Introduction | Installation | Documentation | Terminology | License | Contributors | Others' work

PISA is a software written to analyze the results (or expected results) of an experiment based on Monte Carlo simulation.

In particular, PISA was written by and for the IceCube Collaboration for analyses employing the IceCube Neutrino Observatory, including the DeepCore and the planned Upgrade low-energy in-fill arrays.

PISA was originally developed to cope with low-statistics Monte Carlo (MC) by using parameterizations of the MC and operate on histograms of the data rather than directly reweighting the MC. However, PISA's methods apply equally well to high-MC situations, and PISA also performs traditional reweighted-MC analysis as well.

If you use PISA, please cite our publication (e-Print available here):

Computational Techniques for the Analysis of Small Signals
in High-Statistics Neutrino Oscillation Experiments
IceCube Collaboration - M.G. Aartsen et al.
Mar 14, 2018
Published in: Nucl.Instrum.Meth.A 977 (2020) 164332

git clone git@github.com:icecube/pisa.git
cd pisa
pip install -e .

For detailed installation instructions and common issues see Installation

Producing some oscillograms

from pisa.core import Pipeline
import matplotlib.pyplot as plt

<< PISA is running in single precision (FP32) mode; numba is running on CPU (single core) >>

Instantiate a Pipeline or multiple pipelines in a DistributionMaker using PISA config files

template_maker = Pipeline("settings/pipeline/osc_example.cfg")

Run the pipleine with nominal settings

template_maker.run()

Get the oscillation probabilities

outputs = template_maker.data.get_mapset('prob_mu')

Plot some results

fig, axes = plt.subplots(figsize=(18, 5), ncols=3)
outputs['nue_cc'].plot(ax=axes[0], cmap='RdYlBu_r', vmin=0, vmax=1);
outputs['numu_cc'].plot(ax=axes[1], cmap='RdYlBu_r', vmin=0, vmax=1);
outputs['nutau_cc'].plot(ax=axes[2], cmap='RdYlBu_r', vmin=0, vmax=1);