electronids

Electron IDs are a set of techniques used in particle detectors to classify reconstructed objects as electrons or as other particles. They are essential for analyses involving electromagnetic processes and for suppressing backgrounds in hadronic final states. In collider detectors, electron identification combines information from the inner tracking system, the electromagnetic calorimeter, and, when available, transition-radiation detectors and hadronic calorimetry.

Discriminating variables include the electromagnetic shower shape in the calorimeter (such as lateral and longitudinal energy deposition profiles), the matching between the energy cluster in the calorimeter and the extrapolated track in the inner detector, and the ionization energy loss (dE/dx) in tracking chambers. Additional handles come from conversions (reconstructed electron-positron pairs from photon conversions) and from isolation, which measures activity around the electron candidate. Some analyses also use timing information and, in some detectors, measurements of transition radiation.

Electron identification is implemented as both cut-based and multivariate algorithms. Cut-based IDs define loose, medium, and tight working points with fixed thresholds on a set of variables. Multivariate IDs use machine learning methods, such as boosted decision trees or neural networks, to combine variables and optimize efficiency versus background rejection. IDs are typically parameterized by the candidate's transverse momentum (pT) and pseudorapidity (η) because detector response varies with angle and energy.

Performance is described by electron efficiency (the probability to pass the ID for true electrons) and the fake rate (the probability to pass for background). Validation uses control samples such as Z→ee or J/ψ→ee decays, and data-driven background estimates are used to calibrate simulations. Electron IDs are central to precision measurements of W and Z bosons, Higgs decays to electrons, and many beyond-Standard-Model searches.