Quantum Diaries

I’ve lived and worked at CERN for two years, and now I’m going away. This is a common story.

When I first moved here from Stony Brook, NY, I was a fresh PhD Candidate who had just finished up a hardware project, acquired a young new advisor, and started learning C++ in a serious way. I had no idea what research I would be doing at CERN.

I spent a couple of months thoroughly lost: in red tape and French bureaucracy; in a labyrinthine, nonsensically-numbered hodge-podge of office buildings and industrial warehouses; in a social scene dominated by physics and physicists; in supermarkets that close at 7pm, roads that go roundabout-to-roundabout, and conversations that start in one language and inevitably end in another.

With the return of beam in the LHC (after a well-publicized accident) came a new sense of urgency and purpose, for both CERN and myself. I remember how the pace of life on-site noticeably picked up, all of us marveling at the screens showing the beams’ status as we sped by on the way to a meeting, or a coffee. I got involved in the ATLAS 3D Pixel Collaboration. I took midnight shifts to help with data-taking on the prototype silicon sensors, jumped into software development on particle track reconstruction, and got started on a short-term analysis project to determine the hit resolution of the sensors, complete with smart, Swedish-made post-doc.

As with most “short-term” projects at CERN, mine became long-term, hindered by unforeseen complications in the data and, at all steps along the way, the analysis framework in which many of us do our work: ROOT. Complaining about this distinctly sadistic piece of software became a favorite pastime! And still is. In the end, my analysis was somewhat inconclusive — so it goes.

Fortunately, my group at Stony Brook was in the process of initiating a more cohesive, aggressive research program, and I got drafted by my advisor into an Exotics search for second-generation leptoquarks. I was able to help build our analysis framework from the ground up, and in the process learned how to code in Python. It was a revelation. Meanwhile, the LHC had begun its 2010 run, and it seemed like our dataset was doubling every other day! These were very exciting times. Nobody knew what we might find in all this data, be it supersymmetry or a Higgs Boson or, maybe maybe maybe, some sort of leptoquark.

The analysis went roughly as expected: An endless series of incremental changes, talks in weekly meetings, unanticipated crises, deadlines, plots, emails, interpersonal conflicts, and soul-crushing hoops to jump through on the way to publication. It was an awful lot of work and stress, all for the best null result in the world. Huzzah. To be honest, it was a bit disheartening that the LHC experiments hadn’t dropped a bombshell on the scientific community, like OPERA’s super-liminal neutrinos measurement or the discovery of a true “goldilocks” planet, but it certainly felt like something was just around the corner.

I went to Cambridge for a conference and Stanford for summer school, dated a native Genevois, visited Paris and Berlin and Rome, got trained as an ATLAS Pixel shifter, celebrated the holidays with friends and family back home, and had the chance to visit the ATLAS detector down in its pit during the annual winter shutdown.

For the LHC’s 2011 run, I basically picked up where I left off with my leptoquark search; it was meant to be a straightforward update. But, of course, nothing is ever as easy as we expect: the beams’ increased luminosity meant much more data but also challenges in simulating and effectively filtering out all the extra events; new software releases fixed some bugs but introduced others; new collaborators meant new interpersonal conflicts (or is it just me?!); and improvements to the analysis itself seldom worked out as nicely, or quickly, as we would have liked. Deadlines have a way of flying by… but that’s research! Or so I’m told.

Meanwhile, I visited New Orleans, Helsinki, and Zurich, got involved with LGBT CERN, filmed, edited, and co-produced a particle-physics-themed zombie movie, and started writing my thesis. ATLAS kept taking more and more data — until just a few days ago.

And that’s it! Two years at CERN. In less than a week, I’m going away — back to New York, to write my thesis full-time, hunt for jobs (decide what sort of job I’m hunting for…), and graduate. It’s hard looking back on a chapter in your life and assessing it for lessons learned and enduring connections made, accomplishments and moments of zen. While I have my share of regrets and what-ifs (why, why did I never finish learning French?!), I feel like I’ve come a long way these past two years. From slightly jaded, I-can’t-wait-to-start-my-analysis junior grad student to thoroughly jaded, I-can’t-wait-to-finish-my-analysis-and-get-out-of-here senior grad student. All these gray hairs must count for something.

