The Young Supernova Experiment Data Release 1 (YSE DR1) Light Curves
Bibliographic record
Abstract
This is the official Zenodo data release of the Young Supernova Experiment Public Data Release 1 (YSE DR1) light curves associated with the paper, "The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae". YSE DR1 is comprised of processed multi-color Pan-STARRS1 (PS1)-griz and Zwicky Transient Facility (ZTF)-gr photometry lightcurve files in the SNANA data format of 1975 transients with host galaxy associations, redshifts, spectroscopic/photometric classifications, and additional data products from November 24th, 2019 to December 20, 2021. See Aleo et al. (2022) for details. "yse_dr1_zenodo.tar.gz" -- All lightcurve data with no cut on signal to noise (S/N). "yse_dr1_zenodo_snr_geq_4.tar.gz" -- All lightcurve data with S/N >= 4. This can be used to recreate the analysis in Aleo et al. (2022). "parsnip_results_for_ysedr1_table_A1_full_for_online" -- The full version of Table~C2 in Aleo et al. (2022). The full ParSNIP (tertiary classification) results for YSE DR1. NOTE: An example tutorial on how to download the YSE DR1 data (full sample, spec sample, phot sample), grab metadata, and recreate a plot from the paper can be found on Github.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.160 | 0.168 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".