When you work at an international lab, most of the people you meet and become friends with are only on a 1+1 or 2+1 postdoc, or need to go home every few months for exams, or are only out on a two week or two day assignment. People on permanent assignment also go to at least one conference each year. This does mean you get to meet lots of interesting people from interesting places, but also means your personal interactions also carry an explicit sense of instability. This is also true at any major university, but there’s sort of a communal experience via the academic year. Even though people make friends with students from different years, there are aways people with the same timescale as you. The instability is there, but there are enough people on the same schedule that I found it less isolating and unpredictable.

Here its a bit different, for better or worse. Students and postdocs normally arrive individually, adjust, work and leave on a schedule dictated by their particular institutions. When I first arrived I made a big effort to learn the language fast and make friends off site but, none the less, scientists who aren’t from this city still form the core of my friends. What’s kind of nice is that its like an extended vacation for many people. They come here with few or no local personal attachments or responsibilities, and view their time here both as a career opportunity and simply a great personal experience. Combine this with incessant welcoming parties, going away parties, housewarming parties, and someone-is-back-in-town-for-an-editorial-board-meeting-lets-get-battered parties, and you get a pretty good time. When I was still living at the university we had all these things, but they weren’t so frequent or uniformly spread in time. Then again, scheduling a regular card game or road trip on a weekend when everyone is in town is a bit tricky.

This week is an extreme example, but it gives you an idea. On Sunday, one friend got back in town for after being at her home univeristy for two months. She’ll only be here until the fall, I think. On Monday a friend I haven’t seen in a year came back for just two days to sit in on a meeting. Last night was the probably the last time I’ll play cards with two guys working on the ILC Alignment and Survey project, which recently lost its UK funding. Tonight I go see my a friend who used to be a postdoc for my group, who I haven’t seen since he moved to LHC business a year ago. Its great to see people graduating, getting new and exciting jobs and all, but its a bit sad at the same time. I also notice it when people ask how my thesis writing is going, and where I’m looking at postdocs.

I took a break today from thesis writing to attend the weekly computing seminar, since it had calorimetry in the title, which should supposedly be one of my skills. The slides aren’t posted yet, but whenever they are you can find them here. Mark Thomson was the speaker, who also has a few papers out on the same subject on the web, this one seems to have the most overlap with the talk.

The idea goes a bit like this: If you want to test new physics at a collider, you need to measure collision products very carefully. Specifically, testing electroweak sector models (all this higgs hubub) requires identifying and differentiating Z and W bosons real well. Single production Z and W’s both decay in to quark-antiquark pairs. We can’t see quarks in detectors directly, but we observe sprays of particles called jets which have basically the same energy and direction, etc. So they want to measure jets real well at the proposed and under-development International Linear Collider (ILC), and the main technique to do this that they are trying to develop is called Particle Flow Calorimetry (PFC).

A Generic Detector

Pedantic point: The muon in the above figure shouldn’t be arcing after leaving the solenoid, and definitely not after leaving the iron yoke.

We measure particles by the way they interact with our detector, and different particles interact in different ways. Two of the most important signatures of particles are tracks and calorimeter hits. Tracking chambers only measure charged particles, try to barely interact with them, and watch where they go in a magnetic field. Calorimeters do the opposite: they try to stop everything completely, and add up all the energy they absorbed. We can measure charged particles like electrons, protons, and charged pions very well with trackers, even better than with calorimeters. Photons have no charge, so we don’t see them in trackers, but interact with the calorimeter in a way that’s pretty well understood. Hadrons shower in the cal in kind of nasty, uncertain ways, sometimes leaking out a bit, sometimes showering early like electrons. Neutrons are a big source of uncertainty, because they leave no tracks, and only act weakly, showering late. Neutral pions are really annoying because sometimes they look like a neutron, and sometimes they convert into two photons. Jets are diverse, uncertain mixtures of all these things. The ZEUS calorimeter (which is probably on a ship on its way back to America for burial right now) is pretty fantastic, because it is what’s called compensating. This means the original designers fine tuned the absorber materials in it so that all the stable particles have a very similar response. Why people stopped designing detectors this way is a mystery to me.

