First systematic high-precision survey of bright supernovae
Bibliographic record
Abstract
Rapid variability before and near the maximum brightness of supernovae has the potential to provide a better understanding of nearly every aspect of supernovae, from the physics of the explosion up to their progenitors and the circumstellar environment. Thanks to modern time-domain optical surveys, which are discovering supernovae in the early stage of their evolution, we have the unique opportunity to capture their intraday behavior before maximum. We present high-cadence photometric monitoring (on the order of seconds-minutes) of the optical light curves of three Type Ia and two Type II SNe over several nights before and near maximum light, using the fast imagers available on the 2.3 m Aristarchos telescope at Helmos Observatory and the 1.2 m telescope at Kryoneri Observatory in Greece. We applied differential aperture photometry techniques using optimal apertures and we present reconstructed light curves after implementing a seeing correction and the Trend Filtering Algorithm (TFA, Kovács et al. 2005, MNRAS, 356, 557). TFA yielded the best results, achieving a typical precision between 0.01 and 0.04 mag. We did not detect significant bumps with amplitudes greater than 0.05 mag in any of the SNe targets in the VR-, R-, and I-bands light curves obtained. We measured the intraday slope for each light curve, which ranges between −0.37−0.36 mag day−1 in broadband VR, −0.19−0.31 mag day−1 in R band, and −0.13−0.10 mag day−1 in I band. We used SNe light curve fitting templates for SN 2018gv, SN 2018hgc and SN 2018hhn to photometrically classify the light curves and to calculate the time of maximum. We provide values for the maximum of SN 2018zd after applying a low-order polynomial fit and SN 2018hhn for the first time. We conclude that optimal aperture photometry in combination with TFA provides the highest-precision light curves for SNe that are relatively well separated from the centers of their host galaxies. This work aims to inspire the use of ground-based, high-cadence and high-precision photometry to study SNe with the purpose of revealing clues and properties of the explosion environment of both core-collapse and Type Ia supernovae, the explosion mechanisms, binary star interaction and progenitor channels. We suggest monitoring early supernovae light curves in hotter (bluer) bands with a cadence of hours as a promising way of investigating the post-explosion photometric behavior of the progenitor stars.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".