Top Efficiency (selections.performance.topefficiency)

Import with:

from AnalysisG.selections.performance.topefficiency import TopEfficiency

TopEfficiency

TopEfficiency is a SelectionTemplate subclass that evaluates GNN model performance for top-quark reconstruction, including efficiency, purity, invariant-mass distributions, and signal classification scores.

Output attributes:

Invariant-mass distributions (dict[str, dict[str, list[float]]]):

Attribute	Description
p_topmass / t_topmass	Predicted / truth top-quark invariant mass per decay region.
p_zmass / t_zmass	Predicted / truth Z′ invariant mass per decay region.

Probability scores:

Attribute	Description
prob_tops	dict[str, dict[str, list[float]]] — model score distribution for top-quark edges.
prob_zprime	dict[str, dict[str, list[float]]] — model score distribution for Z′ edges.
t_decay_region / p_decay_region	Truth / predicted decay-region flag distributions.

Node-count distributions:

Attribute	Description
t_nodes / p_nodes	dict[str, dict[float, int]] — truth / predicted node-count histograms.
n_tru_tops	dict[str, int] — total number of truth top quarks per region.
n_pred_tops	dict[str, dict[float, int]] — predicted top counts per score threshold.
n_perfect_tops	dict[str, dict[float, int]] — perfectly reconstructed top counts per score threshold.

Per-event score vectors (list[list[float]]):

Attribute	Description
truth_res_edge / truth_top_edge	list[int] — per-event truth resonance / top-edge labels.
truth_ntops / truth_signal	list[int] — per-event truth top multiplicity / signal flag.
pred_res_edge_score / pred_top_edge_score	Per-edge model score vectors for resonance / top classification.
pred_ntops_score / pred_signal_score	Per-event model score vectors for n-top / signal classification.