Our calendar year is pretty simple: we are always trying to produce new results in time for either the winter conferences or the summer conferences. With a bit of luck, one can have a day or two to relax in between these two seasons. And with new data pouring out of the LHC at an ever increasing pace right now, life is not going to get any easier.
As soon as we had wrapped it all up for the main winter conference, the Moriond conference held in late March, we started updating our analysis techniques in preparation for the summer conferences. Two major conferences are coming up: the first one, the European Physics Society conference will start on July 21st in Grenoble, France, followed by the Lepton-Photon conference in Mumbai, India in late August. Traditionally, all experiments hope to show their results at one of these huge conferences every year.
One would think that once you have the analysis defined, it should just be a matter of passing all the accumulated data through those selection criteria to extract the type of events we want to study. It’d be too easy…
Producing a new result requires an incredible number of checks. When new data comes in, especially now at the LHC when the accelerator team is constantly trying to increase the number of collisions produced, the working conditions change often. These new conditions need to be incorporated into the simulations we use to make our predictions.
Our analysis technique is fairly simple: we use a theoretical model to predict new phenomena and particles, and with complex simulation methods, we reproduce what our detector response would be to such events. We do the same for all known processes, that is, we can predict the various types of collisions that will come out of the LHC. This is what we call “Monte Carlo” simulated events: they look just like the events we collect in our detectors, except they are fabricated based on all our knowledge of what can be produced when protons collide in the LHC, and how our detectors will respond to these events. Since all the laws of physics follow statistical laws, we generate millions of these events in the same proportions as what we expect.
The next step is to determine a series of selection criteria designed will the sole purpose of spotting the needle from a barn full of haystacks. For this, we study in details the characteristics of the events we are interested in, comparing these characteristics with those of other types of known processes. At this stage, the name of the game is to isolate the signal from all other types of events, those we refer to as background.
Most of the time, the background constitutes the bulk of all collected events. This is normal since the events we know best are the ones that are produced copiously and we have already had a chance to study them in depth in previous experiments.
The final step consists of comparing the sum of all simulations of known processes that would survive our selection criteria to the data we collect. If we are testing a particular model, we compare the sifted data to these specific events to see if we select more events than what was expected from all backgrounds, and check if these events bear any resemblance with the theoretical model under test. Other times, and this is what happened in the recent observation made by the CDF collaboration, they were looking for specific events but observed more than what was expected from known sources. Something unexpected showed up, in addition to everything that was already very well known. In all cases, we need to know with the utmost precision how much background is expected, before we can claim a discovery for whatever shows up in excess.
And here is where all our time and effort goes: cross-checking that all is well done at each step. We constantly look at our simulated data events and compare them with real events collected in our detector. Since we are also trying to improve both our reconstruction algorithms and our simulations, every time something is modified, we need to crosscheck it against real data.
Using large samples of data, we compare hundreds of different quantities, selected in a variety of ways, making sure our simulations perfectly reproduce every aspect you can think of, be it the average number of particles per event, their distribution around the detector, their momentum, the energy they carry, anything we can think of. The more data we collect, the more precise these comparisons get, making it increasingly more stringent.
This is why we have no rest in between seasons. We are constantly revisiting our work between conferences, improving the simulation, making corrections, devising more precise checks. The list never ends.
When all this is done, we share our findings with our colleagues, who make every possible attempt to find the slightest flaw in our work. Given that the ATLAS and CMS collaborations each have more than 3000 physicists, while ALICE and LHCb count about 1000 researchers each, you can be sure we have to be convincing. Colleagues have to be merciless if competitors are to be persuaded!
In the end, the goal is to produce absolutely trustworthy results, excluding flaws, bugs and oversights. Most likely, the best new results will be announced at the upcoming summer conferences. Discoveries or signs of new signals will of course be announced as soon as all possible and impossible checks will have been performed.
Should we expect big announcements this summer? It is hard to tell but we can all hope. We are tracking elusive particles that have escaped detection so far. If we don’t catch them properly in time to announce it at these conferences, we will at the very least show in details where we have searched and map out all territory covered so far, where these particles can no longer hide. It’s a bit like trying to find a rare species of a tiny animal in a vast territory covered not only by fields, but forests and mountains. We need to inspect each square centimeter of a territory spanning several square kilometers and many hiding places. Daunting it is, but not impossible. With determination, patience, extreme rigor and huge computing facilities like the Grid, we are determined to do it. At the very least, if we do not find it per se, we might be able to corner it, setting limits that theoreticians will be able to take into account to draw a better picture of the world we live in. And as with any discovery, who knows what might come out of it?
Pauline Gagnon
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