[dguest]

As others have commented we've been using tools like neural networks and boosted decisions trees for a long time. We have quite good simulation which tells us what particles would look like in our detector, but one thing our simulation tells us is that it's often really hard to tell the difference between a Higgs Boson and some other "background" process.

So the logic goes like this: if we trust our simulation, we can simulate the Higgs, and simulate the background, and then train a neural network to tell us which is which. Then we turn the network loose on our data. If it sees lots of things that look like Higgs, yay, we discovered something!

For machine learning tools, we had a few homegrown implementations that didn't get far beyond physics (probably because they weren't particularly user-friendly). But physicists would have referred to techniques like this as "Multivariate Analysis" (or "MVA") a few years ago.

More recently we've started to reach out more to industry and use their tools, which actually much nicer! What she's referring to here is one particular analysis her team contributed to [1], which relied on XGboost [2]. Beyond that we've used Keras a fair bit to identify some types of particles.

[1]: https://arxiv.org/abs/1806.00425 [2]: https://xgboost.readthedocs.io/en/latest/