An Unexpected Outcome from Disentangling
Bibliographic record
Abstract
During our recent spectroscopic study of ε Aur (Chadima et al. 2011), we made an attempt to detect weak spectral lines of the secondary, hidden in a dark disk, using the spectral disentangling technique of Simon & Sturm (1994) and Hadrava (1995, 1997, 2004). We used the Dominion Astrophysical Observatory (DAO) and Ondřejov (OND) red electronic spectra, which cover more than one half of the orbital period. To our surprise, two different programs that disentangled the spectrum in Fourier space, KOREL (Hadrava 1995, 2004) and FDBINARY (Ilijić et al. 2001), both yielded apparently good, similar reconstructions of two well-defined spectra for mass ratios near unity. The results (Solution 1) are shown in Fig. 1 (left panels) and Table 1 (left column). This result is hard to accept as real given the existing knowledge about the system: ε Aur is an F-type star with an unseen companion embedded in a cool, dark disk (temperature ~ 500–600 K). A detailed search for any trace of spectral signatures of the secondary in the spectra was carried out (see Bennett et al., these proceedings). Although they found line profile variations that were correlated with orbital phase, these variations were not consistent with the presence of a secondary.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".