Inclusive top pair production at the particle level
Contents
Inclusive top pair production at the particle level#
We present here plots and animations related to Sections 4.3 and 5.2 of [Gomez Ambrosio et al., 2022].
We consider inclusive top quark pair production at the particle level i.e. including top quark decays in the dilepton channel: \(p p \rightarrow t \bar{t}, t \bar{t} \rightarrow b \bar{b} \ell^{+} \ell^{-} \nu_{\ell} \bar{\nu}_{\ell}\), at the LHC 14 TeV.
Models#
Trained models of top-quark pair production in the dileptonic decay channel are presented in Table 1. The models are trained on either a single feature, \(p_{T}^{\ell \ell}\), a pair of features \((p_{T}^{\ell \ell}, \eta_{\ell})\) or the full set of 18 kinematic features. More details about the kinematic features used in the neural network training can be found in the next section.
Features |
Models |
|---|---|
\(p_T^{\ell\bar{\ell}}\) |
|
\(p_T^{\ell\bar{\ell}}, \eta_\ell\) |
|
all (18) |
Results#
Plots and animations are available at the following links:
- Kinematic features used as inputs to the neural network
- Results from the ML model vs binning in two features, \(O(\Lambda^{-2})\)
- Results from the ML model trained on two features vs all features, \(O(\Lambda^{-2})\)
- Results from the ML model vs binning in two features, \(O(\Lambda^{-4})\)
- Results from the ML model training on two features vs all features, \(O(Λ^{-4})\)
- Impact of methodological uncertainties