Particle physicists turn to AI to cope with CERN’s collision deluge

You're absolutely right about how the amount of data needed for statistical significance grows with the size of the parameter space you're searching.

That depends on the analysis. If you're looking at a partially-reconstructed decay (common in semileptonics) then you can't rely on the regular trick of choosing a sideband sample to work as your combinatorial background. Also, it's very common to model specific misidentified or partially-reconstructed backgrounds using simulation.

> In any case, the more complex your model gets (number of variables) the exponentially more simulated Monte Carlo events you need to fill that multidimensional space.

I understand this argument if you're trying to model the efficiency in nD space (through splines, histograms, moments etc), but that's usually done when you're fitting to n variables, e.g. in an amplitude fit. If you just want the efficiency of a cut on the score from an MVA algorithm, I don't think it matters. What definitely matters is that the behaviour on simulation reproduces that of real signal as faithfully as possible.

It immediately looked like a really interesting challenge to me, but after reading a bit about the state of the art it seems like three months is a pretty short time to come up with a meaningful result even if you could work on it fulltime. Many people already invested a lot of time in that problem and existing solutions are quite sophisticated and good. The material actually mentions that they expect that you will have to take into account things like adjacent detectors overlapping by a few pixels or how particles may light up several pixels if they hit the detector at very shallow angles and cross several pixels as they pass through the detector.

The first thing someone probably considers is something like a Hough transformation and it turns out the creators of the challenge mention that in the material and submitted a solution based on this as a bench mark which achieves a score of about 20 %. If I read the related documents correctly, a meaningful result will require a score of at least about 90 % and the state of the art would probably be somewhere around 95 % to 98 %. The current leader is at 26.48 %, admittedly the challenge is only 5 days old. I am really curious where the scores will be at the end.

I'm not sure I like this approach, however. AI is not pixie dust you can sprinkle over your hard problems and even if you are 100% sure your AI-based system matches perfectly your current systems, you simply can't guarantee it will match the cases you never tested (the never-seen-before data from never-done-before experiments). You'll still need to do the well understood process once you flagged the interesting data (and possibly threw away the sets mislabeled as uninteresting).

A factor of 10, which is what's mentioned in the article, is what you expect to get with Moore's law in three or four years. With current off-the-shelf advances, I'd expect more than a 10-fold improvement in the next three years at the leading edge HPC world. Maybe CERN's problem is one that specialised compute units could solve better.

That seems also to be - but I did not look at the code - more or less what the creators of the challenge implemented and submitted as a benchmark implementation, admittedly with the expected poor performance score of only about 20 %.

When I last worked there in 2015 a typical pile up situation was having about 50 collisions per detector reading. It is no simple problem to simultaneously reconstruct 50 collisions from the same set of overlapping detector measurements.