Classification of Peculiar Supernovae

With the discovery of the accelerating expansion of the universe, type Ia supernovae (SNe Ia) have been used increasingly to determine cosmological parameters. The use of SNe Ia as cosmological probes relies on the fact that SN Ia luminosities at the time of maximum are not only bright but also have low intrinsic scatter. However, SNe are in-fact a highly heterogeneous collection of objects spanning a diverse collection of subtypes.

One approach to this challenge is to identify SNe subtypes based on their light-curve properties. In Gonzalez-Gaitan et al. 2014 (G14) a photometric identification technique was introduced for discriminating SN 1991bg-like objects in photometric samples. Using several low-redshift samples from the literature, it was demonstrated that this method is not only capable of identifying dim, fast-declining SNe, but can also identify other peculiar transients such as SNe Iax-like, SN 2006bt-like, and super-Chandrasekhar SNe Ia.

In Perrefort et al. 2020 we apply a modified version of the same classification technique to the SDSS Supernova Survey. This documentation is provided as a technical reference for the published work, and serves as a complete project writeup for all programmatic aspects of the project. The Project Notes section documents the project from a scientific perspective. It it provided to ensure reproducibility of the results and to clarify various design decisions. The API Reference documents how to use the project’s code base along with various technical clarifications.

Source Code and Repository Structure

Source code for this project can be found online via GitHub. We provide summaries below for key files in the project repository:

project parent
├── config_files/
├── docs/
├── notebooks/
├── phot_class/
├── results/
├── tests/
│
├── README.md
├── environment.yml
├── run_pipeline.py
├── run_pipeline.sh
├── run_salt2.py
└── setup.py

File	Description
config_files/	Files specifying fitting arguments and priors for different models/surveys.
docs/	The project documentation’s source code (what you’re reading right now).
notebooks/	Notebooks that inspect results of the classification pipeline. These contain mostly plotting code.
phot_class/	The python package containing all of the logic for fitting and classifying light curves.
results/	Classification results returned from the phot_class package. This directory can be re-generated by running run_pipeline.sh
tests/	Tests for the phot_class package.
README.md	Github Landing document pointing readers to the online documentation.
environment.yml	Installation requirements for phot_class.
run_pipeline.py	A command line interface for running the phot_class package.
run_pipeline.sh	Runs the command line interface for various combinations of arguments
run_salt2.py	Runs salt2 fits for each SDSS target.
setup.py	Installation script for the phot_class package.