4 ms·

How many iterations that could create this result do they run? 1 in 1000 sounds unlikely, but for all I know they have run this experiment 50,000 times.

• DavidSJ 6y ago
  I would guess there are many more than thousands of such experiments that have been run, testing all the various permutations of dozens (or more) of different beyond Standard Model physical theories.
  • dukwon 6y ago
    Testing different models is done in the analysis of the data, not in running the experiment differently. The "experiment" (colliding protons together) has been repeated quadrillions of times at the LHC. But there are only a few parameters you can change about it, namely the energy and how focused the beams are. Altering the detectors to change/improve how data is collected is an expensive and slow process.
    • DavidSJ 6y ago
      Fair enough; what I meant is "multiple testing" in the context of a single overarching experiment, which is I believe what the comment I was replying to was getting at as well.
      • davrosthedalek 6y ago
        In any case, you have a point. Whether multiple analysis, multiple channels or multiple experiments, you have a look-elsewhere effect, and that can easily knock off one or two sigmas. If you look at a 10000 possibilities, you will a couple with 3 sigma difference, just by chance. LHC looks at MANY possibilities. For a while, they tried to keep track to quantify the look-elsewhere effect. I think they have given up on that.
• dalewe 6y ago
  Not sure I entirely understand your question but I'll try to explain a bit anyway: they're using a dataset of 9fb^-1. That means it includes 9 events for a crosssection of 1fb (femtobarn). Now the paper doesn't seem to include what crosssection their events have but if we assume something of the order of magnitude of 1pb then the dataset would contain around 9000 events of interest. Now all that doesn't really matter as this is already factored into the standard deviation of their result. They have enough data to say it's a 3 sigma (being the standard deviation) deviation from the standard model. Now this "1 in 1000" really just means that the null hypothesis (the Standard Model) says there's a 0.1% chance for that result to happen. Of course it could also be that they forgot to factor in some uncertainty of their measurements and their sigma is actually much bigger. But it surely won't be the size of their dataset as this is quite obvious and easy to take into account.