One common way to improve jet measurements is to combine tracks and CAL deposits into single objects, usually called Energy Flow Objects. This helps, but since you don’t always know how much of a jet is neutral or charged there’s still a bit of uncertainty introduced. According to Thomson has been shown to be far less effective than PFC at ILC. The basic idea of PFC (seems to me to be) that you use a really spatially-fine-grained calorimeter, and when you have a track leading to the cal, you measure what you can with the tracker alone, and whatever else with the cal. This means adding the track energy(ies) and everything except the deposit(s) that belongs to the track(s). Moreover, you try to tag every particle in your calorimeter to undertand your shower profile uncertainities. Sounds easy, but the problem comes when deposits overlap, or the particle showers in a funky way, or two tracks point close to each other, or when you have electronics producing noise and giving fake signals. The people working on this algorithm seem to think they have all the possible scenarios accounted for, but I’m a bit skeptical. ILC people are designing a detector, not running one, so they’re running over simulations to get their answers, and real world collider data never looks like the simulations for the first few years. Then again, this is for an electron-positron collider everyone seems to think the background there will be negligible. Perhaps I’m just pessimistic from two years of painstakingly weeding calorimeter sparks, beam-gas and cosmic rays out of my data sample.

For your viewing pleasure, a simulated jet in the simulated TESLA detector that is one of several proposed designs for a detector that will built in 10 years ( depending on changes in GDPs and future elections in several nations). Different colors label different particles as identified by the PandoraPF algorithm. a partial view of the tracking chamber is on the left, and cal hits are the dots on the right. I stole this from Thomson’s paper here.

Simulated event in TESLA

So I fly back to the states roughly twice a year, and customs/immigration provide varying levels of irritation/amusement.  I’ve entered O’Hare international about four times, and the setup was a bit different each time I did it.  There’s always passport clearance, then baggage claim, then customs, then sliding doors, and the outside.  One time however, there was a standard blue-uniformed (not TSA) police officer between customs and the sliding door, asking each passenger random questions before they exited.  I overheard him asking the suit in front of me something about the length of her stay and something about a conference, and I step up.  I had been traveling for >12 hours at this point, and all his questions were rapid-fire, which I can’t sufficiently simulate syntactically.  The exchange went thusly:

[Cop]: Where are you flying in from?

[Me]: Germany.

[Cop]: What were you doing there?

[Me]: I work at a Lab.

[Cop]: What kind of lab?

[Me]: I study Physics.

[Cop]: High-Energy Particle?

[Me]: …. Uh.  Yes.

[Cop]: Do you make quarks?

[Me]: …..  Uh… not me personally… the machine group provides–

[Cop]: But you work with quarks?

[Me]: Yes.

[Cop]: Which is the worst quark?

[Me]: Worst?

[Cop, Sounding Annoyed and Busy]: Yes, which is the worst.  Down, strange, which is the worst?

[Me]: ….Uh, top…  I guess…

At this point he looks at me like he’s about to check-raise me, sort of smacks my passport against my chest and says

[Cop]: That’s what I thought.  NEXT!

I wander into the Chicago winter, and only really remember the exchange after a few hours.  He probably did that every time someone working at TeVatron went through, but I was way too tired to be prepared for that.

Some of you may recall a readers comment before about publishers manually re-typsetting journal submissions: I now believe this wholeheartedly. You see, the paper that is slowly becoming my thesis got accepted to a respectable journal a few weeks ago, and they sent back some proofs to be double checked before printing.

Some errors could have occurred in electronic reformatting, like all the funding references being dropped. Sure. Then there was ^{+3}_{−1}\% becoming ^{+3}_{−1\%}. Fine. Then there was several “<”s becoming “>”s. Hmmmm. Then there is my personal favorite: “Acknowlegments” [sic].