Heads-up: This blog is about to get hardcore, thesis-style. But first… an international move, and a long series of good-byes.

Burton

Well, I’ve finally started thinking about starting to start writing my PhD thesis. I’m told this is a big deal.

It is known that asking a grad student about the status of his or her thesis, or when he or she plans to graduate, is bad form — practically taboo — and until recently, I applied this golden rule equally to myself as well as people whom I didn’t want to aggravate. Alas, those care-free days are over! The harsh onset of fall (I see the clouds gathering for their annual, six-month layover), the obvious absence of summer students (CERN is notable for many reasons, not least of which is that, at the start of September, all the undergrads leave), and the near-completion of what I consider a polished analysis (my advisor would probably disagree) have all conspired to get me thinking more long-term. And this brought me, kicking and screaming, to my non-existent thesis.

Over the years, and particularly in the last week or so, I’ve accumulated a fair amount of “thesis advice” from more senior grad students and post-docs. Fun fact: Nobody wants to talk about their thesis while they’re writing it (unless to complain about how totally unfair life is), but once they finish, they suddenly become very eager to share their experience with others. This is what I have learned so far:

- Don’t start writing your thesis until you’ve completely finished your analysis and, ideally, published a paper on it. Then start with the analysis section, using your paper as a guide. Try not to plagiarize yourself.
- Write the experimental section of your thesis in the second year of grad school. What you learn about your detector will serve you well in your analysis. Try not to plagiarize others, as there are only so many ways to say “The LHC is a proton-proton collider with a center-of-mass energy of 7 TeV.”
- Why not dance it out?
- The hardest part of your thesis is just getting started. (CHECK!)
- The hardest part of your thesis is writing the introduction, so save it for last.
- The hardest part of your thesis is getting your committee to agree on a date for you to finish.
- Hide “Easter eggs” throughout your thesis, such as figures with dinosaurs used for scale, or phrases not normally seen in scientific writing. Find out how closely your advisor is reading.
- You can write a thesis in about six months.
- You can write a thesis in about three months.
- “I wrote my thesis in two months.”
- “I have to write my thesis in the next five weeks.”
- Start applying for jobs while you write your thesis, so by the time you’re done, you’ll have something to move on to. Don’t graduate before you have a job lined up — you can always delay! Some people do this for years…
- Your thesis title is important! Choose with care.

Okay. So what I’ve learned so far is this: Every thesis, and every person writing a thesis, is unique. What works for one doesn’t work for all. Inevitably, necessarily, you have to find a way through it that works for you, and doesn’t drive you (completely) crazy in the process. Talking to others — perhaps blogging about it? — can help, but in the end, your thesis is what you yourself make out of it.

Whew. The good news is, I have a working title! Leptoquarks: The Particles That Go Both Ways.

– Burton

I recently had an encounter with an unusual reporter. He had been roaming about CERN in search of a story, and by chance that brought him far afield to the test beam control room at CERN’s SPS North Area where I was spending the day on shift with a colleague. Because of technical difficulties (or was it a worker’s strike?), the beam was off, and our devices under test weren’t taking any data, so when the reporter came by and asked to speak with us, it was an emphatic “Yes, but I need coffee first.”

Now, to be fair, it wasn’t the reporter himself that was unusual, it was the story he sought: “The Sound of CERN.” Um, what? This gentleman had been taking field recordings all over in hopes of piecing together an aural representation of the world’s largest particle physics laboratory, and now he wished to speak with us. After seeing — hearing! — so much of CERN’s infrastructure and equipment, all of it humming whirring beeping pulsing clicking, he had decided to widen the scope of his project to include the scientists inhabiting this soundscape. Sounded reasonable to me.

After my colleague rather eloquently explained the work we were doing at the test beam and why it mattered in the grander scheme of things, our conversation moved on to the reporter’s work. Why sound? Well, it’s awfully difficult for people to relate to fundamental physics on a visceral level, and intellectually isn’t much easier — there’s a reason we spend so much time in grad school! But understanding is facilitated whenever you can associate a concrete bit of sensory information with an abstract concept. What sound does a Higgs boson make when it decays into two W bosons? What color is an electron? (No, I’m not talking quantum chromodynamics…) If you could reach out and touch a proton, would it be soft like a plushy, or hard like a billiard, or squishy like a hard-boiled egg?

