CTA and KM3NeT Common Source Search

This repository contains the prospects for combined analyses of hadronic emission from γ-ray sources in the Milky Way with CTA and KM3NeT/ARCA. It complements the publication "Prospects for combined analyses of hadronic emission from γ-ray sources in the Milky Way with CTA and KM3NeT" (arxiv:2309.03007), and for in-depth description of the analysis please refer to the paper. The aim of this analysis is to simulate how well a combined analysis of CTA and KM3NeT data can differentiate between hadronic and leptonic emission scenarios of galactic gamma-ray sources. The focus is on the comparison of the combined analysis to the separate analysis of the two instruments within Gammapy. This content is only compatible with gammapy v0.17, later versions are not supported.
It should be noted, that gammapy v0.17 is not compatible with the M1 CPU. The only option to run this analysis with this CPU is to use a docker image. This option will be provided in the next version of the repository.

Content

• Analysis/: Notebooks to reproduce the analysis
• data/: Instrument Response Functions (IRFs) and flux model for the sources
• envs/: Configuration files for setting up the python environment
• src/: supplementary scripts

Installation

Download

First it is required to download the whole content of the repository, it can be done using git:

git clone [email protected]:KM3NeT/Analysis-galactic-sources-CTA-KM3NeT.git

or

git clone https://github.com/KM3NeT/Analysis-galactic-sources-CTA-KM3NeT.git

then

cd cta-and-km3net/

Creating the environment

Using conda

In order to use conda to build the environment, conda has to be installed. To see how, use these Installation instructions.

Build environment using conda from environment.yml file:

conda env create -f envs/environment.yml
conda activate km3net_cta_env

Using venv

It requires to build a dedicated environment. Build environment using pip, first it requires to install manually python3.8, then install virtualenv:

pip install virtualenv
# for standard preinstalled python 3.8
virtualenv venv --python=python3.8
# or specify path
virtualenv venv --python=/path/to/python3.8

acitvate venv:

# on Windows
.\venv\Scripts\activate.ps1
# on Linux
source venv/bin/activate

Install necessary packages:

pip install cython numpy
pip install -r requirements.txt

Using Jupyter

In order to run the notebooks, you need to have Jupyter installed. You can install it using pip install jupyter or following the instructions at the Juypter website.

Running the Jupyter kernel

Jupyter notebook kernel and launch your notebook:

python -m ipykernel install --user --name=km3net_cta
jupyter-notebook

And for zsh shell, you need to execute these lines first before installation of the kernel

conda install -c conda-forge notebook
conda install -c conda-forge nb_conda_kernels

Integration with REANA

Analysis can be run in REANA, for this purpose it needs to install reana-client inside virtual environment:

# inside venv or conda env
pip install reana-client

$\textcolor{red}{\text{Warning!}}$ reana-client is currently not compatible with Windows even inside a conda environment.

After installation of the client, it needs to set connection using a token. For convinience all REANA commands are specified in run_reana.sh script. Launch the script in terminal.

export REANA_SERVER_URL=https://reana.cern.ch
export REANA_ACCESS_TOKEN=*YOUR_TOKEN*
. run_reana.sh
# get the results of analysis
reana-client download