy until you steadily become the world expert on something really important. What actually happens is that you spend most of your time grappling with the frustrating problems that prevented other people from doing this research before you. Then, with some luck, there are brief moments of ecstatic clarity where you make progress: you’ll remember why you’re doing a PhD and all will be right in the world… for maybe a day or two, at which point you’ll come up to the next hurdle that you’ll have to struggle with.

This perpetual struggle at the heart of research can be hard to swallow, especially for those to whom undergraduate coursework came fairly naturally. The feelings of self-doubt that often arise are so common that it even has a name, impostor syndrome, wherein people feel like their struggles indicate that they are not ‘good enough’ to be a PhD student and their university made a big mistake accepting them to such a program. Just remember: all this is normal! (See Zelda analogy above.)

Footnote: Not every PhD becomes an academic!

I wanted to address something related to this: not every grad student goes on to become an academic, and that this is okay. Somehow it’s almost taboo to talk about going off into industry after grad school instead of continuing to become a postdoc and then a faculty member somewhere—even though there are clearly fewer postdoc positions than grad students, and fewer still faculty hires. (I think it’s great that Burton’s mentioned this in recent posts.)

While there is something special about spending your life pursuing fundamental science, but that doesn’t mean it’s the right path for everyone. And this is not to say that some people “aren’t cut out” for research or that their PhD was not well spent: I’ve seen some truly special and talented individuals with bright academic futures decide that they would be happier applying the skills they developed on something else. And that’s great—one of the reasons why our country invests in fundamental research is to support a highly skilled workforce doing exciting things outside of the ivory tower.

I’ve had difficult conversations with multiple young academics who have struggled to weigh their passion for science against pragmatism: what if they can’t find a job sufficiently close to their spouse? What if they want to settle down and start a family rather than having to bounce between temporary grad and postdoc positions? What if they need to take care of ailing parents and cannot hold off until the indeterminate future to secure that kind of financial stability?

Fortunately, a PhD is something which generally translates into marketable skills “in the real world,” and I think it’s important for those in academia to recognize that sometimes good people will leave the field for good reasons.

5. How to be a good graduate student

I’d like to wrap up by once again addressing the next generation of grad students with some unsolicited advice from someone crawling towards the light at the end of his own PhD tunnel.

1. Find good mentors. Your adviser will have a big impact on your PhD and career, but you should also make a point to find mentors in the form of other faculty, postdocs, and graduate students. Learn as much as you can from the people around you, especially when they can offer advice that they had to learn the hard way.

2. Persistence and enthusiasm goes a long way. You can expect to run into setbacks and roadblocks. One of the most useful things you can develop is an enthusiasm for your work and the persistence to keep pushing even when things feel futile. Persistence and enthusiasm can make up for a lot of things: lost sleep, raw intelligence (when you feel like everyone else is smarter than you), gaps in your problem-solving toolbox, etc.

3. Learn how to communicate. One of the cornerstones of science is being able to effectively communicate your work to others. Learn how to effectively read and write papers, and learn how to give good talks about your research.

4. Use your freedom wisely. For the most part, people won’t tell you how to spend your time. It’ll be up to you to work on what you want, when you want to, and however you think will best solve the problem. Just be careful that you’re not using all of this extra rope to hang yourself. Find the right balance of work and play that works for you.

5. Science is social. There is synergy in academia. People wonder what theorists do all day long since it seems like all we do is to think up silly ideas—we spend most of the day talking to each other. Ideas are meant to be bounced off of one another: revised, refined, and re-assessed. Don’t fall into the bad habit of hiding in a hole in the ground until you find the answer—make use of the community around you!

Science is a team sport, it helps to figure this out earlier rather than later.

While we’re on this note—take time to be part of the science community in your field. There are some scientists who develop their best ideas while hiking with friends or at a pub after a conference.

6. Let it be fun. Despite all the things one has to struggle with from research to personal life and everything in-between, grad school is a special time in your life; enjoy it.

As has been reported elsewhere, the LHC is off and running again. Yesterday we saw the first stable beam collisions of the year. So far, collision rates are extremely small, and the detectors are just being roused from their winter slumber, so there is certainly no physics news to report yet. Over the next few days and weeks, the LHC will, fill by fill, increase the number of proton bunches circulating in the machine, and thus the collision rates. Meanwhile, the experiments will check that all of the detector elements are functioning and calibrated, which will allow us to get back to our full menu of work.

