Data for "Superphot+: Realtime Fitting and Classification of Supernova Light Curves"

de Soto, Kaylee;Villar, Ashley;Berger, Edo;Gomez, Sebastian;Hosseinzadeh, Griffin;Branton, Doug;Campos, Sandro;DeLucchi, Melissa;Kubica, Jeremy;Lynn, Olivia;Malanchev, Konstantin;Malz, Alex I.

Description

This is the dataset and static code base associated with the paper: "Superphot+: Real-Time Fitting and Classification of Supernova Light Curves". The contents are as follows:superphot-plus-v0.0.7.tar: Superphot+ code base downloaded at time of paper submission. Static copy of the Github repo: https://github.com/VTDA-Group/superphot-plus -- This version corresponds to commit: 956b5d555f58800c01a74b3977e0a3b5476ea9cd and tag v0.0.8.dataset_spec_pruned.csv: Spectroscopic dataset pruned according to Table 1 of the paper.dataset_phot_final.csv: Photometric dataset (without spectroscopic labels) pruned according to Section 2 of the paper. Label and probability columns are values from the ALeRCE-SN classifier.model_0.pt: One of the 10 (redshift-independent) LightGBM models trained for 5-way SN classification.model_0.yaml: Configuration file associated with model_0.pt.model_z_0.pt: Same as model_0.pt, but trained using redshift information.model_z_0.yaml: Configuration file associated with model_z_0.pt.early_phase_classifier_0.pt: Same as model_0.pt, but trained only using early-phase light curve features. Tailored for realtime classification.early_phase_classifier_0.yaml: Configuration file for early_phase_classifier_0.pt.probs_concat.csv: Spectroscopic set's classification results without using redshift information.probs_z_concat.csv: Spectroscopic set's classification results using redshift information.probs_photometric_v2.mrt: Superphot+'s probabilities for the photometric set without using redshift information. Updated to correct for missing IAU names.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.8

FAIR Score

79%

Citations

1

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

MIT License

Assigned Domain

Subfield

Artificial Intelligence

Field

Computer Science

Domain

Physical Sciences

Confidence Score

39%

Source

Scholar Data Model

Keywords

supernovaeclassificationnested samplinggradient-boosted machines

Normalization Factors

FT

51.92

CTw

1.00

MTw

1.00