This repository contains the analysis to test the homogeneity of Type Ia Supernovae (SNe Ia) in the Near-infrared (NIR) for accurate distance estimations
In this work, we use the Carnegie Supernova Project (CSP I+II) sample of SNe Ia with uBgVriYJH-band light curves. To fit these objects we use SNooPy (Burns et al. 2011)
To run the notebooks it is recommended to create an environment first (e.g., using Anaconda) and activate it:
conda create -n flows_sims pip
conda activate flows_sims
and then install the rest of the requirements (except for SNooPy -- see below):
pip install -r requirements.txt
To install the latest version of SNooPy (v2.6 at the momment), follow these commands:
git clone https://github.com/obscode/snpy
cd snpy
git checkout gen3
python setup.py install
If you make use of these scripts or the analysis, please cite the following paper: https://ui.adsabs.harvard.edu/abs/2022arXiv220704780M/abstract
@ARTICLE{2022arXiv220704780M,
author = {{M{\"u}ller-Bravo}, T.~E. and {Galbany}, L. and {Karamehmetoglu}, E. and {Stritzinger}, M. and {Burns}, C. and {Phan}, K. and {I{\'a}{\~n}ez Ferres}, A. and {Anderson}, J.~P. and {Ashall}, C. and {Baron}, E. and {Hoeflich}, P. and {Hsiao}, E.~Y. and {de Jaeger}, T. and {Kumar}, S. and {Lu}, J. and {Phillips}, M.~M. and {Shahbandeh}, M. and {Suntzeff}, N. and {Uddin}, S.~A.},
title = "{Testing the Homogeneity of Type Ia Supernovae in the Near-Infrared for Accurate Distance Estimations}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics},
year = 2022,
month = jul,
eid = {arXiv:2207.04780},
pages = {arXiv:2207.04780},
archivePrefix = {arXiv},
eprint = {2207.04780},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220704780M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}