In the Search of Molecular Carriers of Diffuse Interstellar Bands (DIBs): From Statistics of Observation to Ionized Molecules
Bibliographic record
Abstract
Chérif F. Matta Dept. of Chemistry & Physics, Mount Saint Vincent University, Halifax, NS, Canada B3M 2J6. Corresponding author: cherif.matta@msvu.ca Abstract: The enigma of Diffuse Interstellar Bands (DIBs) — absorption features in stellar spectra due to dust and molecular clouds between stars and the observer on Earth — has puzzled astronomers for over a century. Recent work has shed light on this mystery by exploring potential molecular carriers for DIBs and mapping their relationship to interstellar environments. We have discovered four new families of strongly correlated DIBs through a statistical analysis of the Apache Point Observatory (APO) DIB catalog. Ionized molecular candidates associated with the first observed DIBs at 5780 and 5797 Å, using both observational and computational chemistry techniques (e.g., TD-DFT), are proposed. Key candidates, such as glycolamide, lactamide, and oxamic acid for the 5780 Å DIB family, along with 2-cyclopenten-1-one and 3(2H)-selenophenone for the 5797 Å family, emerged through correlations in spectral line strengths and functional group analysis. Notably, recent observational data from the molecular interstellar cloud (code: G+0.693-0027) detected glycolamide, which re-enforces our theoretical predictions and supports the role of this molecule as a potential DIB carrier. The spectral signatures of ions, as opposed to neutral species, match observed DIB features in the visible range, underscoring the importance of ionization in identifying DIB carriers. For instance, anions of 3(2H)-selenophenone display bands near 579.4 nm, closely corresponding to the prominent 5797 Å DIB. It is further suggested that molecular candidates connected to DIBs often possess amide and hydroxyl groups, indicating potential structural motifs of interstellar molecules. The work underscores the need for comprehensive databases of ionized molecular spectra, which can be used to refine DIB associations and guide future laboratory and observational studies. References Majaess, D.; Seuret, H.; Sullivan, A.; Harriott, T. A.; Morera-Boado, C.; Massa, L.; Matta, C. F. (2024) “Strengthening the Link Between Fullerenes and a Subset of Diffuse Interstellar Bands”; Publications of the Astronomical Society of the Pacific, submitted, in review. Majaess, D.; Seuret, H.; Sullivan, A.; Harriott, T. A.; Morera-Boado, C.; Massa, L.; Matta, C. F. (2024) “Characterizing Functional Groups Within DIB Energy Offsets”; Publications of the Astronomical Society of the Pacific, submitted, in review. Seuret, H.; Sullivan, A.; Morera-Boado, C.; Harriott, T. A.; Majaess, D.; Massa, L.; Matta, C. F. (2024) “Vetting Molecular Candidates Linked to the First Diffuse Interstellar Bands Discovered (5780 and 5797 Å)”; Physical Chemistry Chemical Physics (PCCP), submitted, in review. Xing, H.; Sullivan, A.; Seuret, H.; Morera-Boado, C.; Harriott, T. A.; Majaess, D.; Massa, L.; Matta, C. F. (2024) “Extinction Along Sightlines Sampled by the APO Catalog of DIBs”; Research Notes of the American Astronomical Society (RNAAS) 8, 90. Smith, E. R.; Smith, F.; Harriott, T. A.; Majaess, D.; Massa, L.; Matta, C. F. (2022) “Novel correlations between diffuse interstellar bands and optical reddening”; Research Notes of the American Astronomical Society (RNAAS) 6 (No. 4), 82. Smith, F.; Majaess, D.; Harriott, T. A.; Massa, L.; Matta, C. F. (2021) “Establishing new diffuse interstellar band correlations to identify common carriers”; Monthly Notices of the Royal Astronomical Society (MNRAS) 507, 5236–5245.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".