For the time being the code is contained under the src folder and structured in Analysis and Simulation folders.
Currently performs standard 3-flavor neutrino oscillation analyses for the implemented experiments (IceCube Upgrade and Super-Kamiokande) and their combination. The code also includes some systematic uncertainties associated to the neutrino source and each experiment.
- numpy
- nuSQuIDS
- nuflux
- h5py
- matplotlib
- pandas
- scipy
Except for nuSQuIDS, you can install them by doing:
pip install -r requirements_analysis.txt
For nuSQuIDS, please follow the instructions at https://github.com/arguelles/nuSQuIDS/ .
An analysis is specified to the code via a xml file which contains all the information about the experiments, the neutrino sources and the systematic errors. An example of this can be found in src/Analysis/xmlAnalysis/AnalysisTemplate.xml.
In order to run some examples and get familiar with the code, the src/Analysis/run_example.sh contains a few quick examples. The main program for running the analysis is src/Analysis/runAnalysis.py and is run as follows:
usage: runAnalysis.py [-h] [-p [POINT]] [-o [OUTFILE]] [--multi] [--cluster] [xml_file]
positional arguments:
xml_file Input analysis file in xml format
optional arguments:
-h, --help show this help message and exit
-p [POINT], --point [POINT]
Specify analysis point to run. Only if 'cluster' option is enabled
-o [OUTFILE], --outfile [OUTFILE]
Analysis output file
--multi Option for running the analysis with multiprocessing (recommended locally)
--cluster Option for submitting jobs to a cluster
The results of the analysis are saved in <output_file> as a text file of columns (by deafult the output file is out.dat). If no running mode (--multi or --cluster) is specified it will run sequentially the list of points from the xml file.
Further, these output files can be plotted using src/Analysis/PlotGlobalSens.py. Some examples are shown in src/Analysis/plot_example.sh.
python3 PlotGlobalSens.py <experiment> <output_file> [output_file2]
In order to run src/Analysis/plotting/PlotGlobalSens.py, one should specify the experiment: SK, IC or IC+SK.
The option of providing a second analysis output file is only for the cases when one wants to add the output of two separate analyses but with the same parameter grid.
NOTE: This needs to be improved and merged with Ivan's plotting.
It contains the official MC simulation of IceCube Upgrade and preliminary effective/toy simulations for the ORCA and Super-Kamiokande experiments.
- nuSQuIDS
- nuSQUIDSTools
- nuflux
- particle
- matplotlib
- numpy
- pythia8
- h5py
- pandas
- scipy
Contains the relevant information extracted from various publications to closely match the reconstruction performance for SK. The code works applying the reconstruction on GENIE simulation files with format gst.
Super-Kamiokande atmospheric neutrino simulation
usage: makeSimulation_HDF5.py [-h] [-o OUTFILENAME] [-v] [--sk] [--H] [--Gd] [in_hdf5filename]
positional arguments:
in_hdf5filename Input Genie HDF5 file.
optional arguments:
-h, --help show this help message and exit
-o OUTFILENAME, --outfilename OUTFILENAME
Output file name in HDF5 format.
-v, --verbo Verbosity of simulation process.
--SK Default SK simulation without neutron tagging.
--H SKIV simulation with neutron tagging on hydrogen.
--Gd SKVII simulation with neutron tagging on gadolinium.
Public release of the MC simulation at https://icecube.wisc.edu/data-releases/2020/04/icecube-upgrade-neutrino-monte-carlo-simulation/ .
Tune IC-Up MC release to match the quoted resolution and efficiencies reported by the ORCA collaboration.
The folder utils gathers tools for converting ROOT files produced by GENIE to HDF5 files, and read and merge any HDF5 files.
Please, send any doubts or suggestions to pablo.fernandez@dipc.org, ivan.j.martinez-soler@durham.ac.uk, carguelles@g.harvard.edu, miaochenjin@g.harvard.edu, santiagoginer@college.harvard.edu .