tianluyuan / nuveto

Atmospheric neutrino passing fraction

nuVeto

This package calculates the effect of a detector veto on the high-energy atmospheric neutrino flux via detection of muons that reach the detector. The result calculated is the passing-flux or passing-fraction of atmospheric neutrinos as a function of energy and zenith angle.

Getting started

Installing

This now relies on the updated version of MCEq. For the legacy version that relies on MCEq_classic do git checkout v1.5 and see the README.

pip install nuVeto

This will install MCEq.

Extras are pip install nuVeto[plotting, resources] which will install some packages for plotting and generating muon reaching probabilities.

Usage

The simplest way to run is

from nuVeto.nuveto import passing
from nuVeto.utils import Units
import crflux.models as pm

enu = 1e5*Units.GeV
cos_theta = 0.5
pf = passing(enu, cos_theta, kind='conv nu_mu',
             pmodel=(pm.HillasGaisser2012, 'H3a'),
             hadr='SIBYLL2.3c', depth=1950*Units.m,
             density=('CORSIKA', ('SouthPole','December')))

where kind can be (conv|pr|_parent_) nu_(e|mu)(bar)

See examples/plots.py for more detailed examples.

Building muon detection probabilities

To calculate the passing fraction requires knowing the muon detection pdf as a function of the overburden and energy of the muon at the surface. This is constructed from a convolution of the muon reaching probability and the detector response. The muon reaching probability is constructed from MMC simulations and is provided for propagation in ice in resources/mu/mmc/ice.pklz. The detector response probability must be defined in resources/mu/pl.py as a function of the muon energy (at detector). Then, construct the overall muon detection pdf and place it into the correct location.

cd nuVeto/resources/mu
./mu.py -o ../../prpl/mymudet.pkl --plight pl_step_1000 mmc/ice_allm97.pklz

To use the newly generated file, pass it as a string to the prpl argument.

passing(enu, cos_theta, prpl='mymudet')`.

Contributers

Carlos Arguelles, Sergio Palomares-Ruiz, Austin Schneider, Logan Wille, Tianlu Yuan