A long-lasting eruption heralds SN 2023ldh, a clone of SN 2009ip
Bibliographic record
Abstract
We discuss the results of the spectroscopic and photometric monitoring of the type IIn supernova (SN) 2023ldh. Survey archive data show that the SN progenitor experienced erratic variability in the years before exploding. Beginning May 2023, the source showed a general slow luminosity rise that lasted for over four months, with some superposed luminosity fluctuations. In analogy to SN 2009ip , we call this brightening ‘Event A’. During Event A, SN 2023ldh reached a maximum absolute magnitude of Mr = −15.52 ± 0.24 mag. The light curves then decreased by about 1 mag in all filters for about two weeks reaching a relative minimum, which was followed by a steep brightening (Event B) to an absolute peak magnitude of Mr = −18.53 ± 0.23 mag, replicating the evolution of SN 2009ip and similar to that of type IIn SNe. The three spectra of SN 2023ldh obtained during Event A show multi-component P Cygni profiles of H I and Fe II lines. During the rise to the Event B peak, the spectrum shows a blue continuum dominated by Balmer lines in emission with Lorentzian profiles, with a full width at half maximum velocity of about 650 km s−1. Later, in the post-peak phase, the spectrum reddens, and broader wings appear in the Hα line profile. Metal lines with P Cygni profiles and velocities of about 2000 km s−1 are clearly visible. Beginning around three months past maximum and until very late phases, the Ca II lines become among the most prominent features, while Hα is dominated by an intermediate-width component with a boxy profile. Although SN 2023ldh mimics the evolution of other SN 2009ip -like transients, it is slightly more luminous and has a slower photometric evolution. The surprisingly homogeneous observational properties of SN 2009ip -like events may indicate similar explosion scenarios and similar progenitor parameters.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".