So what can we expect in the year to come? Here are a few things that I could think of.

OK, readers — what are you expecting from the LHC this year? We welcome your comments.

I’m currently writing you from Atlanta, GA, where I’ve been attending the APS April Meeting on particle, nuclear, and astro physics (this year’s theme is “100 Years of Cosmic Ray Physics”). Officially I’m here to give a talk on my thesis research (the leptoquark search I’ve been alluding to for some time but still haven’t fully explained — patience, it’s coming!), but really I’m here to network and interact with other physicists re: getting a job post-PhD.

Self-promotion comes naturally to some people… Not me. I prefer to casually undersell, which is a bit of a problem given that resumes are crafted to formally oversell a person. Still working on my elevator pitch.

At any rate, the conference has provided a number of opportunities for an almost-graduated job-hunter to explore interesting intellectual avenues and make connections with advice-givers and potential employers. I was fortunate to attend a panel discussion held specifically for grad students on the topic of careers, networking, and carrying out a job search. In addition to providing an occasion for free food, the panel also imparted some very useful wisdom:

• Create individualized resumes and cover letters for each position you apply to. Whoever reads your application will know if you’ve done your homework or, alternatively, if you’re trying to catch a bunch of different fish with the same net. Heads-up: Most fish will slip through.
• Communication is essential. You can’t expect employers to infer why it is that they should hire you, you have to tell them, in clear, understandable terms.
• Networking is also essential. People often get jobs because they “know somebody who knows somebody,” so it is beneficial to your job search to talk about your qualifications and what you’re looking for with as many people as possible. If that sounds obnoxious, well… indeed it is, but don’t let that stop you!
• Maintain interests and passions outside of those in your particular sub-field. Physicsts are “all-arounders” that perform well in a variety of tasks; a surprisingly large fraction of physics PhDs get jobs totally unrelated to their thesis research or, in fact, physics. Being well-rounded is a strength to be valued and emphasized.

Tonight is the last night of the conference. Earlier today I nailed my talk, even getting a couple general laughs from a room full of physicists (not easy!) so now I just have to find myself an interested future employer or somebody who knows him. I’ll be in the hotel bar, working on my pitch and resumes — let’s chat!

— Burton

Last week Cornell hosted the sixth “Monte Carlo Tools for Beyond the Standard Model” mini-workshop and I thought it was a terrific success. Here “Monte Carlo” refers to the computer simulation techniques used to solve difficult problems such as the behavior of high energy particles at the LHC. The name is a reference to the famous casino in Monaco since these methods are based on random sampling.

MC4BSM poster: if CMS reminds you of a roulette wheel, you may have a gambling problem.

Playing Dice with the LHC?

It’s not what it sounds like. At first glance talking about ‘random sampling’ might make it sound like someone doesn’t know what they’re doing. It’s actually quite the opposite. The theory of hadron colliders (which is mostly quantum chromodynamics) is well established, but actual calculation requires compromise.

I won’t go into details, but the rough sketch is that a high energy collision at the LHC does not look like the nice Feynman diagrams that we’ve been drawing (and that we can calculate easily):

Nope. In fact, the events look much, much more complicated (from S. Hoeche’s talk):

Needless to say, this is very difficult to calculate using pen and paper. In fact, the situation is even more difficult than it looks: many of the steps in this calculation require systematic approximations and are non-perturbative (hopeless to calculate in the usual method). There’s more: the above picture is just what happens when there’s a high energy collision in vacuum. We also have to model how all of that interacts with the detector to give a picture more like this:

It is practically impossible to calculate a closed form expression for the Standard Model prediction for the distribution of detector signatures. On the other hand, what we can do is imagine actually simulate particle production and decay at each step of the process so that the random evolution of the initial collision into the final detector signature follows the probability distribution of the “closed form expression” that we can’t write down. By doing this many times, we can determine the probability distribution simply by looking at the distribution of the Monte Carlo simulation.

This probably still sounds a little abstract—but it’s the analog of determining the interference pattern of the double slit system by actually doing the experiment with electrons and looking at the distribution of electron hits on the screen. Another nice example is to determine the area of a circle (or the value of π) by Monte Carlo.

