Higgs BosonDetector
The Higgs boson turns up in about one collision in a billion, then falls apart right away. This model learns to spot those rare events. Scroll down to see how it works, and try it yourself.
scroll to explore_The collisions
250,000 collisions from the ATLAS detector, each already labelled signal (a Higgs) or background (everything else). This is what the model learns from.
Pick a cutoff
The model scores every event from 0 to 1, where higher means more Higgs-like. You set the cutoff: anything above it gets flagged as signal. Drag it and watch the trade-off. Flag too little and you miss real Higgs events; flag too much and background slips through. The AMS score (short for Approximate Median Significance, the competition's metric) rewards the best balance.
Two models
I trained two kinds of model on the same examples and scored them the same way. The gradient-boosted trees (XGBoost) edged out the neural network (a multilayer perceptron, or MLP) here. That's common for this kind of table-shaped problem.
What matters most
These are the measurements the model leaned on most. The estimated mass of the two tau particles stands out by far, which is exactly what you'd expect from the physics.
DER_mass_transverse_met_lepDER_mass_MMCPRI_tau_ptDER_mass_visDER_deltaeta_jet_jetDER_pt_ratio_lep_tauDER_mass_jet_jetDER_met_phi_centralityDER_pt_hDER_deltar_tau_lepDER_prodeta_jet_jetPRI_lep_etaDER_mass_MMC is the model's estimate of the combined mass of the two tau particles, by far the strongest sign that a collision was a Higgs.