The evolution of superluminous supernova LSQ14mo and its interacting host galaxy system
Bibliographic record
Abstract
We present and analyse an extensive dataset of the superluminous supernova (SLSN) LSQ14mo (z = 0.256), consisting of a multi-colour light curve from −30 d to +70 d in the rest-frame (relative to maximum light) and a series of six spectra from PESSTO covering −7 d to +50 d. This is among the densest spectroscopic coverage, and best-constrained rising light curve, for a fast-declining hydrogen-poor SLSN. The bolometric light curve can be reproduced with a millisecond magnetar model with ~ 4 M⊙ ejecta mass, and the temperature and velocity evolution is also suggestive of a magnetar as the power source. Spectral modelling indicates that the SN ejected ~ 6 M⊙ of CO-rich material with a kinetic energy of ~7 × 1051 erg, and suggests a partially thermalised additional source of luminosity between −2 d and +22 d. This may be due to interaction with a shell of material originating from pre-explosion mass loss. We further present a detailed analysis of the host galaxy system of LSQ14mo. PESSTO and GROND imaging show three spatially resolved bright regions, and we used the VLT and FORS2 to obtain a deep (five-hour exposure) spectra of the SN position and the three star-forming regions, which are at a similar redshift. The FORS2 spectrum at + 300 days shows no trace of SN emission lines and we place limits on the strength of [O <ns0:sc>i</ns0:sc>] from comparisons with other Ic supernovae. The deep spectra provides a unique chance to investigate spatial variations in the host star-formation activity and metallicity. The specific star-formation rate is similar in all three components,as is the presence of a young stellar population. However, the position of LSQ14mo exhibits a lower metallicity, with 12 + log (O/H) = 8.2 in both the R23 and N2 scales (corresponding to ~0.3 Z⊙ ). We propose that the three bright regions in the host system are interacting, which could induce gas flows triggering star formation in low-metallicity regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".