NotebooksΒΆ
Although the phot_class package houses the core logic of our analysis, the
visualization and interaction with results from the package is performed using
Jupyter Notebooks. Notebooks also contain commentary on the analysis process,
making them good a good place to build familiarity with the analysis pipeline.
Descriptions of each notebook and what they discuss are provided below.
Online interactive versions are provided for each notebook via
BinderHub. To launch a BinderHub server click here.
Note
Please note that the BinderHub server might take a while to initialize and may require patience. Refreshing the page may help somewhat.
| Notebook | Description |
|---|---|
| classification.ipynb | Applies photometric classifications to all supernovae. |
| classifying_single_target.ipynb | Demonstrates the classification technique of Gonzalez-Gaitan+ 14 on a single supernova. |
| creating_config_files.ipynb | Creates config files for CSP, DES, and SDSS. |
| Data Cuts | Inspects SDSS targets and applys quality cuts on observed light-curves |
| fit_inspection.ipynb | Inspects fit results for individual light curves. |
| fitting_method_comparison.ipynb | Comparison of classification results when using band-by-band vs. collective fitting. |
| fom_optimization.ipynb | Determines optimal classification boundaries by optimizing the FOM parameter. |
| iminuit_vs_emcee.ipynb | A simple comparison of the fit_lc and mcmc_lc minimization routines. |
| inspecting_91bg_model.ipynb | Demonstrates the properties of the 91bg model we use for classification. |
| salt2_fit_results.ipynb | Minimal investigation of fit results from the Salt2 model. |
| sdss_redshift_distribution.ipynb | Plots of redshift distributions for the SNe data set. |
| sncosmo_chisq_bug.ipynb | Outlines a bug in the calculation of chi-squared in SNCosmo and demonstrates that our results do not suffer from this bug. |
| snid_classifications.ipynb | Exploration of spectroscopic classifications for SDSS supernovae. |