I’m currently spending my time earning a “Scientific Scuba Diver” certification.   For those not in the know, this is a professional qualification, proving that you can collect data and rescue scientists and cope with setting up equipment in cloudy water and navigate and so on.   It’s a 100 hour course and I’m doing it over two weeks, here in San Diego.   It’s fun - both classroom stuff and lots of tasks to complete on various dives.   It is possibly the last place that I expected to learn anything about physics.

Even recreational dive courses contain sections on the physics of diving.   It’s fairly important to understand at least the gas laws and the effect of pressure on solubility, since you breathe air at the pressure of the water around you, thereby ensuring that your lungs don’t collapse when you’re at depth.    I didn’t know that before I started to dive and it’s really pretty cool.    It causes all sorts of problems if you go down deep for long periods of time… but I digress.    The first scuba manuals were written in the fifties and sixties, back when imperial units were especially popular.   And so the dive manual states “There are four temperature scales:  Farenheit, Celsius, Kelvin and Rankine”.   Rankine?   As I rather arrogantly pointed out at some point (although I really didn’t intend it that way), I have 3 degrees in physics and I have never heard of the Rankine scale of temperature.      It turns out that it’s the absolute temperature scale for farenheit.   -459.67 degrees F is 0 R.   I suppose it’s a perfectly obvious analogy, but it had genuinely never occurred to me that it might exist.   Has anyone out there heard of it?

I think that the long-term aim should definitely to be to convert all the dive manuals (and diver thinking) to Centigrade, but I accept that this takes time and that an extra conversion stage when teaching ideal gas laws might confuse some people.   Most people in the US think in farenheit when it comes to practical experience and diving in 50 degrees F water is definitely an experience.  However, I have to give the course credit for having taught me something that I did not know from the history of my subject.

I have a new job.  I have advanced from the trivial consideration of a single bubble or even two bubbles at once (although that was only every other Thursday, when I’d been good) to a better place.   At my new level, which is physically in Rhode Island although mentally stranded out in an Atlantic storm,  cogitation and comprehension of millions of bubbles at once are required.     Cogitation is rather my thing, and I have been happily absorbing information on storms and bubbles and particulates and a mysterious thing called Langmuir circulation.   It’s amazing how quantitative you can make all this stuff, but it’s really only background for the main project, which is counting and sizing millions of bubbles at once by looking at their effect on the acoustic resonances between two circular plates.     You put radio hiss (a.k.a. white noise) into one plate, let it pass through the bubbles caused by the storm overhead and then watch with interest and popcorn while the other plate responds to all this fuss.  And then science is supposed to occur.

In learning the background for all of this, I have read a lot about turbulence.   The best thing to do with turbulence, according to the clever people who make a living out of this, is to measure eddies and make a Hilbert transform.    But what does Hilbert transform into?   And what is it of Eddy’s that is being measured anyway and does he know about it?   These are important questions for the tired mind.   Perhaps Hilbert is a small blue confused-looking animal with a skill for hiding in car glove compartments.   The day he transforms into something with sharp teeth and a taste for fingernails, you’d better watch out.    I don’t even want to think about Eddy and his potential measurements.

It is possible that although I’ve only been in Rhode Island for a week, I’ve already got cabin fever.  After a whole year I’ll probably be stalked by a small pack consisting of a Fourier (like a terrier but with more fur), two Laplaces (these are definitely blue, but only half of them exists) a few Hartleys (more sharp teeth, I’m afraid) and a small Identity (who has a monobrow and a permanently puzzled expression).    This sounds terrifying (or should that be Fourifying?).

