How to Calculate Effective Areas

SimWeights objects have a method simweights.Weighter.effective_area() for calculating effective areas. Some example usage is shown below.

The first example will calculate effective area in 4 different zenith bins and 25 energy bins and plot each zenith bin separately.

# load hdf5 file
f = tables.open_file("Level2_IC86.2016_NuMu.021217.N100.hdf5", "r")
# create weighter object
w = simweights.NuGenWeighter(f, nfiles=10)
# create the energy and zenith bins
energy_bins = np.geomspace(1e2, 1e8, 25)
cos_zenith_bins = [-1, -0.5, 0, 0.5, 1]

# calculate the effective area in energy and zenith bins with all of the events in the sample
effective_area = w.effective_area(energy_bins, cos_zenith_bins)

# make some labels for the different zenith bins
zenith_labels = [
    "120° < Zenith < 180°",
    "\u200790° < Zenith < 120°",
    "\u200760° < Zenith < \u200790°",
    "\u2007\u20070° < Zenith < \u200760°",
]

# for each zenith bin plot effective area as a function of energy
for i, zenith_slice in enumerate(effective_area):
    plt.step(energy_bins, np.r_[0, zenith_slice], label=zenith_labels[i])

This example demonstrates the use of the mask parameter. Effective areas are calculated separately for both NuMu and NuMuBar primaries. Only a single zenith bin is used. The third call to effective_area() has both NuMu and NuMuBar and so the output is averaged over primary type.

# use a mask to select just the NuMus
numu_mask = w.get_weight_column("pdgid") == simweights.PDGCode.NuMu
ea_numu = w.effective_area(energy_bins, [-1, 1], numu_mask)
plt.step(energy_bins, np.r_[0, ea_numu[0]], label=r"$\nu_{\mu}$")

# Now use a mask to select just the NuMuBars
numubar_mask = w.get_weight_column("pdgid") == simweights.PDGCode.NuMuBar
ea_numubar = w.effective_area(energy_bins, [-1, 1], numubar_mask)
plt.step(energy_bins, np.r_[0, ea_numubar[0]], label=r"$\bar{\nu}_{\mu}$")

# not using any mask will calculate the average
ea_avg = w.effective_area(energy_bins, [-1, 1])
plt.step(energy_bins, np.r_[0, ea_avg[0]], label="Average")

The final example shows a different use of the mask parameter. Three different filters are used as masks creating three different curves. Again only a single zenith bin is used.

# list of the filters we want to show
filters = ["MuonFilter_13", "GRECOOnlineFilter_19", "HESEFilter_15"]

# loop over the filters
for filter_name in filters:
    # create a mask
    mask = getattr(f.root.FilterMask.cols, filter_name)[:][:, 1].astype(bool)
    if np.any(mask):
        EA = w.effective_area(energy_bins, [-1, 1], mask)
        plt.step(energy_bins, np.r_[0, EA[0]], label=filter_name)