optimize_cuts

pyirf.cut_optimization.optimize_cuts(signal, background, reco_energy_bins, multiplicity_cuts, gh_cut_efficiencies, theta_cut_efficiencies, fov_offset_min=<Quantity 0. deg>, fov_offset_max=<Quantity 1. deg>, alpha=1.0, theta_min_value=<Quantity 0.02 deg>, theta_max_value=<Quantity 0.3 deg>, progress=True, **kwargs)

Optimize the gamma/hadronnes, theta and multiplicity cut in every energy bin of reconstructed energy for best sensitivity.

Parameters:

signal: astropy.table.QTable

event list of simulated signal events. Required columns are theta, reco_energy, ‘weight’, gh_score No directional (theta) or gamma/hadron cut should already be applied.

background: astropy.table.QTable

event list of simulated background events. Required columns are reco_source_fov_offset, reco_energy, ‘weight’, gh_score. No directional (theta) or gamma/hadron cut should already be applied.

reco_energy_bins: astropy.units.Quantity[energy]

Bins in reconstructed energy to use for sensitivity computation

multiplicity_cuts: np.ndarray[int, ndim=1]

Values to scan for minimum telescope multiplicity

gh_cut_efficiencies: np.ndarray[float, ndim=1]

The gamma/hadron separation cut efficiencies to scan for best sensitivity.

theta_cut_efficiencies: astropy.table.QTable

The theta cut efficiencies to scan for best sensitivity.

fov_offset_min: astropy.units.Quantity[angle]

Minimum distance from the fov center for background events to be taken into account

fov_offset_max: astropy.units.Quantity[angle]

Maximum distance from the fov center for background events to be taken into account

alpha: float

Size ratio of off region / on region. Will be used to scale the background rate.

theta_min_valueu.Quantity[angle]

minimum theta cut value

theta_max_valueu.Quantity[angle]

maximum theta cut value

progress: bool

If True, show a progress bar during cut optimization

**kwargs are passed to ``calculate_sensitivity``