If you happen to see my mind lurking about under the sofa, please post it back to me…

Sorry for the long time since last post. Today I have a shortish lunch talk to all types of physics graduate students. The talk was titled “Physics at Strong Coupling: Why String Theory and Supersymmetry are Ridiculously Cool, Part I,” with future parts to follow in the coming years. The talk went ok, I hope I convinced some graduate students that string theorists are not completely crazy.

But maybe now I can convince you! A friend helped me make the talk a podcast, with audio track and all. You can subscribe to it (and future, podcasts, I guess) here. Hitting “subscribe” on that page imports it into your i-tunes. Let me know if you enjoyed it! I guess, also let me know if you did not enjoy it…

According to my Librarian, The National Geographic Channel will be re-airing a show on man made marvels tomorrow and next Thursday on antarctic construction, with a partial highlight of the IceCube Experiment.  IceCube is a cubic kilometer of arctic ice laden with photomultipliers, and a surface array to detect air showers.   The PMT’s pick up Cherenkov light from fast moving charged particles (mostly muons), which come from cosmic rays and neutrino collisions with the water molecules.  Once completed, you can think of IceCube as being, among other things, a giant cubic telescope that views the universe with neutrinos instead of light.  It also contains an earlier detector called AMANDA, which is shown here as the yellow cylinder.  Photo comes from  gallery.icecube.wisc.edu.

I went to the weekly computing seminar yesterday, because it was on a statistical data mining tool that is being simultaneously used by physics experiments and marketing firms. The speaker is a physics professor, used to work for the PLUTO Collaboration, DELPHI, and now is variously associated with CDF and CMS. The company, Phi-T, is now totally private, and employs a couple dozen ex-physicists, or physicists, depending on how much of a purist you are. The software is proprietary and closed source, and the speaker was severely vague about what specific tools were actually used, but there is an interface in C++/ROOT/C#/Lisp already made, so its (supposedly) trivial to use, with a discounted academic licence.

So what is it? Basically, you have a vector of measureables, like detector channels, and some target, like say Resonance mass. Or Age, profession, #kids, and your target is “How much will this person cost us in Health Care in the next n years.” You then train the thing on your historic or simulated data, and it generates Bayesian posteriori distributions for new data. This is pretty common in neural computing literature, but this thing seems actually practical.

The only really fascinating thing is the generality of the thing, which was (supposedly) applied with minimal expert consultation on problems like car insurance premiums, to B_s mixing at CDF. Here’s a list of referred journal articles with their stamp. So whats inside? A neural network you say? No! The guy said in most applications they skip the neural net entirely and just use “Other” statistical methods. It’s clear that he was using some kind of input decorrelation like principle component analysis, but he wouldn’t say what specifically. He used a bunch of phrases that were cryptic to me like “zero layer network” to mean something other than a perceptron (I asked), and “zero iteration training” of a network. Maybe these things mean something to yall statters, but nothing to me. Anyways, the output of whatever was a discretized probability histogram that got splined together.

I’m unconvinced that the “default settings” he mentioned could schedule re-stocks for the largest book distributor in Germany AND find the X(3872) resonance, but what do I know? He also said that the companies own stock were controlled by this thing, but that selling it for this purpose is somehow illegal. Anyone know what he was talking about? Here’s a paper on it, by the speaker.

In the end, the talk was a sales-pitch/head-hunt, but if anyone out there needs to solve a highly nonlinear problem and has a cushy grant, go nuts.

If you speak German, and like science history, you can download Weyl’s 1919 Zeit, Raum, Materie book in PDF for free from The Internet Archive. I wanted a sort of journalistic, “this is how it went down” kind of book, but that doesn’t seem to be the case. There’s some nice first person stuff in the forward, and some nice philisophical musing at the end, but mostly is seems textbook stylie. I just skimmed it a bit, so maybe the gems are buried deeper.

If anyone can find anything on his early attempt at gauge invariant therory of unified electronmagnetism and gravity, I’d be grateful to hear from you.

You can also check out a collection of original papers on Relativity from before 1920, too. These are all translated into english.