Or, in this reporter’s case, What does CERN sound like? Well, I will give you some hints — in words, until I acquire the means to record the audio myself:

• like a room full of computers, fans blowing hard while merciless physicsts bang on their keyboards
• like a 1.6 Tesla Morpugo magnet thrumming softly while a beam of 180 GeV pions passes through it at close to the speed of light
• like a flock of sheep grazing in a field 100 m directly above the low-energy end of CERN’s accelerator complex, the bells on their necks chiming pleasantly
• like the coffee machines in R1 dispensing liquid energy to tired grad students
• like the electronic tone emanating from television screens once every forty seconds, indicating when the SPS is spilling beam your way
• like water cascading down the inside of the ATLAS cooling towers at Point 1 on the LHC
• like two people chatting excitedly as you pass them by, “… saw good agreement in the high-pT tails, but something strange …”
• like this, maybe

Until next time — keep your eyes open and your ears tuned!

Burton

Editor’s note: For those who missed it, here’s “So You Want to Discover a New Particle (Pt. 1)“

Hi, Readers!

I went away for a bit to pursue some other extra-curriculars, but now I’m back — and the blog has a new home! Perhaps a new audience as well. Since this (my first) Quantum Diaries post is actually the second part in a series, and I am a shamefully sporadic poster, I suppose I owe you a synopsis:

• The Standard Model is boring, but new particles are not.
• If you’d like to discover a new particle, theorists have conveniently provided you with an expansive menu of possibilities.
• Choose your particle with great care. This involves physics-, politics-, and plushy-based considerations.

So, let’s assume you’ve done some research, sent some emails, formed (or joined) a research group, and selected the new particle you’d like to discover. Congrats. Now what? Well, your particle hasn’t been found yet probably because… it isn’t easy to find. There could be multiple reasons for this:

• It has a tiny production cross-section (the LHC produces it only once in a blue moon, as opposed to millions of times per second).
• It looks like other, already-discovered particles, so it blends right in with the (so boring!) Standard Model.
• It is produced and/or decays in such a way that our particle detectors have a tough time seeing it and measuring its properties.
• It is shielded from present detection by a future version of itself, traveling back in time to thwart your research plans (see here or here).

With the notable exception of that last one, the practical consequence is that you’re essentially looking for a needle in a haystack. Only sometimes the needle looks an awful lot like hay. And in some cases, the needle could actually be lying outside the haystack, in a place you aren’t even looking. We call this hay “background”; the needle, “signal.” Before you can even think about looking for your needle of interest, you first have to get to know the hay.

In general, the bulk of your background is made up of Standard Model processes that resemble your signal but are produced far more copiously. As an added complication, this can be split into two groups: “physics” backgrounds, which actually involve the same “final state” particles as your signal  — that is, those that are measured by the detector and then used to reconstruct the event; and “instrumental” backgrounds, which involve a different final state that is mis-measured by the detector such that it “fakes” looking like your signal. To make matters worse, it’s possible that non-Standard Model (new!) particles similar to your own could interfere with your analysis, adding an entirely theoretical component to your total background. Insidious! You must be aware that yours isn’t necessarily the only needle in this haystack.

(… As is often the case, one person’s trash is another person’s treasure. )

Assuming you did the research before choosing your particle, you already know the main backgrounds for your signal. Great! Now to study these background processes in a systematic, controlled manner, we use what are called “Monte Carlo” (MC) simulations, aka “fake data.” Although I won’t go into detail about how the MC is generated, I will say that it’s a sophisticated simulation representing our best guess at how the data should look based on both theoretical and experimental constraints. It involves random numbers, probability distributions, lots of computing power, and magic.

Most likely your backgrounds have already been simulated, and all you need to do is get them. Do so. Practically speaking, this is person- and experiment-specific, so I offer no details, but let’s just assume that you now have access to the MC simulated datasets for your main backgrounds — and so the fun can begin! Except it is now 2am, and my workweek is about to start. Nuts!

Next time: Examining your simulated haystack, comparing it to your simulated needle, and finding ways to effectively separate the two with an algorithmic baler.

– Burton