Higgs resonance resolution and background model

ID: higgs-resonance-resolution-and-background-model

The bridge from detector readout to a particle resonance is four-momentum conservation. In units , sum the measured final-state four-momenta:
For two photons separated by angle , gives the displayed diphoton formula. Two back-to-back photons of each have . A four-lepton candidate instead uses four four-momenta in the same sum. Collision debris outside the selected decay is not included. Since the partons carry unknown fractions of the incoming proton momenta, reconstructing the decay products is more informative than assigning the full beam energy to each candidate.
Figure 1.
Detector functions, reconstructed mass and a chosen resolution comparison
. Functional schematic followed by an original expectation model: sixty selected signal events at , a flat background of eight events per electronvolts, and chosen Gaussian mass widths and . No historical event counts or copied discovery histograms are shown. Details.
Suppose the intrinsic decay width is negligible compared with the stipulated detector spread. Let the reconstructed signal mass follow a normal distribution with mean and standard deviation . For a total expected selected signal and flat background density events per electronvolts, the expected count density is
This is a deliberately chosen response model, not a fit to either experiment. In the fixed window with , direct integration gives
The expected signal counts are for and for . Doubling the spread lowers the fraction in the same window without changing the total signal. Detector resolution can therefore change the visibility of a peak even with identical particle production.
As a separate counting example, stipulate an observed integer count and an exactly known background mean . The Poisson distribution gives a likelihood for nonnegative signal mean . Its maximum-likelihood estimator is , and the likelihood ratio statistic against is
This tests a specified window with a specified background, and is not either experiment's discovery significance. If the background mean is allowed to vary freely, background alone can choose and fit that count exactly. Control samples, a defensible mass-shape model, nuisance parameters and corrections for searching many masses are therefore essential to the experimental inference.

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