Tools of the Trade

Suffice it to say that Monte Carlo is a very important tool in high energy physics. For example, the results of Monte Carlo studies are used to determine what sorts of events we should be looking at to find the cleanest signals for a Higgs or for new physics—this sort of thing is especially relevant since the rate of high energy events at the LHC is actually much larger than our bandwidth for recording data, so we need to be able to trigger on particular events that we think are worth a closer look.

On the more theoretical side, Monte Carlo gives us a handle for mapping models of new physics to experimental signatures. For example, if we see a definitive signal of a new particle outside of the Standard Model, how can we begin to determine whether it is a supersymmetric partner, an extra dimensional resonance, or something else?

In between theory and experiment, there’s a lot of hard work done ‘in the trenches’ to develop better tools (both theoretical and computational) to model quantum chromodynamics. Often times this is under appreciated in the field since the work is not glamorous enough to land in one of Dennis Overbye’s New York Times articles, but recently three of the leaders of this field received the 2012 Sakurai prize—congrats to Altarelli, Sjostrand, and Webber!

Theorists will wax poetic about espresso machines and long nights at a chalkboard, experimentalists will tell you what it’s like to jump into the world’s largest scientific apparatus (armed with a vacuum cleaner), but the truth of the matter is that we spent a lot of time running computer simulations. We rely on the subset of the community that develops and maintains these tools, and occasionally we hold workshops (such as MC4BSM) to learn the latest and greatest tools.

MC4BSM 2012

Probably the first mystery of the MC4BSM series of workshops is the strange logo:

Apparently the illustration was done by a professional artist who is a friend of one of the organizers. The interpretation still isn’t clear to me—though it’s been suggested that it represents the “elephant in the room” associated with the lack of training opportunities to learn Monte Carlo techniques. Alternately, it was also pointed out that it’s a different kind of “pink elephant.”

The workshops are geared toward an audience of theorists who don’t necessarily have a background in Monte Carlo methods. The “big idea” is connecting our models of new physics to experimental data (image from M. Perelstein’s slides):

The key to doing this efficiently has been to develop a pipeline of Monte Carlo tools which interface with one another and take a theorist’s model to something that can be compared to real data; one example of such a pipeline is (image from C. Duhr’s talk):

The ovals are different stages of computational tools—the first two or three stages can usually be done by hand by a careful graduate student. From there on out we really rely on the Monte Carlo tools available to us. The red text highlights common programs used to connect each step, while the green-ish text are common languages that are used to provide a standardized language for program to communicate with one another.

All of these programs are open source (though some depend on commercial software like Mathematica) and are developed by high energy physicists for high energy physicists.

Tutorial

The real highlight of the workshop were the two tutorial sessions where attendees had a chance to play with various programs in a hands-on environment. The whole point of the meeting, after all, is to learn how to use these tools. The tutorial session allow attendees to ask questions directly to the program developers and to build their own templates by solving a simple toy problem.

Unlike previous MC4BSM workshops, the organizers adopted a novel format for the tutorial sessions which I thought worked very well. Each participant brought their own laptop and had a choice choice of which chain of programs they would use to solve the toy problem:

Instead of having representatives from each program give a short talk about how to install and run their code, users were left to themselves to jump in head first with their colleagues and then flag own experts as needed. (The night before the workshop there was also a group installation session where people could work out kinks in getting specific programs to compile on specific operating systems.)

Several of the graduate students there got their first taste in going through the entire series of programs, while more senior researchers learned how to use alternate tools than the ones they’re used to.

The tutorial information is all available online for anyone who wants to follow along on their own. The material will eventually be made available as proceedings for the workshop; I think it will be a valuable resource for anyone interested in learning to use these tools.

Human vs. Machine

One of the running jokes at the workshop was that eventually we’d be able to select a few options in a smart phone app to cook up a model of new physics and then send it to a computing cluster to work out the detailed phenomenology—perhaps obviating the need for graduate students. However, one thing that computers cannot yet replace is the value of having face-to-face interactions with one’s colleagues.

I’ve said many times that physics is a social activity and the field progresses from the collaborative efforts of the entire community. Meetings like MC4BSM are more than just ways to learn new tools, but are also ways to catch up with friends and colleagues and bounce new ideas off one another.

One bright idea that was promptly shot down was a request to hold the next MC4BSM meeting at the Monte Carlo casino in Monaco.