Quantum Diaries

Every week I try to take a few hours to study something different. The idea being that this will give me a broader sense of what’s going on within the ATLAS collaboration and the world of particle physics at large. Last week I was mostly watching Gavin Salam’s superb lectures on jets. They’re available as videos from here.

So what is a jet? It’s certainly nothing to do with aeroplanes. Jets are what we observe at ATLAS when a highly energetic quark or a gluon (collectively referred to here as partons) is produced in a collision.

I won’t take the time to explain the physics behind a jet and how they come into being. Those interested can see Flip’s excellent post. In essence, instead of the individual partons, what we see in the detector is a spray of collimated particles. This is what we refer to as a “jet”.

At hadron colliders, such as the LHC, jets are everywhere. In fact the vast majority of interactions at a hadron collider will result in the creation of multiple jets. They are our window on partons and on to the strong force itself.

Being so ubiquitous it’s important that we’re able to reliably identify these within our detector. Unfortunately this isn’t always such an easy task. The event display below illustrates a typical jet event. How many jets do you see?

Here is ATLAS’s answer.

In this case the jets have helpfully been colour coded. In real life, this doesn’t happen.

As you can tell the definition of a jet can be somewhat ambiguous. At ATLAS the trigger system has to quickly identify thousands of jets a second in order to pick out the interesting events to record. Identifying such a large number of jets is no easy feat.

To solve this problem we use jet algorithms. These are pieces of software which define jets based on what we see in the detector. They come in all sorts of shapes and sizes, from “simple” versions where a jet is defined as all the particles inside a cone, to more advanced versions which sequentially combine together individual particles based on their separation and energy.

Different algorithms have different strengths and weaknesses. Cone based jets are relatively simple and provide nice, round jets. Unfortunately though, the jets they identify can easily be altered by changes within the jet itself, or by small amounts of energy coming from unrelated collisions. This makes them very hard to compare to the predictions from theory. More complicated algorithms such as the “kT” algorithm remove these ambiguities, but often result in “ugly” irregularly shaped jets.

The current vogue algorithm both at CMS and ATLAS is the so called “anti-kT” algorithm. This starts from the most energetic single particles and sequentially combines them with everything nearby, stopping at some pre-defined distance. This algorithm results in the identification of nice, round jets, and does this consistently regardless of the small amounts of additional energy or the structure of the jets themselves.

A canister, containing AMS being loaded onto the space shuttle Endeavour. Photo from NASA.

I had a plan for this first post, but unfortunately, like so much in life, it didn’t work out quite as I hoped. The plan went like this: On April 19th the space shuttle Endeavour was set to launch from the Kennedy Space Centre in Florida. Having recently relocated to Louisiana from the UK, I found myself within “easy” travelling distance of a launch for the first time ever. So like any good, space-obsessed 26-year-old, I immediately set plans in action to journey across the US and set up camp on the beach to watch this once-in–a-lifetime event.

Endeavour was set to transport the Alpha Magnetic Spectrometer 2 (AMS-02) to the International Space Station. AMS is a nifty little particle physics detector (little in the context of LHC-style detectors) designed to study the content of cosmic rays for evidence of dark matter and anti-helium. This provided the perfect tie–in between a trip to see the big rockets and my day-to-day work on ATLAS.

Even more so as I spent my PhD years helping design search strategies for neutralinos. Which are the hypothesised partner particles to the Standard Model gauge bosons, the force-carrying particles, proposed by a particularly popular extension to the Standard Model, Supersymmetry. And they represent a prime dark matter candidate.

Unfortunately though, this plan came crashing down when NASA postponed the launch date of the shuttle by 10 days, putting it right in the middle of a conference I’m scheduled to attend and hence scuppering both my travel and blog plans.

This set me thinking about delays and how delays to big experiments have a defining role in so many careers. The biggest and most public delay of recent years was the postponing of the LHC physics programme following the quenching incident of ’08. I was present at CERN at the time and remember well the sense of disappointment that surrounded this. Of course, it was quickly replaced with a cheery optimism as everyone settled in for another year with our treasured simulations and the always-relished chance to polish our respective code bases. I was lucky enough to be studying at a UK university at the time, so I was able to continue my studies — and complete the vast bulk of my thesis — using only these simulations and very little real data.

A lot of my US contemporaries were not so lucky. They needed data in order to be able to graduate. This led to a lost generation of students who had to either switch experiments or ride out the new LHC schedule and wait for the data. A lot of these students are still around today, all putting the finishing touches to their theses, and generally being massively over qualified for the post-doctoral positions they are only now applying for.

All of which goes to show that planning your life around science doesn’t always turn out the way you expected.