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And this is scary. At least with "high-energy experiments" (like the one that discover Higgs) in colliders, a lot depends on so-called triggers, which dismiss 9

And this is scary. At least with "high-energy experiments" (like the one that discover Higgs) in colliders, a lot depends on so-called triggers, which dismiss 99.9% of information produced in a collision "on the spot", so that this information is never recorded and analyzed. They have to: there is way too much information produced. So the triggers try to identify "trivial" events and dismiss them immediately, relaying only the ones that are may be somewhat unusual/unexpected. Essentially, the triggers are computers with highly specialized programs. Very smart people work on this, and supposedly they figure out problems with triggers before they affect the results of experiments...

dguest 3y ago
The triggers are the most fun part of the experiments! The composition of teams working on triggers might be a bit of an exception in the "engineer : "data scientist" ratio. Most of the talent is still from a physics background but there's more of an engineering bent where around half the team can probably write performance critical code when they need to. Elsewhere that ratio is much lower. Determining which data to save is a mix of engineering, algorithms, physics, bits of machine learning, and (for better or worse) a bit of politics. Surprisingly we're always desperate for more talent there. As you say, the goal is to try to stop problems before they affect the data, but it's not always perfect. Sometimes we discover sampling biases after the data comes in and need to correct for them, and in the worst case we sometimes blacklist blocks of data.