3D optical spectroscopic study of NGC 3344 with SITELLE: I. Identification and confirmation of supernova remnants
Bibliographic record
Abstract
ABSTRACT We present the first optical identification and confirmation of a sample of supernova remnants (SNRs) in the nearby galaxy NGC 3344. Using high spectral and spatial resolution data, obtained with the CFHT imaging Fourier transform spectrograph SITELLE, we identified about 2200 emission line regions, many of which are H ii regions, diffuse ionized gas regions, and also SNRs. Considering the stellar population and diffuse ionized gas background, which are quite important in NGC 3344, we have selected 129 SNR candidates based on four criteria for regions where the emission lines flux ratio [S ii]/H α ≥ 0.4. Emission lines of [O ii] λ3727, H β, [O iii] λλ4959,5007, H α, [N ii] λλ6548,6583, and [S ii] λλ6716,6731 have been measured to study the ionized gas properties of the SNR candidates. We adopted a self-consistent spectroscopic analysis, based on Sabbadin plots and Baldwin, Phillips & Terlevich diagrams, to confirm the shock-heated nature of the ionization mechanism in the candidates sample. With this analysis, we end up with 42 Confirmed SNRs, 45 Probable SNRs, and 42 Less likely SNRs. Using shock models, the confirmed SNRs seem to have a metallicity ranging between Large Magellanic Cloud and 2×solar. We looked for correlations between the size of the confirmed SNRs and their emission lines ratios, their galaxy environment, and their galactocentric distance: We see a trend for a metallicity gradient among the SNR population, along with some evolutionary effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".